An Early Predictor of Neonatal Sepsis As

HAEMATOLOGIAL SCORING SYSTEM: AN EARLY PREDICTOR OF

NEONATAL SEPSIS AS COMPARED TO BLOOD CULTURE

Dissertation submitted in

Partial fulfilment of the regulations required for the award of

M.D. DEGREE

PATHOLOGY – BRANCH III

THE TAMILNADU DR. M.G.R. MEDICAL UNIVERSITY

CHENNAI

APRIL 2017

DECLARATION

I hereby declare that the dissertation entitled “HAEMATOLOGICAL

SCORING SYSTEM: AN EARLY PREDICTOR OF NEONATAL SEPSIS

AS COMPARED TO BLOOD CULTURE” is a bonafide research work done by me in the Department of Pathology, Coimbatore Medical College during the period from MAY 2015 TO APRIL 2016 under the guidance and supervision of

Dr.A. Dhanalakshmi, M.D, Associate Professor, Department of Pathology,

Coimbatore Medical College.

This dissertation is submitted to The Tamilnadu Dr.MGR Medical

University, Chennai towards the partial fulfillment of the requirement for the award of M.D., Degree ( Branch III ) in Pathology. I have not submitted this dissertation on any previous occasion to any University for the award of any

Degree.

Place: Coimbatore

Date: Dr. M.Poornima

CERTIFICATE

This is to certify that dissertation entitled " HAEMATOLOGICAL

SCORING SYSTEM: AN EARLY PREDICTOR OF NEONATAL SEPSIS

AS COMPARED TO BLOOD CULTURE" is a bonafide work done by

Dr.M.POORNIMA, a postgraduate student in the Department of Pathology,

Coimbatore Medical College, Coimbatore under guidance and supervision of

DR.A. DHANALAKSHMI M.D, Associate Professor , Department of

Pathology, Coimbatore Medical College, Coimbatore in partial fulfillment of the regulations of the Tamilnadu Dr. M. G. R. Medical University, Chennai towards the award of M. D. Degree (Branch III) in Pathology.

Guide Head of the Department

Dr.A. Dhanalakshmi M.D, Dr.C.Lalitha,M.D, Associate professor, Professor, Department of Pathology, Department of Pathology, Coimbatore Medical College Coimbatore Medical College, Coimbatore. Coimbatore.

Dr.A.EDWIN JOE, M.D; B.L, Dean Coimbatore Medical College, Coimbatore.

ACKNOWLEDGEMENT

To begin with, I thank the almighty God for bestowing his blessings on me in successful completion of this dissertation.

I wish to thank our beloved Dean Dr.A.EDWIN JOE,M.D,B.L, Coimbatore

Medical College and Hospital, Coimbatore for permitting me to conduct this study.

I thank Dr. C.Lalitha. M.D., Professor and Head of the Department, Department of Pathology, Coimbatore Medical College, Coimbatore forher guidance and support.

I express my gratitude and sincere thanks to my guide Dr.A. Dhanalakshmi,

M.D, Associate Professor, Department of Pathology, Coimbatore Medical

College, Coimbatore. This dissertation bears her valuable suggestions and highly professional advice.

I wish to express my gratitude and sincere thanks to Professor Dr. A. Arjunan.

M. D., for his guidance and support.

I owe my gratitude to Dr. Geethanjali M.D., Professor of Pediatrics and

Dr.V.K.Sathyan, Neonatologist, Department of Pediatrics, Coimbatore Medical

College Hospital, for their encouragement and suggestions throughout the course of my work. I thank all Associate professors, Assistant Professors and Tutors of the

Department of Pathology, Coimbatore Medical College, Coimbatore for their opinion and encouragement.

I thank my family, my parents, my husband Dr Sakthivel M.S.Mch.and my son,

S.Iniyan who stood behind me in all my efforts.

I thank all lab technicians working in Department of Pathology, Coimbatore

Medical College, Coimbatore.

DR.M.POORNIMA

CONTENTS

SI.NO. PARTICULARS PAGE NO.

1. INTRODUCTION 1

2. AIMS AND OBJECTIVES 3

3. REVIEW OF LITERATURE 4

4. MATERIALS AND METHODS 56

5. STATISTICAL ANALYSIS 61

6. RESULTS 62

7. DISCUSSION 80

8. CONCLUSION 82

9. BIBLIOGRAPHY

10. ANNEXURES

I. PROFORMA

II. LIST OF ABBREVIATIONS

III. MASTER CHART

IV. CONSENT FORM

LIST OF TABLES

SI.NO TITLE PAGE.NO.

1. HAEMATOLOGICAL SCORING SYSTEM 60

2. CLINICAL GROUP DISTRIBUTION 62

3. AGE AND SEX DISTRIBUTION 63

4. AGE DISTRIBUTION 64

5. DISTRIBUTION OF CLINICAL AND 67 HAEMATOLOGICAL SEPSIS 6. ASSOCIATION OF CLINICAL VARIABLES 68 WITH HAEMATOLOGICAL SCORING SYSTEM

7. TOTAL WBC COUNT AS PREDICTOR OF 69 SEPSIS 8. PERFORMANCE OF INDIVIDUAL 77 HAEMATOLOGICAL PARAMETERS

LIST OF CHARTS

SI.NO. TITLE PAGE.NO.

1. GENDER DISTRIBUTION 65 2. DISTRIBUTION OF NEONATES ACCORDING 66 TO HAEMATOLOGICAL SCORE 3. TOTAL WBC COUNT AS PREDICTOR OF 70 SEPSIS 4. TOTAL PMN COUNT AS PREDICTOR OF 71 SEPSIS 5. IMMATURE PMN COUNT AS PREDICTOR OF 72 SEPSIS 6. I:T PMN RATIO AS PREDICTOR OF 73 NEONATAL SEPSIS 7. I:M PMN RATIO AS PREDICTOR OF 74 NEONATAL SEPSIS 8. DEGENERATIVE CHANGES AS PREDICTOR 75 OF NEONATAL SEPSIS 9. PLATELET COUNT AS PREDICTOR OF 76 NEONATAL SEPSIS 10. PREVALENCE OF CRP POSITIVITY IN THE 78 STUDY POPULATION 11. DISTRIBUTION OF CRP POSITIVITY 79 ACCORDING TO THE HAEMATOLOGICAL SCORING SYSTEM

LIST OF COLOR PLATES

COLOUR PLATE. NO. TITLE

1. LEISHMAN STAIN- NEUTROPHILIC LEUCOCYTOSIS

LEISHMAN STAIN- MATURE AND IMMATURE 2. FORMS

3. LEISHMAN STAIN- IMMATURE FORMS

LEISHMAN STAIN- NEUTROPHIL SHOWING 4. CYTOPLASMIC VACUOLATIONS

5. LEISHMAN STAIN- TOXIC GRANULES

INTRODUCTION

Neonatal sepsis is the commonest and the most important cause for the morbidity and mortality of neonates in developing countries like India (1, 2). The signs and symptoms of sepsis in neonates are subtle and non-specific which makes it difficult to diagnose clinically. Timely diagnosis of sepsis in neonates is critical as the illness can progress more rapidly when compared to adults (3).

Mortality rate in developing countries is between 10-70/ 1000 live births.

Fortunately neonatal sepsis is a treatable condition if it is diagnosed early and treated with appropriate antibiotics. But early diagnosis of sepsis is still a great challenge. Inability to diagnose the sepsis earlier results in unnecessary and prolonged exposure to antibiotics. This increases the risk of antibiotic side effects and also the emergence of drug resistant microorganisms. Neonatal sepsis can be early or late and most of the cases of early sepsis are within 24 hours (4).

As the immune system of neonates is weak, they are more susceptible to invasive infections. Premature infants are even more prone to infections than term neonates (5). The tests which diagnose sepsis with high positive predictive value like blood culture, haptoglobin and immunoelectrophoresis are expensive and time consuming (6). Though definite diagnosis is made by positive blood culture the test is time consuming. Measures of cytokines, acute phase proteins, cell surface antigens and bacterial genomes are used for the early diagnosis of

1 sepsis either alone or in combination. Though these markers are sensitive and specific they are expensive and are not readily available in resource poor settings. Hence the ideal diagnostic tests should give quick results and should have good sensitivity, specificity so that unnecessary antibiotic is avoided (7-11).

In order to diagnose the sepsis earlier certain haematological parameters are evaluated and each of them assessed to find the most suitable parameters.

AIMS & OBJECTIVES

AIMS AND OBJECTIVES

 To assess the importance of haematological parameters in the early

diagnosis of neonatal sepsis

 To compare the variables with other laboratory parameters.

 To assess the most sensitive and specific variables in diagnosing neonatal

sepsis.

REVIEW OF LITERATURE

NEONATAL SEPSIS:

It is a clinical syndrome which is considered when the neonate (< 28 days of life) has infective signs and symptoms along with presence or absence of bacteria in the blood.

It includes many systemic infections affecting the neonate like meningitis, Pneumonia, Osteomyelitis and infections of the urinary tract.

Neonatal sepsis does not include conjunctivitis and oral thrush.

CLASSIFICATION OF NEONATAL SEPSIS

Neonatal sepsis is classified as early onset and late onset sepsis (12)

EARLY ONSET SEPSIS:

In early onset sepsis neonate has symptoms and signs in the first 3 days of life. In severe sepsis, neonate may present even at birth. The main source of infection is maternal genital tract. Neonates usually have pneumonia which is followed by respiratory distres

There are certain maternal and perinatal risk factors to be considered for the early diagnosis of neonatal sepsis. (12, 13)

Sepsis screen criteria from AIIMS protocol which is followed in NICU of Coimbatore Medical College is as follows:

1. Prematurity/ low birth weight < 2.5 kg

2. Prolonged labour ( 1st and 2nd stage of labour together comprise >24hrs)

3. Amniotic fluid which is either foul smelling or meconium stained

4. Membrane rupture for >24 hrs

5. Single unsterile vaginal examination or if sterile, more than three

examinations during labour

6. Perinatal asphyxia – with APGAR score <4 at 1 min

7. If mother had fever within 14 days before delivery

INDICATIONS FOR STARTING ANTIBIOTICS

Presence of any one of the factors in neonates at risk of early onset sepsis

(EOS):

 Presence of ≥3 risk factors for EOS

 Presence of foul smelling liquor

 Presence of ≥2 antenatal risk factors and a positive septic screen

 Strong clinical suspicion of sepsis.

LATE ONSET SEPSIS (14, 15):

Neonates presents with symptoms and signs after 3 days of life. Here neonates either have pneumonia, meningitis or full blown sepsis. Neonates acquire the infection during the stay in the hospital (nosocomial or hospital acquired) or from community (community acquired)

RISK FACTORS FOR HOSPITAL ACQUIRED INFECTION:

1. Birth weight < 2.5 kg

2. Prematurity

3. Assisted ventilation

4. Parenteral fluid administration

5. Usage of stock solutions

6. Any invasive techniques applied during labour

RISK FACTORS OF COMMUNITY ACQUIRED LATE ONSET SEPSIS:

1. Lack of umbilical cord care

2. Lack of hygiene

3. Bottle fed infants

4. Prelacteal feeds

Breast feeding improves resistance to infections.

CLINICAL PRESENTATION:

In early stage of sepsis, neonates usually have non specific signs and symptoms, even though we should have high suspicion for the early diagnosis of sepsis.

NON SPECIFIC SIGNS AND SYMPTOMS

 Hypothermia or hyperthermia ( hypothermia usually seen in low birth

weight babies)

 Feeble cry

 Poor activity

 Refusal to suck

 Increased or decreased heart rate

 Low or high blood glucose

 Low perfusion

 Metabolic acidosis

SPECIFIC SIGNS AND SYMPTOMS:

 CNS: Irritable baby, bulging anterior fontanelle, seizures, neck stiffness.

If these features are present we should suspect meningitis.

 CVS: Low blood pressure, poor perfusion, shock

 GIT- vomiting, loose stools,distended abdomen, intolerance to

feeds,paralytic ileus, necrotising entero colitis

 HEPATIC- enlarged liver, conjugated hyperbilirubinemia (usually seen

in infections of the urinary tract)

 HAEMATOLOGICAL- Bleeding manifestations, purpuric spots,

petechial spots

 RENAL- acute renal failure

Indications for starting antibiotics in late onset sepsis:

 Positive septic screen

 Strong clinical suspicion of sepsis

INVESTIGATIONS (16)

 Peripheral smear

 Blood culture

 Lumbar puncture

 Serological markers

 Urine culture

 Radiology

PERIPHERAL SMEAR

MANUAL METHOD

Fresh blood with or without anticoagulant is needed for preparing blood films.

Blood films are prepared in a clean glass slide wiped free of dust using cotton.

Slides size should be 7.5x 2.5 cm. Thickness should be 1 mm. One end is

8 frosted to enable labelling. Spreader slide is prepared first by breaking the one corner of the slide with the help of a glass cutter so that its width is 1.8cm.A spreader slide can be used several times after proper washing.

A drop of blood is placed at 1cm from one corner of a slide in the midline and a spreader slide is placed at a 30 degree angle in front of the drop .The slide is moved backwards so that it touches the drop. The drop spreads quickly along the contact line. Then the blood is spread along the slide for a length of 3 cm.

This forms the monolayer where the cells are widely spaced so that cell counts can be made. In the slide this monolayer is formed in the feathered edge created by the spreader. Blood film made is allowed to air dry. The thickness of the blood film can be altered by varying the spreader angle or by changing the spreading speed. For anaemic blood, wider angle is used to obtain the correct thickness. For polycythemic blood, narrower angle is used. For ideal thickness there should be some overlap of red blood cells throught the smear’s length.

White blood cells should be present throughout the blood film.

AUTOMATED METHODS

In order to stain several batches of stain we can use automated staining machines. It can be a separate machine or combined along with automated blood counter. Spreading of the slide, fixation, staining are all done by the instrument. Either single blood film or multiple blood film per sample can be obtained. Staining of the slides can be of two methods. One is flat-bed staining

9 where staining solutions are applied to the horizontally placed slide. Other is

“dip and dunk” method slides are immersed in a staining solution. We can even spray the stains on to the slides in a centrifuge.

LABELLING BLOOD FILMS

The blood film should be labelled with the pencil on the frosted end.

STAINING BLOOD FILMS

Romanowsky stains are employed universally for staining blood films.

Romanowsky dyes consists of two components 1. Azure B (trimethylthionin) 2.

Eosin Y ( tetrabromofluorescein). (17, 18) The original Romanowsky dyes constitutes 1. Polychromemethylene blue 2. Eosin There are several factors for variation in staining pattern. One among them is the presence of contaminants in the commercially available dyes. As it gives consistent results simple dyes are preferable to complex dyes (17,19, 20). Pure dyes of azure B and eosin Y are expensive. Hence, the stain containing 80% of the dye is sufficient (21). In

Romanowsky group, simplest dye is the Jenner and complex dye is the Geimsa.

Routinely employed stain is the Leishman stain. pH recommended is 6.8. In order to obtain uniform pH, 1ml of water is mixed with 50 ml of Sorensen’s phosphate buffer.

Some component of cell stain with particular dye while the other component do not. It depends on the difference in interaction between the dye

10 and the molecular structure (22). Azure B has affinity to anionic molecules. Eosin

Y has affinity towards cationic molecules. Cell nucleus having nucleic acid and cytoplasmic proteins have acid group. So they stain with azure B , the basic dye.

Cytoplasmic granules of neutrophils stain weakly by azure B. As haemoglobin molecule contains basic group, it stains with acidic dye, eosin Y.

Granules in the eosinophils contain alkaline group- spermine derivative that stain with acidic dye. Granules of basophils contain heparin which is an acid that stains with basic dye hence it appears blue.(18) RNA binds slowly and DNA binds more rapidly. Haemoglobin binds very slowly. Therefore, correct proportion of azure B and eosin should be used. Proper timing for the staining of the slides should be ensured.

LEISHMAN’S STAIN :( 23)

0.2 g of leishman powder is weighed and is mixed with 100ml of methanol in the conical flask. After warming for 15 min allow it to cool.

Filtered solution can be used immediately but the staining quality can be improved on standing.

LEISHMAN STAIN- PROCEDURE (24)

After making the blood film it is allowed to air dry. The slides are flooded with Leishman stain for 2 minutes. Twice the volume of buffered water is added and kept for 5 minutes. Slides are washed in buffer water for 2

11 minutes so that it acquire a pinkish tinge. Back of the slide is washed and is air dried.

OTHER STAINING METHODS

MAY-GRUNWALD-GIEMSA STAIN –PROCEDURE (23)

After drying the slides should be fixed in methanol for 10 minutes. May-

Grunwald stain is taken in a jar and is diluted with buffer water of equal volume. Fixed films are kept in this jar for 15 minutes. Then without washing it is transferred to another jar containing Giemsa stain which is diluted with 9 volumes of buffer water. Ph should be 6.8. Wash the slide in buffer water. Three or 4 changes given. It should be left undisturbed for 5 minutes so that differentiation can take place. The colour of the blood film is taken as a good guide or it should be viewed under low power. We should not allow the slide to dry at any time. Once the differentiation step is over the slides are allowed to dry.

Stain related problems

 Inadequate staining of neutrophil granules.

 Degranulation of basophils.

 Increased background staining.

 Blue staining of red blood cells.

AUTOMATED BLOOD COUNT TECHNIQUES

It can be semiautomated or fully automated. Semi-automated requires dilution of blood sample that is done by the operator. It also measures only a few components like Hb and WBC. In contrast fully automated instruments need appropriate blood sample to be presented to the instrument. This multichannel instruments measure 10 to 20 components for full blood count and differential count. It also measures some variables that is not counted using manual techniques. They have higher precision in cell counting and cell sizing techniques which is superior to the manual techniques. Accurate test results are obtained by carefully calibrating the instruments and ensuring correct operation by quality control practices. Blood having abnormal characteristics produce aberrant parameters which are flagged by the instrument. So inaccurate counts produced due to abnormal characteristics of blood vary between instruments. It is necessary for operators to be aware of the factitious results to which the instruments are prone. (25)

Blood cell counters have automated facilities for the recognition of the sample, for adequate mixing of the sample, to take the blood sample automatically and for detecting clots and inadequately sized samples. Fully automated instruments are needed to perform blood counts in large number of samples and to give precise and accurate results rapidly. Most instruments count

13 for a specified time period rather than measuring exact blood volume. Hence it requires calibration in counting cells in a defined volume of diluted blood. (26)

RED BLOOD CELL COUNT

Red blood cells and other blood cells are counted using light scattering or aperture impedance technology. As large number of cells is counted rapidly there will be high level of precision. RBC counts and red cell indices like MCV and MCH rendered by electronic counts have greater relevance clinically than obtained manually which was slow and imprecise. (25, 27)

IMPEDANCE COUNTING

Impedance counting is based on the principle that red blood cells are bad conductors of electricity. Some diluents act as good conductors. This forms the basis in several counting instruments such as Sysmex, Beckman Coulter, Horiba

Medical, and Abbott.

To perform cell count, first blood is diluted with buffered electrolyte.

Either mercury siphon controls the flow rate of the diluted blood sample or by displacing the tightly fitted piston. Aperture measures 10 cm in diameter and 7 cm in length. It results in the passage of measured volume of blood sample through the aperture tube. Direct current is flowed between the two electrodes one placed in the beaker containing sample or the chamber which surrounds the aperture tube and the other placed inside the aperture tube. When a blood cell

14 flows through the aperture it displaces some conducting fluid thereby increasing electrical resistance. This produces potential difference between the two electrodes. It lasts as long as the red blood cell passes through the aperture. The height of the pulse reflects the volume of the cells flowing through the aperture.

The pulses are recorded on a oscillograph screen. These pulses are passed on to a threshold circuit which has an amplitude descriminator. It selects the minimum pulse height that will be counted. (27)

LIGHT SCATTERING

Electro-optical detectors can be used to count red blood cells and other blood cells. (27) Diluted blood sample passes in a single file through the aperture in front of light source. Cells scatter the light while passing through them.

Photodiode or photomultiplier detects this scattered light by converting into electrical impulses that are counted. The amount of scattered light is proportional to surface area and hence the cell volume. Hence the cell volume is estimated by the height of the electrical impulse.

Current instruments use high intensity coherent laser having superior optical qualities than the earlier non coherent tungsten lights. Sheathed flow permits cells to pass in a axial stream with the diameter that is not much more than a red blood cell. This allows precise focussing of light on the cells. Electro- optical detectors are employed in Siemens systems for red cell counting and sizing and also used for white blood cell differential counting. (28)

HAEMATOCRIT AND MEAN CELL VOLUME

The flow of cells through the aperture or through the light generates electrical impulse. The height is proportional to the cell volume. RBC count is determined by the number of pulses generated. MCV or haematocrit is determined by the analysis of pulse height. Average pulse height gives the

MCV. Hct is calculated by multiplying MCV and the RBC. Summation of pulse heights gives Hct and MCv can be obtained by dividing haematocrit by RBC.

Before determining Hct or MCV automated instruments should be calibrated.

Calibration of haematocrit is by manual determination of Hct. Calibration of

MCV is by generating pulse heights by stabilized cells or latex beads. But unfixed flexible biconcave human red blood cells will not show same charecteristics as latex particles or any other artificial calibrant in a cell counter.

Impedance system measure apparent volume which is more than the true volume that is influenced by a shape factor. (26) Shape factor < 1.1 – young, flexible red blood cells 1.1 to 1.2 – fixed biconcave red cells 1.5- spheres either fixed cells or latex spheres. (26, 27)

The Hct and MCV will vary with cell characteristics like shape while determined using automated cell counter. While passing through the aperture in impedance counters, the normal disc shaped red blood cell gets elongated and becomes cigar shaped. This is due to deformation of RBC produced by shear force. It occurs in normal flexible cells. So, cells with low haemoglobin

16 concentration become more elongated than normal leading to reduction of shape factor. This causes reduction in pulse height compared to the true size of the cell underestimating the MCV. In contrast, spherocytes with high Hb concentration and red cells with abnormal rigid membranes undergo less deformation resulting in overestimation of MCV. (29)

Isovolumetrically sphered cells are used. Sphered red cells have light scattering characteristics which are predictable permitting the calculation of both volume of the cell and intracellular concentration of haemoglobin by using calibrated Mie map. It describes the refraction and scatter characteristics of spheres in a monochromatic source of light. (30) Scattering of light by the individual cell is measured at 2 angles. High angle scatter at 5 to 15 degree and low angle scatter at 2 to 3 degree allows calculation of both haemoglobin concentration and cell volume.(29)

Cellular haemoglobin concentration mean –CHCM is a measure of cellular haemoglobin. It is different from the MCHC derived from the PCV and

Hb. MCHC and CHCM should be the same if all the measurements are accurate. This provides internal quality control. When compared to manual methods automated MCV and H ct are prone to some errors.

AUTOMATED DIFFERENTIAL COUNT

Many automated differential counters use flow cytometry that is incorporated into full blood counter. Automated cell counters also provide differential counts as three parts, five or seven part. Differential counts are done on diluted whole blood. Here red cells are rendered transparent or it is lysed.

Three part differential count assigns the cells as 1) large cells / granulocytes 2) small cells / lymphocytes 3) middle cells / monocytes/ mononuclear cells.

Basophils and eosinophils come under granulocyte category but here they are counted under monocyte category.(31) Some three part differential counters assign the white blood cells as WBC-large cell ratio that is equivalent to neutrophils, WBC- middle cell ratio that is equivalent to eosinophils , basophils and monocytes and WBC- small cell ratio that is equivalent to lymphocytes. (32)

Five part differential counters categorise the cells as neutrophils, basophils, eosinophils, lymphocytes and monocytes. Seven part differential count also includes large immature cells / immature granulocytes that is composed of blasts and other immature granulocytes, atypical lymphocytes that includes small blasts. Certain automated counters that do not count nucleated red blood cells or immature granulocytes either flag or reject counts from samples with blasts, promyelocyte, myelocyte, atypical lymphocytes or nucleated red blood cells.

Three part differential counter which are not able to enumerate basophils or eosinophils as separate categories are capable of flagging a sample with increased number of these cells. (33)

Both light scattering and impedance systems are able to produce three part differential from a single channel. Cells are categorised based on the differing volumes of the various cells after partial lysis and cytoplasmic shrinkage. Five part and seven part differential counters needs 2 or more channels. Here cell volume and various other characteristics are analysed by several modalities. Analysis depends on volume or other physical characteristics. It also depends on cellular enzyme activity or on the binding of dyes to granules. In order to study the cell characteristics various technologies are used that includes absorbance and impedance measurement with high and low frequency electromagnetic current / radiofrequency current and light scattering. Before studying the cell characteristics, cells are exposed to lytic agents or to a cytochemical reaction. Either two parameters are analyzed or cells are divided into clusters that are matched with the position of the different white cell clusters of normal blood. Some fixed and variable thresholds divide the clusters from each other allowing the cells in each cluster to be counted. (34)

AUTOMATED IMMATURE GRANULOCYTE COUNT

Automated cell counters count the immature granulocytes that includes promyelocytes, metamyelocytes, and myelocytes . They are not counted as

19 separate classes of cells. As larger number of cells is counted automated analysers reliably detect even smaller number of immature granulocytes than by manual method. Low counts of immature granulocytes in leucopenic blood samples or when only small percentage of cells are present it can be missed in manual 100 cell differential count or by blood film review. Immature granulocytes are identified by light absorbance and impedance after staining the cells or by flow cytometry detecting the side scattered light and by fluorescence staining with a fluorescent dye. The immature granulocytes percentage measured by Sysmex may predict the infection. (35)

Some systems that don’t count nucleated red blood cells separately, their differential counts include some of the nucleated red cell in the total WBC count. Therefore, if there is increased number of nucleated red cells the total count may not be a true WBC count. Absolute White blood cell count calculated from the total may be erroneous.

BLOOD CULTURE (16)

Blood culture is considered as the gold standard in the diagnosis of sepsis. This test should be done in all patients with suspected sepsis before starting antibiotics.

If blood culture reveals growth of a particular organism, we can do sensitivity testing so that we can start appropriate antibiotics. Blood samples should be collected under sterile condition.

Blood cultures are observed for minimum 72 hrs before reporting it as sterile. There are certain advanced techniques like BACTEC AND

BACT/ALERT that can detect the growth of organisms in 12 to 24 hrs.

Moreover, they can detect even bacteria in low concentrations (1-2 colony forming units/ ml).

Blood culture is used in the detection of infections that spread via the blood stream. As blood is sterile normally, it can be employed to detect septicaemia/ bacteremia. When a patient presents with signs and symptoms of systemic infection, from the blood culture results, we can identify whether the infection is present or not and also the type of microorganism that causes the infection. Blood culture is used to identify the infective organism in case of sepsis, puerperal fever, pelvic inflammatory disease, severe pneumonia, neonatal epiglottitis and fever of unknown origin. Even if there is no growth in blood culture infection is not excluded.

METHOD

Strict aseptic technique is followed while collecting blood. This involves cleaning of the venipuncture site with 70% isopropylalcohol or povidone and

21 are allowed to dry. This reduces the contamination of skin commensal producing false-positive results. Minimum 10 ml blood is collected and with new needle blood is injected to 2 or 3 bottles containing specific culture media for both anaerobic and aerobic organisms. (36) Then they are sent to the microbiology department. Here, the culture bottles are incubated at body temperature. They are monitored for 5 days. If the culture vials reveals no growth they are removed. If the vial shows growth, then Gram stain is performed on the blood. This blood is also subcultured onto the agar plate in order to isolate the causative organism. Susceptibility testing is also done that takes upto 3 days. Assessment of antibiotic sensitivity is needed so that appropriate antibiotic can be started. In order to confirm series of 3 blood cultures are performed. (37)

LUMBAR PUNCTURE (14, 38)

Meningitis constitutes 0.5 – 3% of neonatal sepsis. Meningitis can occur along with septicaemia without any organ specific symptoms. Hence, lumbar puncture is warranted in neonates having clinical suspicion of sepsis. In early onset sepsis, lumbar puncture is performed if there is positive blood culture or if there is clinical signs and symptoms of sepsis. For late onset sepsis, lumbar puncture is performed in infants before starting antibiotic therapy.

INTERPRETATION

CELL COUNT

The presence of leucocytes in CSF is known as pleocytosis. Normally, few monocytes can be seen. If granulocytes are seen in CSF it is definitely abnormal. There will be increased granulocytes in bacterial meningitis.

LEUCOCYTES IN CSF

Leucocytes in CSF are also seen in

 Reactions to previous drugs or dyes

 Repeated spinal taps

 Leukemia

 Metastasis

 CNS haemorrhage

 Recent epilepsy

Peripheral blood can be contaminated with CSF which is a common complication. If so, leucocytes are seen admixed with red blood cells and their ratio is same as in peripheral blood. Erythrophagocytosis if present, it implies that CSF haemorrhage has occurred prior to the lumbar puncture. (39) Hence erythrophagocytosis suggests other causes like herpetic encephalitis and intracranial bleed. In such cases, viral culture and imaging studies have to be done.

MICROBIOLOGY

CSF collected can be subjected to microbiological examination to rule out meningitis.

Various tests done are Gram staining, microbiological culture, polymerase chain reaction.

Gram stain is used to identify bacteria in case of bacterial meningitis. (40)

Microbiological culture is considered as the gold standard in detecting bacterial meningitis. Viruses and fungi are also cultured using respective culture techniques.

Polymerase chain reaction has been widely used nowadays in the diagnosis of certain viral meningitis like enterovirus and herpes virus and

Neisseria meningitidis.Though PCR technique is expensive, we can get the results fast and it is a highly sensitive and specific technique. It can be done even with small amount of CSF. (41, 42)

MARKERS IN SEPSIS

Numerous serological markers like pro-inflammatory cytokines, chemokines, acute phase proteins, adhesion molecules and cell surface proteins are used to diagnose sepsis early.

 Interleukin-8

 Interleukin-6

 C- reactive protein

 Procalcitonin

 Tumour necrosis factor- alpha

 Integrin alpha-M

 CD64

 L- Selectin

 Melatonin

INTERLEUKIN 8 IN THE DIAGNOSIS OF LATE ONSET SEPSIS

Interleukin 8 is a C-X-C chemokine produced mainly by macrophages, epithelial cells, endothelial cells airway smooth muscle cells. (43) Interleukin 8 is a main chemotactic factor for neutrophils and other granulocytes. (44)

Interleukin 8 in serum is higher in infants with sepsis when compared to normal neonates. Among the sepsis cases, interleukin 8 level is lower in survived infants than in those who succumbed to infection.

INTERLEUKIN-6

IL-6 acts as pro-inflammatory cytokine secreted mainly by T- lymphocytes and macrophages. It induces the hepatocytes to synthesize the acute phase reactants.IL-6 rises rapidly following the invasion of bacteria. It is highly sensitive during the early phases of infection as the cytokines are

25 elevated before CRP. But during the late stages it is not sensitive because of its short half-life. This may be due to plasma protein binding and inactivation resulting in normalisation of values although the sepsis persists.

C reactive protein

C – Reactive protein is an acute phase protein produced in liver. (45)

Normal CRP level in serum is 5 to 10 mg/L. It is the first identified pattern recognition receptor. It binds with its high affinity ligand phosphocholin which is present in the cell walls of microbes and in many bacterias. It activates the complement pathway through C1Qcomplex. Interleukin-6 and interleukin -1 helps in the production of CRP by hepatocytes.CRP is higher in pregnancy, viral infection(10-40 mg/L), bacterial infection(40 – 200 mg/L), severe infections and burns -200mg/L and above.

CRP crosses the placenta only in minute quantities. Hence elevation of

CRP in neonates indicates endogenous synthesis. De novo synthesis of CRP starts rapidly within 2 hours of inflammation and attain a peak at two days.

Elevated CRP level is not always indicative of sepsis as the rise may be physiologic after birth or due to non- infectious causes.

CRP is not reliable in the early stages of sepsis. Determination of single

CRP value is also not sufficient for diagnosis. Therefore, serial measurements

26 of serum CRP helps to monitor the treatment response, to identify the possible complications and to determine the duration of antibiotic treatment.

If two consecutive CRP levels are less than 10mg/l which is determined more than 1 day apart it indicates infants are not infected or the infection has resolved.

Preterm infants have lower CRP baseline levels and their response to infection is also lower than term infants. Initially determination of CRP should be combined with other sensitive serological markers like IL-6, IL-8, and procalcitonin.

Non-infectious causes for elevation of CRP in neonates

 Perinatal asphyxia/ shock

 Prolonged labour

 Fetal distress

 Prolonged rupture of membranes

 Maternal fever

 Meconium aspiration

 Application of Surfactant

 Pnumothorax

 IVH

 Tissue injury

PROCALCITONIN

Procalcitonin is the precursor of calcitonin, the hormone involved in calcium homeostasis. Normal procalcitonin level is <0.01 microgram/L that cannot be detected by routine clinical assays. Procalcitonin level rises following bacterial infection. In such cases it is released mainly by intestinal cells and lung cells. When procalcitonin level is greater than 0.5 microgram/L, antibiotic can be started. If it is less than 0.1 microgram/L there is no need for antibiotics.

(46) Procalcitonin was significantly higher in gram positive sepsis than gram negative sepsis.

TUMOUR NECROSIS FACTOR ALPHA (TNF-alpha):

TNF-alpha is also known as cachexin or cachectin. This cytokine is produced by macrophages, CD4 T cells, NK cells, mast cells, eosinophils, neutrophils, neurons. TNF is an acute phase protein responsible for systemic inflammation.

(47) TNF is released in response to bacterial products, lipopolysaccharide. It acts along with interleukin 1 and interleukin 6 in sepsis. In the liver, it stimulates the release of C- reactive protein. Helena Martin et al, found that serum levels of

IL-8, IL-6, and TNF alpha were higher in septic neonates.

INTEGRIN ALPHA M (ITGAM)

ITGAM also known as complement receptor 3 (CR-3) or CD11b or macrophage-1 antigen. It is the heterodimeric integrin found on the surface of

28 white blood cells, macrophages, natural killer cells, granulocytes. (48) This integrin is the mediator of inflammation producing phagocytosis, leukocyte adhesion, chemotaxis and cellular activation. It binds inactivated component of complement 3b.

CD64

Cluster of differentiation 64 is an integral membrane glycoprotein otherwise known as Fc receptor. Neutrophils express CD64 when they are exposed to G-CSF and interferon gamma. SIRS patients had increased expression of CD11c, CD64 and EMR2 on neutrophils. (49)

L-Selectin

This is a cell adhesion molecule otherwise called CD62L. It recruits lymphocytes to the secondary lymphoid organs. (50) Central T lymphocytes express L-selectin that will proliferate when they encounter antigen. There are increased expression of L-selectin in leucocytes during sepsis. L-selectin is a predictor of mortality due to sepsis. (51)

MELATONIN

Melatonin is an indolamine which is endogenously synthesized by pineal body. It is an anti-inflammatory agent and also an anti-apoptotic factor. El-

Mashad et al found that endogenous melatonin levels are elevated in late onset neonatal sepsis and it can be used as a potential serological marker for sepsis

29 when combined along with CRP. As melatonin also has anti-oxidant property, it scavengesthe free radicals responsible for the pathogenesis of neonatal sepsis and the related complications. Elosio Gitto et al found that white blood cell count, absolute neutrophil count and CRP were reduced significantly in melatonin treated sepsis infants.

URINE CULTURE

Urine cultures are usually not indicated as the yield is very low. But in the following conditions urine examination has to be done.

 Neonates suspected to have fungal infection

 Neonates with vesicoureteric reflex

 Neonates having urogenital malformation

 Neonates having symptoms of urinary tract infection ( crying during

micturition ).

URINE SAMPLE

Urine sample can be obtained by three ways

 Clean catch midstream urine sample

 Suprapubic aspiration

 Catheterising bladder

DIAGNOSIS OF URINARY TRACT INFECTION

Urinary tract infection should be diagnosed if there is any one of the following

 Centrifuged sample- presence of > 10 white blood cells/ cu.mm in 10ml

of urine

 Suprapubic aspiration- presence of any microorganism in urine

 Catheterised sample- presence of > 10000 organisms/ ml.

RADIOLOGY

 Chest X-Ray is indicated in case of apnea or respiratory distress.

 Abdominal X-Ray should be considered when there are signs of

necrotising enterocolitis.

 CT should be done in all neonates having meningitis.

Clinical features of sepsis are grouped under a syndrome called Systemic

Inflammatory Response Syndrome.

SIRS- SYSTEMIC INFLAMMATORY RESPONSE SYNDROME (52 – 55)

Systemic inflammatory response syndrome is a systemic inflammatory condition affecting the various organs mainly due to immune system response to infection. There will be cytokine storm. It may be due to infectious or non- infectious cause.

PAEDIATRIC SIRS CRITERIA (53)

 In children, either the heart rate should be more than 2 SD above normal

for that age in the absence of pain or drug administration or persistently

elevated heart rate for more than half an hour to four hours that is

unexplained.

 For infants, the heart rate should be < 10th percentile for that age inthe

absence of drugs like beta-blockers, vagal stimuli or any congenital heart

disease. It also includes persistently depressed heart rate for more than 30

minutes that is unexplained.

 Abnormal body temperature that is >38.5 degree C or < 36 degree C that

is recorded either orally, rectally or from Foley catheter.

 Respiratory rate should be more than 2 SD above normal for that age or

the mechanical ventilation requirement that is not due to neuromuscular

disease or anaesthetic administration.

 Higher or lower leucocyte count for that age that is unrelated to

chemotherapeutic drugs or band forms more than 10% including other

immature forms.

CAUSES (54, 55)

 Infectious

 Non-infectious

When infection leads to systemic inflammatory response syndrome it can be considered as sepsis.

Non infectious causes

 Burns

 Trauma

 Haemorrhage

 Ischaemia

 Pancreatitis

 As surgical complication

 Pulmonary embolism

 Complicated aortic aneurysm

 Cardiac tamponade

 Adrenal insufficiency

 Anaphylactic reaction

 Drug overdosage

SIRS leads to single or multiorgan failure.

It includes,

 Acute kidney injury

 Acute lung injury

 CNS dysfunction

 Shock

 Multiorgan dysfunction

INITIATION OF SIRS (52, 53)

Any infection, inflammation or trauma activates inflammatory cascade.

Infectious agent release either exotoxin or endotoxin that induces macrophages, mast cells, endothelial cells and platelets to release cytokines. Interleukin 1 and tumor necrosis factor alpha is first released and they cleave NF-KB inhibitor.

Hence NF-KB is activated. It enters the nucleus from cytoplasm and starts the transcription of several genes involved in inflammation. It initiates the formation of several pro-inflammatory cytokines. In case of viral infection interferon gamma is released. NF-KB mainly induces IL-8, IL-6 and interferon gamma.IL-6 causes the release of C-reactive protein. TNF-alpha is released more in infection than in trauma. Hence there is high fever in infection than trauma. The proinflammatory interleukins act on the tissue directly or it causes activation of complement ascade, coagulation cascade. Complment proteins C3a and C5a causes’ vasodilation, hence vascular permeability is increased.

Prostaglandins produce endothelial damage resulting in multiorgan failure.

TNF-alpha and IL-1 serves 2 functions. One, it acts on endothelial surfaces exposing the tissue factor leading to the formation of thrombin.

Thrombin promotes further coagulation it itself a proinflammatory mediator.

Second, they impair fibrinolysis by plasminogen activator inhibitor -1

34 production. In addition, the cytokines inhibit activated protein C and antithrombin. If coagulation cascade is unchecked it results in the formatiom of microvascular thrombosis and multiorgan dysfunction.

BALANCE BETWEEN INFLAMMATORY AND ANTI-INFLAMMATORY

RESPONSE (55)

In order to tackle the inflammatory response, counter inflammatory response is activated. Both processes run simultaneously.

Counter inflammatory response involves anti-inflammatory cytokines IL-

10 and IL-4. They inhibit the formation of IL-8, IL-6, IL-1 and TNF- alpha.

They are involved in the production of TNF- alpha and IL-1 receptor antagonists. Hypothalamo- pituitary –adrenal axis activation leads to the formation of glucocorticoid. It has immunosuppressive effect thereby inhibits the release of cytokine.

Patient’s prognosis is determined by the balance between SIRS and counter response. The antagonistic system is activated mainly to balance the effect of pro-inflammatory and anti- inflammatory cytokines. So any derangements results in excess activation of proinflammatory cytokines leading onto severe systemic inflammatory response syndrome with high risk for multiorgan dysfunction. 2. Immunosuppression due to excess of anti-inflammatory cytokines.

ORGAN DYSFUNCTION (56)

 SIRS can cause multi-organ dysfunction. It mainly affects lungs, kidneys,

liver, heart and CNS.

 Mechanisms involved are

o Dilation of blood vessels

o Increased vascular permeability- as starling’s mechanism is

impaired , fluid enters the interstitial space

o Endothelial damage and formation of microvascular thrombosis

leading onto DIC

o Formation of free radicals

o Formation of proteases

o Nitric oxide synthase induction resulting in NO production

RESPIRATORY DYSFUNCTION (56)

It is common in SIRS. Patient either have tachypnoea, hypoxia or respiratory alkalosis. In severe cases it leads to acute lung injury or ARDS.

There is dysfunction of endothelium of pulmonary capillaries due to proinflammatory cytokines. As capillary permeability is increased , it results in accumulation of interstitial fluid and formation of protein rich alveolar edema.

With progression there will be destruction of surfactant, type I pneumocytes and formation of microatelectasis.

CARDIOVASCULAR DYSFUNCTION (56)

Pro-inflammatory cytokines have its effect on heart and the blood vessels.

Nitric oxide is synthesized from L-Arginine found in the endothelium of blood vessel by inducible nitric oxide synthase enzyme. NO causes hypotension due to reduction of systemic vascular resistance. Hence cardiac output is increased.

Baroreceptors are stimulated causing tachycardia. Hence stroke volume is increased. But hypovolemia causes decrease in preload thereby reducing the cardiac output. Moreover endotoxins and cytokines causes myocardial depression within 24 hours of SIRS. Constitutive nitric oxide causes myocardial relaxation thus increasing end diastolic volume. Inducible nitric oxide decreases the contractility of the myocardium.

RENAL DYSFUNCTION (56)

As there is systemic vasodilation in SIRS renal perfusion is reduced.

There is cytokine induced production of vasoconstrictors like thromboxanes and leukotrienes that decreases renal blood flow. Renin-angiotensin system is also activated. Kidney is also damaged by leucocyte mediated injury causing neutrophil aggregation and formation of reactive oxygen species.

GASTROINTESTINAL DYSFUNCTION (56)

Hypoperfusion distrupts the intestinal wall causing barrier dysfunction.

As a result there is translocation of bacteria from the intestinal lumen to the internal environment.

METABOLIC DYSFUNCTION (56)

Hypoperfusion results in hypoxia of tissues and lactic acidosis. Nitric oxide blocks cytochrome oxidase and hence affects mitochondrial electron transport resulting in cellular hypoxia and formation of reactive oxygen species.

HAEMATOLOGICAL DYSFUNCTION (56)

Coagulation pathway is affected in SIRS due to cytokine mediated release of tissue factor from the endothelium. This results in disseminated intravascular coagulation leading to formation of microvascular thrombi and also bleeding.

Antithrombin III inhibits not only thrombin but also factors IX, X, XI,XII.

Thrombomodulin is derived from the endothelium which inhibits clotting and activates fibrinolysis. This thrombin binding protein decreases the action of thrombin. Thrombin-thrombomodulin complex activates protein C and protein

S which inhibits factor V and factor VIII. In sepsis, thrombomodulin production and circulating protein S levels are reduced.

Expected haematological values for term newborns

At birth, term babies have different red blood cell count, white blood cell count and haemoglobin levels when compared to adults. They have relative polycythemia and their red blood cells are macrocytic with marked polychromasia. Nucleated red blood cells are seen and white blood cell count is high. After birth, there are significant changes in oxygenation. Erythropoietin slowly disappears after few days. Hence red blood cell production is markedly reduced during the 1st week of life. This results in the development of physiological anemia which is transient during the end of neonatal period. (57) In the postnatal period the haemoglobin, RBC count and MCV all gradually decreases.RBC count reaches its lowest level at 7th week and the haemoglobin reaches its lowest point only during 9th week. This delay in the fall of haemoglobin concentration is due to high MCV that progressively decreases and adult level is reached by 11 weeks. Mean corpuscular haemoglobin concentration is relatively low in newborns when compared to adults. MCHC increases in the first 5 weeks and then it remains constant. As the erythropoietic activity is persistent during the first few days the reticulocyte count is high after birth. At the 1st week reticulocyte count drops and remains low. Then it increases and attains the peak level at 9th week. As the size of the red blood cell varies red cell distribution width is elevated. In particular, premature neonates

39 have more number of irregular red blood cells that is schistocytes, keratocytes and acanthocytes.

In full term infants haemoglobin F is 50 to 80% and haemoglobin A is 20 to 50%. After birth haemoglobin A is predominant. As the oxygen affinity for

Hb A is low than Hb F it delivers oxygen readily to the tissues thus provides better oxygenation. As erythropoietin level is maintained by tissue partial pressure of oxygen its level decreases following better oxygenation. After birth erythropoietin is produced mainly from kidney. (57) Nucleated red blood cells are routinely seen in the 1st day of healthy neonates. 0 to 10 nucleated red blood cells per 100 white blood cells can be present. Normally after birth nucleated red blood cells are cleared from the peripheral blood. By 3rd to 4th day, there will not be any nucleated red blood cells. But in preterm babies it can be seen upto one week. (58)

Term neonates have increased white blood cell count with increased neutrophils at birth. The neutrophils continue to rise in the first 12 hrs then it attains a peak and further it decreases gradually. Neutrophil count will be lower around 3rd day of life and then it increases gradually to attain the stable level by

5th day. This count is maintained throughout the neonatal period. Certain perinatal factors alter the dynamics of neutrophils. That includes maternal fever, maternal hypertension, haemolytic disease of newborn and perivascular haemorrhage. (59) Girl babies have higher neutrophils 2000cells/ microlitre than

40 boys. Increased neutrophils at birth is mainly due to mobilization of neutrophils from bonemarrow because of the stress occurring in the labour period and not due to increased production of white blood cells in the bonemarrow.(57)

Neutrophils show shift to left . Many metamyelocytes, myelocytes and even blasts are noted in the peripheral blood. Rarely, micromegakaryocytes may be seen in the peripheral blood of early neonates. It should not be considered as blasts. Neutrophil count is increased only transiently at birth. Later, lymphocytes form the predominant population which remains even in early childhood. In bone marrow erythroid series show marked hyperplasia and myeloid series show relative hypoplasia. At the last weeks of pregnancy, there will be rapid growth of fetus and at that time production of red blood cells are 3 to 5 times more than that of adults. (57)

HAEMATOLOGICAL VALUES IN SMALL FOR GESTATIONAL AGE

TERM NEONATES

If birth weight of the baby is below the tenth percentile it is considered as small for gestational age. Small for gestational age neonates have different complete blood count parameters when compared to appropriate for gestational age neonates. (60) Ozbek et al found that at day 1 SGA neonates have increased haemoglobin, packed cell volume, red blood cell count and nRBCs than AGA babies. SGA neonates have increased erythropoietin levels because of intrauterine hypoxia. Hence they show higher red blood cell indices and relative

41 polycythemia. Compared to AGA term babies, SGA neonates have decreased leucocyte count and shift to left in neutrophilic series is more pronounced as there is more metamyelocytes in the peripheral blood. 34% of SGA neonates have platelet count < 1.5 lakhs/cu.mm. But only 4% of AGA neonates have reduced platelet count. Platelet count reaches normal by 7th postnatal day. (61)

EXPECTED HAEMATOLOGICAL VALUES FOR PRETERM INFANTS

Production of erythropoietin and composition of fetal blood are highly varied with gestational age. Hence normal haematopoiesis is interrupted in premature birth of babies. Thus premature babies have very low level of erythropoietin and reduced red cell mass at birth. Anaemia of prematurity may be due to rapid growth of the body producing hemodilution, reduced lifespan of red blood cells, poor body iron stares and initial dependence of liver as the source of erythropoietin. (62) Hence the haemoglobin concentration continues to decrease for a more period of time in premature babies for about 8 to 12 weeks.

Moreover, their RBC count is low with reduced life span. Haemoglobin and hematocrit values are reduced but they have higher mean corpuscular volume.

When compared with term babies, preterm babies have more nucleated red blood cells and they remain longer in blood. (63)

HAEMATOLOGY LABORATORY ISSUES RELATED TO NEONATAL

BLOOD SAMPLES (64)

There are number of pre analytic variables that affect the quality of laboratory test. It includes collection of specimen, handling of specimen, size of the sample and analytic interference. These factors are considered for samples of any age but it is much more important in neonates and infants.

PREANALYTIC FACTORS AFFECTING HAEMATOLOGY TESTING IN

NEONATES AND YOUNG CHILDREN

Limited blood sample, different sampling sites varies the test results vigorous crying of the baby or any exertion affects the test results. (65)

LIMITED BLOOD AVAILABILITY

In children the total blood volume is markedly lower when compared to adults as it depends on the weight and height of the individual. The total blood volume of preterm and term babies ranges from 80ml/kg to 120ml/kg. So in preterm babies, 10ml of blood collected for testing represents 10% of the total volume of blood. (66) Therefore, blood drawn should not be more than 5% of total volume of blood per draw. Hence in infants, less amount of blood is available for testing. Moreover, repeated phlebotomies may produce iatrogenic anemia. Hence greater scrutiny is needed while collecting blood in preterm neonates.

As vacutainer has constant amount of anticoagulant, very small amount of blood collected may cause clotting of the sample or produce haemodilution.

To reduce those problems, Microtainer tubes are used. But this tube is so small and is of nonstandard size. It should be handled manually as automatic haematology analyzers or robotic systems cannot process these tubes automatically. Moreover, clotting of blood, insufficient sample and haemolysis are commonly encountered problems in neonates. (65)

VARIATION OF RESULTS DEPENDING ON BLOOD SAMPLING SITES

In neonates and children, we can collect blood from so many sites by direct puncture of arteries, umbilical vessels or peripheral vein catheterization, heel prick. In heel prick, we can get adequate amount of blood and there is no risk of vascular catheterization. (67) Automatic devices can be used for collecting blood. In that case, a standard incision is made on the infant’s heel and adequate blood is collected for testing. Blood collected from skin puncture has various proportion of blood from venules, arterioles, intracellular and interstitial fluids. Blood counts show some variations according to the sampling site and they are very much pronounced in neonates.

Composition of blood varies among veins, arteries and skin puncture and they are not considered equivalent. Warming the heel of the infant may improve the circulation but it does not reduce these differences. (68) Skin puncture blood have increased haemoglobin, red blood cell count and packed cell volume.

White blood cell count and neutrophils are also increased. Capillary blood composition can be affected by perfusion status, metabolic state or several other factors. Microcirculation causes higher capillary haematocrit. (69) Therefore in a sick neonate, haematocrit value obtained from capillary blood may be misleading and underlying anaemia may be missed.

Unlike the other red blood cell indices, venous mean corpuscular volume is higher than capillary MCV. This is due to capillary blood hemoconcentration and loss of fluid from red blood cells. Mean corpuscular concentration and red cell distribution width is same in both venous and capillary blood.

Leukocyte counts and differential counts also varies between arterial, capillary and venous blood. Arterial blood has lower WBC count than capillary and venous blood. Hence we may consider the neonate to be neutropenic when actually the arterial blood has normal WBC count. There are only minor differences when the arterial and venous samples are collected simultaneously.

Mean corpuscular haemoglobin concentration is slightly more and neutrophil counts are same.

Platelet counts are same in arterial, venous and heel stick samples. Other studies reveal low platelet count in capillary samples when compared to venous samples. (69) As heel stick causes activation, aggregation and local consumption of platelets it produces low platelet count. This observation is also supported by the finding that venous platelets have lower mean platelet volume than capillary

45 platelets. Resting platelets have lower mean platelet volume than activated platelets. (69)

The difference in the neutrophil counts and haemoglobin values among arterial and capillary blood are more pronounced in preterm neonates.

Thurlbeck and McIntosh found that capillary blood haemoglobin is higher than arterial blood with an average difference of 2.5 g/dl. WBC count in capillary blood is higher than arterial blood with a mean of 1.8 x 10^9/L. (70) If we are aware of these differences existing between the samples obtained from different sites we can avoid the unnecessary intervention.

ANALYTIC FACTORS AFFECTING HAEMATOLOGY TESTING

Blood samples of neonates often encounter analytic interference than adults.

Blood sample may be inadequate resulting in various problems. There may be excess EDTA or insufficient blood sample producing fibrin precipitates, aggregates of platelets and leucocytes. This leads to spuriously reduced platelet and leucocyte counts. As there is insufficient blood sample repeat testing of the sample cannot be done to confirm the test results thus further complicate the problem. There is no much information regarding any differences between automatic analyzers and laser technology in dealing with neonatal blood samples. (71) Both instruments have its own advantage but none is considered as the superior instrument regarding analysis of neonatal blood samples. Neonatal sample analysis can be done in Beckman Coulter, sysmex or Abbott

46 instruments. An automated differential white blood cell count done on Beckman

Coulter is considered superior to manual differential leucocyte count. Though automatic analyzers are fast and measure accurately, at times spurious results can be obtained. In neonates, there is much interference in haematological analysis.

 The neonatal red blood cells are resistant to lysis.

 Increased number of nucleated red blood cells

 Increased serum bilirubin

 Hyperlipidemia

 In neonates receiving total parenteral nutrition.

In case of hyperbilirubinemia , hyperlipidemia due to total parenteral nutrition and high leucocyte count ther will be increased turbidity of the blood sample. It may result in high haemoglobin levels. Hence MCH, MCHC and

PCV all will be elevated spuriously. (65)

POST ANALYTIC ISSUES: INTERPRETATION OF HAEMATOLOGICAL

RESULTS IN TERM AND PRETERM NEWBORNS

As interpretation of results is the starting point to arrive at differential diagnosis thourough knowledge of the haematological values of newborns is necessary for proper diagnosis. Complete blood count is associated with

47 gestational age, weight at birth, crying, sampling site, delivery mode, physical therapy and several other factors. (65)

REFERANCE INTERVALS FOR NEWBORNS

As neonates and infants haematological values different from adults separate reference values should be maintained in each laboratory. However health related reference values are difficult to obtain in neonates and young infants. Extra blood samples from normal healthy infants cannot be obtained.

As it is a difficult task many laboratories use already published reference values instead of formulating their own. As reference intervals depends on the method/ technology applied the values obtained with old instrumentation or manual counts are not employed in current practice. With the current instruments and haematology analysers the results obtained are more precise and more accurate.

In order to solve the problem manufacturers establish their own reference values. This, statistical methods have been established to formulate reference intervals.

Haematological values of children who are hospitalized for some health problems are used in generating reference intervals. By using this method

Soldin generated reference intervals for newborns which were published in

American association for clinical chemistry press. (72) Main problem with the published reference intervals for newborns including the published intervals of

AACC does not include the site of sampling, weight of the newborn, gestational

48 age and race. The reference intervals are based on the assumption that all neonates and all blood samples are similar. Term neonates have haematological values different from preterm babies. Hence reference values of term babies cannot be applied for preterm babies. As gestational age advances haemoglobin and packed cell volume increases, mean corpuscular volume decreases.

In preterm neonates white blood cell count is 30% to 50% lower as compared to term neonates. Even though it is known that gestational age alters the haematological parameters, the exact degree of this effect is not certain due to the inconsistent and limited published data available for preterm neonates.

Due to this reason applying reference intervals of term neonates to preterm babies may result in misdiagnosis of anaemia. It may lead to unnecessary workups and repeating the blood sample may aggravate the anaemia. Moreover, composition of venous blood, arterial blood and capillary blood differ from one another. Hence reference intervals generated by using capillary blood will vary from reference intervals generated by using venous blood.

There are also ethnic and racial differences that are not included in current reference intervels. White infants have higher haemoglobin, mean corpuscular volume, and packed cell volume than black infants. (73)

PLATELETS IN THE NEONATAL PERIOD

Platelets appear first in the fetus at 5th week after conception and it increases in number reaching 1.5 lakhs by the end of 1st trimester. It reaches adult levels by 22 weeks. 22 weeks is considered as the lowest gestational age at which the fetus is viable. Hence even the most premature neonates have platelet counts in the range of 1.5 to 4.5 lakhs. Healthy preterm neonates usually have platelet counts in the range of 1 to 1.5 lakhs when compared to term neonates, children or adults. Therefore in neonates thrombocytopenia is considered when platelet count is below 1.5 lakhs. (74) Incidence of thrombocytopenia in sick neonates admitted in NICU is higher than in the general neonates. Most of them are most premature having birth weight < 1 kg. Intracranial haemorrhage is common in preterm neonates of any age and 25% of neonates with birth weight less than 1.5 kg have an intraventricular haemorrhage especially during the 1st week. But the cause of IVH in this age is multifactorial and IVH commonly seen in preterm neonates having normal platelet counts. As premature neonates have immature haemostatic system and frequent occurrence of intracranial haemorrhage producing poor neuro-developmental outcome such thrombocytopenic infant has to be transfused with higher platelet counts than adults. But transfusion thresholds are not established and hence there are extreme variability in practicing platelet transfusion. (75)

RESPONSE OF NEONATAL MEGAKARYOCYTES TO

THROMBOCYTOPENIA

Normally, adult bone marrow responds by increasing the size and ploidy of megakaryocytes first followed by increasing the number of megakaryocytes inorder to overcome the increased platelet demand. This results in 2 to 8 fold increase of megakaryocyte mass. Neonates with thrombocytopenia can increase the platelet number but the size is not increased. Hence neonatal megakaryocytes have some developmental limitations to increase the size to response to increased platelet demand. (76)

PLATELET FUNCTION AND PRIMARY HEMOSTASIS IN NEONATES

Platelet transfusions are provided routinely when the platelet count is below a certain level but the risk of bleeding is determined not only by platelet count but also by several factors like gestational age, post conceptional age, platelet function and haemostatic balance. Recent study reveals that among thrombocytopenic neonates 90% of significant clinical haemorrhages occurs in

< 28 weeks of gestation and during first 14 days of life. (77) Preterm neonates are more prone to bleeding but most of the studies are conducted in term neonates using cord blood.

PLATELET FUNCTION IN TERM NEONATES

The developmental differences of platelets are first established by platelet aggregation studies. It is done in platelet- rich plasma derived from cord blood of term neonates. Initial studies revealed that cord blood derived neonatal platelets were less responsive to agonists like epinephrine, ADP, collagen and thrombin. Flow cytometric studies also reveals that neonatal platelets when stimulated with agonists reduced expression of platelet surface activation markers. The reduced response to epinephrine is due to lower alpha 2 adrenergic receptors. (78) The hyporesponsiveness to collagen is due to impaired calcium mobilization and to thromboxane is ineffective signalling of the downstream pathways.

Bleeding times were shorter in full term neonates. PFA- 100 revealed shorter closure times in full term neonates. All these studies suggested an increased vessel wall- platelet interaction that causes increase in haematocrit,

MCV, VwF. There is increased VwF polymers in neonatal blood which counteract the hyporeactivity of platelets. (79)

PLATELET FUNCTION IN PRETERM NEONATES

Thrombocytopenia incidence is highest in preterm neonates especially <

30 weeks .There is also increased risk of bleeding in these neonates.

Flowcytometric and aggregometric studies revealed that platelets were

52 hyporeactive at birth in preterm neonates. In vitro hyporeactivity of platelets was more pronounced in preterm than full term neonates. These were most evident on infants with lowest gestational age (< 30 weeks) imparting a correlation between gestational age and reactivity of platelets.

Two of the studies with cone and platelet analyzer revealed that platelet adhesion was reduced in preterm neonates as compared to full term and the adherence was also related to gestational age in the first 2 days of life. Hence the difference in the adhesion of platelets was not due to low VwF antigen levels or the ristocetin cofactor activity. They were because of some developmental differences in platelet function. (80) Bleeding times done on 1st day of life were higher in preterm when compared to full term neonates.

Neonates with < 33 weeks exhibit longest bleeding time that is approximately twice as long as in full term neonates.

Closure time done on 1st day neonatal blood in response to ADP using

PFA-100 was inversely correlated to gestational age. These differences reflect the lower haematocrit and more pronounced hyporeactivity of platelets in preterm neonates. Closure times measured in neonatal blood even in the 1st 48 hrs of life using PFA- 100 were longer than cord blood. They remained shorter or same as adult closure times at all gestational ages suggesting that pretetrm babies have adequate primary homeostasis. (81) Flow cytometry and platelet

53 aggregation studies suggested that hyporeactivity of platelets was present 3- 4 days after birth in both full term and preterm neonates.

The gestational age dependent difference in adhesion of platelets found to persist for 10 weeks. Anemia prolongs the BT in preterm in the 1st week of life.

Preterm neonates with sepsis have reduced platelet adherence than healthy preterm neonates suggesting a increased bleeding tendency in that population.

Full term neonates born to mothers with gestational diabetes and pregnancy induced hypertension displayed lower platelet adhesion as compared to healthy full term infants. (82)

ABSOLUTE NEUTROPHIL COUNT

Total white blood cell count acts as a good predictor of occult bacteremia and absolute neutrophil count is more sensitive than total WBC count. (83)

BAND COUNTS AND TOXIC CHANGES (84)

Absolute neutrophil count and morphological changes in neutrophils like toxic granulations, Dohle bodies and vacuolations are more sensitive in predicting bacterial infections. Neutrophil band counts have greater sensitivity in infants.

IMMATURE COUNTS

Seebach et al evaluated neutrophil left shift parameters in the diagnosis of infectious diseases. It was found that band count of 20% or more of total

54 leucocyte count has 79% specificity and 53% sensitivity and it was superior to

I:T ratio.

Leucopenia, neutropenia, elevated I:M and I:T ratio were important predictors of sepsis during the first 3 days of life and C reactive protein was the best after 3 days of life. (11)

Identifying the patients with sepsis or very prone to sepsis is the most important aspect of the management. Properly collected blood culture before giving the antibiotics is the gold standard to diagnose sepsis. But the organism takes time to grow in blood culture medium and the final report will be available only after a minimum period of 48-72hours. Antibiotics are started when the sepsis is clinically suspected after taking the blood sample for culture and other blood parameters. Sometimes there may be no growth in the blood culture but the antibiotics are started on the basis of clinical suspicion which will cause the community and hospital acquired antibiotic resistance. Moreover, it increases the cost of the treatment. To avoid this problem, there are other methods which help to predict the neonate in sepsis and so that antibiotics can be started appropriately. There are certain studies which assessed the haematological parameters, which rapidly diagnose the neonates with sepsis.

But only a few studies, from India, compared the clinical parameters with the haematological parameters in patients with sepsis. Hence this study was conducted in our institution. (4, 5)

MATERIALS & METHODS

MATERIALS AND METHODS

INCLUSION CRITERIA:

Neonates( < 28 days of age ) with clinically suspected infection.

EXCLUSION CRITERIA

Neonates who received antibiotics already.

STUDY DESIGN:

Prospective observational study

SAMPLE SIZE:103

This is a prospective observational study which includes neonates (<28 days of age) who were admitted in neonatal intensive care unit of Coimbatore medical college hospital. Duration of the study was between July 2015 to July

2016. Sample size of this study was 100. Blood samples were collected before starting antibiotics. In neonates with suspected infection, 2 ml of peripheral venous blood was taken under sterile precautions. 1 ml for blood culture and1 ml for peripheral smear. 1ml blood was collected in conventional blood culture tubes and was sent to the microbiology department for assessing culture sensitivity. The culture reports were collected after 72 hours.

Peripheral smear

A clean glass slide of size 7.5 x 2.5 cm and thickness of 1 mm was taken and was wiped with cotton to remove any dust. The slide was labelled at one end. The spreader slide was made by breaking the one corner of the slide with the help of a glass cutter so that its width is 1.8cm.

A drop of blood was placed at 1cm from one corner of a slide in the midline and a spreader slide was placed at a 30 degree angle in front of the drop

.Then the slide was moved backwards so that it touches the drop. Then the blood was spread along the slide for a length of 3 cm. A monolayer was formed where the cells were widely spaced to enable cell count. In the slide this monolayer was formed in the feathered edge created by the spreader. Blood film made was allowed to air dry. The air dried smear was stained with Leishman stain.

STAINING PROCEDURE

The slide was set in a rack and was flooded with Leishman stain for 2 minutes. And then double the volume of buffer was added and was kept for 20 minutes. After that the slide was washed in tap water and was air dried.

Blood films were examined under oil immersion. Total white blood cell count, platelet count were analysed with Sysmex auto- analyser and total white blood

57 cell count were corrected for nucleated red blood cells. Differential count and neutrophilic changes were checked manually.

Haematological parameters like total leucocyte count (TLC), total polymorphonuclear neutrophil count (PMN), immature polymorphonuclear neutrophil count(iPMN), immature: Total (I:T) PMN ratio, immature: mature

(I:M) PMN ratio, platelet count, degenerative or toxic changes in neutrophils were assessed and scored. All these seven parameters were assigned a score of 1 if they were abnormal. Abnormal total leucocyte count, abnormal total polymorphonuclear neutrophil count, elevated immature PMN count (>600), elevated immature to total PMN (I:T) ratio (>0.12),elevated immature to mature

PMN (I:M) ratio(>0.3), Platelet count (<150,000/cu.mm) degenerative changes like toxic granulations, vacuolations, Dohle bodies were recorded. Abnormal total PMN count was given a score of 2 if there was no mature neutrophils in order to compensate for the low I: M ratio. The reference values of the haematological parameters of Manisha Makkar et al were used as the standard values. (Table-1).

Immature neutrophils include promyelocytes, myelocytes, metamyelocytes and band forms. Absolute polymorphonuclear neutrophil count, immature to total PMN (I:T) ratio and immature to mature PMN (I:M) ratio were calculated from the observed values.

Score of ≤ 2: sepsis unlikely

Score 3-4: sepsis possible

Score ≥ 5: sepsis very likely

Minimum score- 0

Maximum score- 8

The scores were compared with clinical parameters and the most predictive factor was identified.

Table-1: Hematological scoring system

PARAMETERS ABNORMALITY SCORE

Total WBC Count < 5000µl 1

≥ 25000 at birth 1

≥ 30000 at 12-24hrs

≥ 21000 day 2 onwards

Total PMN count 1800-5400 0

No mature PMN 2

Increased or decreased 1

count

Immature PMN count 600 0

Increased 1

I:T PMN ratio 0.12 0

Increased 1

I:M PMN ratio ≤ 0.3 0

≥ 0.3 1

Degenerative changes in Toxic granules or 1

PMN cytoplasmic vacuolations

Platelet count ≤ 1.5 lakhs/µl 1

WBC- white blood cell count; PMN- polymorphonuclear neutrophils; I:T PMN ratio- Immature:Total ratio; I:M PMN ratio- Immature : Mature ratio

COLOUR PLATES

COLOUR PLATE 1: Leishman stain, Neutrophilic Leukocytosis, 40X

COLOUR PLATE:2 Leishman stain: Mature neutrophils and Immature forms,40X

COLOUR PLATE 3: Leishman stain, Immature forms, 100X

COLOUR PLATE 4: Neutrophil showing cytoplasmic vacuolations, 100X

COLOUR PLATE: 5 Leishman stain: Toxic granules in neutrophils, 100X

STATISTICAL ANALYSIS

The data are reported as the mean +/- SD or the median, depending on their distribution. Frequencies are expressed in percentages. The differences in quantitative variables between groups were assessed by means of the unpaired t test. ANOVA was used to assess the quantative variables. Sensitivity and

Specficity test was performed. The chi square test was used assess differences in categoric variables between groups. A p value of <0.05 using a two-tailed test was taken as being of significance for all statistical tests. All data were analysed with a statistical software package. (SPSS, version 16.0 for windows).

RESULTS

This cross sectional study included total of 103 neonates. Based on the clinical scoring system, neonates were classified into groups, namely, sepsis, probable infection and normal infants (Table-2).

Table 2: Clinical group distribution.

Groups Number of cases (%)

Group-1 (sepsis) 36 (34%)

Group-2 (probable infection) 15 (14%)

Group-3 (normal infants) 53 (52%)

The diagnosis of sepsis was made when the blood culture was positive.

Neonates were classified as having probable infection when there was strong clinical history or presence of 2 risk factors for infection and when the blood culture was negative. Neonates were taken as normal when there was no clinical history or risk factors for infection and negative blood culture.

Table 3: Age and Sex distribution

95% CI for

Mean

Mean SD Lower Upper Minimum Maximum p value

Male 3.9 3.1 3.1 4.8 1 13

Female 3.8 4 2.7 5.0 1 19 >0.05

Total 3.9 4 3.2 4.6 1 19

Mean age of male and female patients were 3.9 and 3.8 days respectively

(Table-3).

Table-4: Age distribution.

Sepsis – Hematological score

No Very likely Possible Sepsis[n=40] p value [n=35] [n=28]

Age

[Days] n (%) n (%) n (%)

1 14 40% 12 43% 11 28%

2 2 6% 3 11% 4 10% >0.05

3 8 23% 3 11% 8 20%

4 2 6% 3 11% 2 5%

5 1 3% 0 0% 6 15%

>5 8 23% 7 25% 9 23%

Among the study population majority of the neonates were within 24 hours of birth (Table-4).

Figure. 1: Gender distribution

Gender Distribution[N=103]

Female 49% Male 51%

Both male and female patients were almost equally distributed in the study population (Fig.1).

Figure: 2. Distribution of neonates according to haematological score.

Sepsis-Hematological [N=103]

No Sepsis n=40 (38)%

Very likely n=35 (34%)

Possible n=29 (28%)

38%, 28% and 34% of cases were in no sepsis, possible sepsis and very likely sepsis group according to haematological scoring system respectively.

(Figure-2) According to haematological scoring system, 86% of the patients in very likely sepsis group had positive blood culture. None of the no sepsis group, according to the haematological scoring system had positive blood culture. Only 6% of patients in very likely sepsis group had probable infection.

But up to 50% of the patients in clinically probable infection group had blood culture positive (not shown in the Table-5). Only 5% of patients in very likely sepsis group had normal clinical score.

Table.5: Distribution of clinical and haematological sepsis groups

Hematological score Clinical score Very likely (%) Possible sepsis No sepsis (%) (score: > 5) (%) (score: 3-4) (score:0-2) Blood culture 31 (86%) 5 (13%) 0 (0%) +ve(n=36) Probable infection 1 (6%) 6 (40%) 8 (53%) (n=15)

Normal (n=53) 3 (5%) 18 (33%) 32 (60%)

Majority of neonates (71%) in the very likely and possible sepsis group were preterm. Low birth weight neonates were predominantly in very likely and possible sepsis group, 77% and 71% respectively. Most neonates who require resuscitation perinatally were in very likely and possible sepsis group, 86% and

57% respectively. Up to 30%of the neonates in very likely sepsis group had meconium stained amniotic fluid.

Table-6: Association of clinical variables with haematological scoring system.

Sepsis – Hematological score Very likely Possible No p value [n=35] [n=28] Sepsis[n=40] Age n % n % n % 1 - 4 26 74% 21 75% 25 63% 5 - 8 3 9% 3 11% 12 30% >0.05 9 - 12 5 14% 2 7% 3 8% 13 - 16 0 0% 2 7% 0 0% 17 - 20 1 3% 0 0% 0 0% Gender Male 25 71% 16 57% 17 43% Female 10 29% 12 43% 23 58% >0.05 Gestational age Pre Term 25 71% 20 71% 11 28% <0.001 Term 10 29% 8 29% 29 73% Birth weight <2.5 kg 27 77% 20 71% 12 30% <0.01 >2.5 kg 8 23% 8 29% 28 70% PROM Yes 2 6% 1 4% 0 0% No 33 94% 27 96% 40 100% >0.05 Resuscitation need Yes 30 86% 16 57% 30 75% No 5 14% 12 43% 10 25% <0.05 Meconium stained amniotic fluid Yes 10 29% 5 18% 2 5% No 25 71% 23 82% 38 95% <0.05 Prolonged labour(>24hrs) Yes 1 3% 0 0% 0 0% No 34 97% 28 100% 40 100% >0.05 Prematurity Yes 9 26% 9 32% 9 23% No 26 74% 19 68% 31 78% >0.05 Activity Yes 9 26% 15 54% 16 40% No 26 74% 13 46% 24 60% >0.05 Culture Positive 31 89% 5 18% 0 0% No Growth 4 11% 23 82% 40 100% <0.001

89% of the neonates in very likely sepsis group had blood culture positive and none of the neonates in no sepsis group had positive blood culture.

Premature rupture of membrane, prolonged labour, prematurity and activity of the neonate were not the predictors of neonatal sepsis. Gestational age, birth weight, requirement of resuscitation perinatally and meconium stained amniotic fluid were significant predictors of neonatal sepsis. Prematurity and perinatal asphyxia which are sepsis screen parameters for at risk of sepsis are also found to significantly correlate with haematological scoring system.(Table-6)

Table-7: Total WBC Count as predictor of sepsis

Sepsis - Hematological

Very No WBC (ul) Possible Total % likely Sepsis

<5000 2 1 1 4 4%

5000 - 21 24 39 84 82% 21000

>21000 12 3 0 15 15%

Total 35 28 40 103

Figure 3: Total WBC Count as predictor of sepsis

Total WBC Count as predictor of sepsis [N=103][p<0.01] 120% 100% 80% 60% 40% 20% 0% Very likely Possible No Sepsis <5000 6% 4% 3% 5000 - 21000 60% 86% 98% >21000 34% 11% 0%

Only 4% of neonates had leucopenia (<5000 cells / µl). Among them 2% of neonates comes under very likely sepsis group.

15% of neonates had leucocytosis (>21,000 cells /µl)

12% of the neonates in very likely sepsis group and none of the case in No sepsis group had leucocytosis.

Total WBC Count is considered as significant predictor of sepsis.

Figure-4: Total PMN count as predictor of sepsis

Total PMN count as predictor of sepsis [N=103][p<0.001] 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% Very likely Possible No Sepsis < 1800 0% 7% 3% 1800 - 5400 11% 36% 68% >5400 89% 57% 30%

89% of the neonates in very likely sepsis group had high total PMN count

(>5400) and only 30% of the patients in no sepsis group had high total PMN count. Only very few neonates had low total PMN count. High total PMN count was significant predictor of neonatal sepsis (p<0.001). (Figure-3).

Figure-5: Immature PMN count as a predictor of sepsis

Immature PMN count as a predictor of sepsis [N=103][p<0.001] 120%

100%

80%

60%

40%

20%

0% Very likely Possible No Sepsis <600 0% 18% 90% >600 100% 82% 10%

All the neonates in the very likely sepsis group and 82% of the neonates in the possible sepsis group had high immature PMN count (more than 600). 90% of the neonates in no sepsis group had low immature PMN count (less than 600).

Immature PMN count is significant predictor of the neonatal sepsis (p<0.001)

(Figure-4).

Figure-6: I:T PMN ratio as a predictor of neonatal sepsis.

Association of IT PMN ratio with Sepsis [N=103][p<0.001] 120%

100%

80%

60%

40%

20%

0% Very likely Possible No Sepsis <0.12 3% 25% 98% >0.12 97% 75% 3%

97% of the patients in very likely sepsis group and 75% of the neonates in possible sepsis group had higher I:T PMN ratio (ie, >0.12). 98% of the neonates in no sepsis group had low I:T PMN ratio (<0.12). Higher I:T PMN ratio had more likely chance of sepsis (p<0.001) (Figure-5).

Figure-7: I:M PMN ratio as a predictor of neonatal sepsis.

Association of IM PMN ratio with Sepsis [N=103][p<0.001] 120%

100%

80%

60%

40%

20%

0% Very likely Possible No Sepsis <0.3 3% 54% 88% >0.3 97% 46% 13%

97% of the patients in very likely sepsis group and 46% of the neonates in possible sepsis group had higher I:M PMN ratio (ie, >0.3). 88% of the neonates in no sepsis group had low I:M PMN ratio (<0.3). Higher I:M PMN ratio had more likely chance of sepsis (p<0.001). (Figure-6)

Figure-8: Degenerative changes as a predictor of sepsis.

Association of Degenerative changes in PMN ratio with Sepsis [N=103][p<0.001] 120%

100%

80%

60%

40%

20%

0% Very likely Possible No Sepsis Yes 60% 11% 0% No 40% 89% 100%

60% of the patients in very likely sepsis group and 11% of the neonates in possible sepsis group had degenerative changes in PMN like toxic granules and vacuolations. 89% of possible sepsis group and all the neonates in no sepsis group had no toxic changes in PMN. Degenerative changes in the PMN is a significant predictor of sepsis (p<0.001). (Figure-7)

Figure-9: Platelet count as a predictor of sepsis

Association of Platelet count with Sepsis [ N=103][p>0.05] 80%

70%

60%

50%

40%

30%

20%

10%

0% Very likely Possible No Sepsis <1.5 lakhs 57% 39% 30% >1.5 lakhs 43% 61% 70%

57% of patients in very likely sepsis group had thrombocytopenia and 43% of the patients in very likely sepsis group had normal platelet count. 70% and 30% of neonates in the no sepsis group had normal platelet count and thrombocytopenia respectively. Though this difference numerically appears significant, statistically it is insignificant (p>0.05), hence platelet count is not a significant predictor of sepsis (Figure-8).

Highest sensitivity is seen with immature PMN count, IT PMN ratio and

IM PMN ratio. Total WBC count has lowest sensitivity but high specificity.

Platelet count and total PMN count have lower sensitivity, specificity, negative

predictive value and positive predictive value. Degenerative changes in PMN

and IT PMN ratio have the highest specificity.

Table-8: Performance of individual haematological parameters

Sensitivity Specificity Positve Predictive value Negative Predictive value

S P S+P S P S+P S P S+P S P S+P

Total WBC (µl) 40.00 14.29 28.57 97.50 97.50 97.50 93.33 80.00 94.74 65.00 61.90 46.43

Total PMN count 88.57 64.29 77.78 67.50 67.50 67.50 70.45 58.06 79.03 87.10 72.97 65.85

Immature PMN count 100.00 82.14 92.06 90.00 90.00 90.00 89.74 85.19 93.55 100.00 87.80 87.80

IT PMN ratio 97.14 75.00 87.30 97.50 97.50 97.50 97.14 95.45 98.21 97.50 84.78 82.98

IM PMN ratio 97.14 46.43 74.60 87.50 87.50 87.50 87.18 72.22 90.38 97.22 70.00 68.63

Degenerative 60.00 10.71 38.10 100.00 100.00 100.00 100.00 100.00 100.00 74.07 61.54 50.63 changes inPMN Platelet count 57.14 39.29 49.21 70.00 70.00 70.00 62.50 46.67 46.67 46.67 46.67 46.67

S - Sepsis group; P- Possible sepsis

IT PMN ratio, immature PMN count and degenerative changes have high

positive predictive value. IT PMN has the good sensitivity and excellent

specificity and positive predictive value. Degenerative changes have poor

sensitivity but good specificity and positive predictive value. Immature PMN

and I:T PMN ratio and I:M PMN ratio had better negative predictive value than

other parameters.

C-reactive protein:

Among the 103 neonates 40% had CRP positive. (Figure-9)

Figure-10: Prevalence of CRP positivity in the study population.

CRP [N=103]

Positive 40%

Negative 60%

Figure -11: Distribution of CRP positivity according to the hematological

scoring system:

Association of CRP with Sepsis [ N=103][p<0.001] 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Very likely Possible No Sepsis Positive 77% 32% 13% Negative 23% 68% 88%

77% of neonates in very likely sepsis group had elevated CRP and only 13% of no sepsis group had elevated CRP. 88% of the neonates in the no sepsis group had normal CRP and only 23% of sepsis group had normal CRP. This difference was statistically significant.

DISCUSSION

Sepsis neonatorum, neonatal septicaemia and Neonatal sepsis are the terms used to describe systemic response in neonates to infective focus.

Neonatal sepsis causes significant morbidity and mortality. Morbidity can be immediate or delayed sequelae which is devastating. Neonates are more susceptible to the infection because of the immature development of the immune system. Because of the subtle signs of sepsis in neonates, early diagnosis and prompt starting of antibiotics are of paramount importance. (85)

Blood culture is the gold standard to diagnose the sepsis. But there is a significant delay in getting the final report and sensitivity of the organisms to various antibiotics. To overcome this problem there are various clinical and haematological parameters suggested to predict the neonatal sepsis in advance.

(6)

In our study, sensitivity, specificity, and positive predictive value were significantly more for degenerative changes in the PMN and IT PMN ratio.

These findings were similar when compared to available literature. (3, 86, 87, 88)

There is greater chance of sepsis noted in patients who have higher haematological score. With the haematological score of less than 2, the sepsis is least likely.

In our study, total WBC Count had high specificity and positive predictive value.

In our study total PMN count had high sensitivity and Negative predictive value and this finding was similar to Akenzua et al and Manisha et al. (89, 4)

In our study total platelet count was not a good predictor of sepsis which is in contrast to the study by Speer et al. (3) Elevated I:M PMN ratio had good sensitivity in identifying sepsis which is similar to the study done by Philip et al and Basu et al. (87, 88)

Immature PMN count is an excellent predictor of sepsis which correlates with study done by Gosh et al and Narasima et al. (5, 6)

C- reactive protein is considered as better predictor of neonatal sepsis which correlates with study done byGanesan P et al.

Hematological scoring system improves the accuracy of diagnosis of sepsis. So, this can be used as a screening test in diagnosing sepsis. But it is of paramount importance to standardize the procedure and interpretation of the results by specific protocol.(86)

There are several methods for the rapid detection of microorganisms in blood culture like, automated culture system, Fluorometric detection system and

DNA probe assay. But still haematological scoring system can be used as a reliable parameter in predicting the neonates with sepsis

CONCLUSION

Hematological scoring system is a quick, simple, reliable and cost effective tool for the early diagnosis of neonatal sepsis. This also helps clinicians to predict the neonatal sepsis early and start the appropriate antibiotic therapy to prevent sepsis related events. This also helps to avoid unnecessary institution of antibiotics and development of resistance.

Immature: Mature polymorphonuclear neutrophil ratio(I:M ratio),

Immature: total polymorphonuclear neutrophil ratio (I:T ratio), degenerative changes in neutrophils can be viewed as regular parameter which can predict the possibility of neonatal sepsis.

BIBLIOGRAPHY

1. Bang AT, Bang RA, Bactule SB, Reddy HM, Deshmukh MD. Effect of home-based neonatal care and management of sepsis on neonatal mortality: field trial in rural India. Lancet 1999;354:1955-61

2. Stoll BJ. The global impact of neonatal infection. Clin Perinatol 1997;24:1-21

3. Speer CP, Ghar M, Schrotter W. Eary diagnosis of neonatal infection. Monatsschr Kinderheilkd. 1985; 133 (9):665-668.

4. Manisha M, Chinki Gupta,Mahajan NC. Performance Evaluation of haematological scoring system in early diagnosis of neonatal sepsis. J Clin Neonatol 2013 2(1): 25-29.

5. Narasima and Harendra kumar. Significance of Hematological scoring systemin early diagnosis of Neonatal sepsis. Indian J HematolBlood Trans 2011; 27:14-7.

6. Ghosh S, Mittal M, Jeganathan G. Early diagnosis of neonatal sepsis using a haematological scoring system. Indian J Med Sci 2001; 55(9):495-500.

7. Sharma A, Kutty C, Saharwal U, Mohan. Evaluation of sepsis screen for diagnosis of neonatal septicaemia. Indian J Pediatr. 1993; 60:559-63.

8. Mishra PK, Kumar R, Mallick K, Mehra P. Simple hematologicl tests for diagnosis of neonatal sepsis. Indian Pediatric. 1989; 26:156-60.

9. Baltimore RS, Huie SM, Meek J, Schuchat A, O’brien KL. Early onset neonatal sepsis in the era of group B streptococcal prevention. Pediatrics. 2001; 108:1094-8.

10. Gladstone IM, Ehrenkranz RA, Edberg SC, Baltimore RS. A 10 year review of neonatal sepsis in comparison with the previous 50 year experience. Pediatr Infect Dis J. 1990; 9:819-25.

11. Varsha, Rusia U, Sikka M, Faridi MM, Madan N. Validity of haematological parameters in identification of early and late onset neonatal infection. Indian J Pathol Microbiol. 2003; 46:565-8. 12. Singh M, Narang A, Bhakoo ON. Predictive perinatal score in the diagnosis of neonatal sepsis. J Trop Pediatr. 1994 Dec;40(6):365-8

13. Takkar VP, Bhakoo ON, Narang A. Scoring system for the prediction of early neonatal infections. Indian Pediatr. 1974;11:597-600

14. Baltimore RS. Neonatal nosocomial infections. Semin Perinatol 1998;22:25-32

15. Wolach B. Neonatal sepsis: pathogenesis and supportive therapy. Semin Perinatol1997;21:28-38

16. Gerdes JS, Polin R. Early diagnosis and treatment of neonatal sepsis. Indian J Pediatr 1998;65:63-78.

17. Wittekind D. On the nature of Romanowsky dyes and the Romanowsky Giemsa effect. Clin Lab Haematol. 1979;1:247–262.

18. Horobin RW, Walter KJ.Understanding Romanowsky staining. I. the Romanowsky-Giemsa effect in blood smears. Azure B-eosin as a substitute for May–Gru¨nwald– Giemsa and Jenner–Giemsa stains. Microsc Acta. 1987;79:153–156.

19. Wittekind DH, Kretschmer V, Sohmer I. Azure B-eosin Y stain as the standard Romanowsky- Giemsa stain. Br J Haematol. 1982;5:391–393.

20. International Committee for Standardization in Haematology. ICSH reference method for staining of blood and bone marrow films by azure B and eosin Y (Romanowsky stain). Br J Haematol. 1984; 57: 707–710.

21. Schenk EA, Willis CT. Note from the Biological Stain Commission: certification of Wright stain solution. Stain Technol. 1989;64 :152–153.

22. Wittekind DH. On the nature of Romanowsky-Giemsa staining and its significance for cytochemistry and histochemistry: an overall view. Histochem J.1983;15:1029–1047.

23. Krafts KP, Hempelmann E, Oleksym BJ. The Color purple: from Royalty to Laboratory. Biotec Histochem 2011. Feb; 86(1):7-35.

24. Plehn F. Zur Aetiologie der Malaria. Berl. Klin. Wochenschr (1890b). 27:292-294. 25. Coulter WH. High speed automatic blood cell counter and cell size analyser. Proceedings of National Electronics Conference. 1956;12: 1034–1040.

26. England JM, van Assendelft OW. 1986: Automated blood counters and their evaluation. In: Rowan RM, England JM, eds. Automation and Quality Assurance in Haematology. Oxford: Blackwell Scientific.

27. Thom R. Automated red cell analysis. Baillieres Clin Haematol. 1990; 3: 837–850.

28. International Council for Standardization in Haematology. Guidelines for the evaluation of blood cell analysers including those used for differential leucocyte and reticulocyte counting and cell marker applications. Clin Lab Haematol. 1994;16: 157–174.

29. Mohandas N, Kim YR, Tycko DH, et al. Accurate and independent measurement of volume and hemoglobin concentration of individual red cells by laser scattering. Blood. 1986;68:506–513.

30. Tycko DH, Metz MH, Epstein EA, et al. Flow-cytometric light scattering measurement of red blood cell volume and haemoglobin concentration. Appl Opt. 1985;24:1355–1366.

31. Bain BJ. An assessment of the three population differential count on the Coulter Model S Plus IV. Clin Lab Haematol. 1986;8:347–359.

32. Briggs C, Kunka S, Pennaneach C, et al. Performance evaluation of a new compact haematology analyser, the Sysmex pocH-100i. Lab Hematol. 2003;9:225–233.

33. Bentley SA. Automated differential white cell counts: a critical appraisal. Baillieres Clin Haematol. 1990;3:851–869.

34. Miguel A, Orero M, Simon R, et al. Automated neutrophil morphology and its utility in the assessment of neutrophil dysplasia. Lab Haematol. 2007;13:98–102.

35. Ansari-Lari MA, Kickler TS, Borowitz MJ. Immature granulocyte measurement using the Sysmex XE- 2100. Am J Clin Pathol. 2003;120:795–799. 36. KiyoymaT, Tokuda Y, Shiki S. Isopropyl alcohol compared with isopropyl alcohol plus povidone iodine as skin preparation for prevention of blood culture contamination. J Clin Microbiol. 47(1): 54-58.

37. Lee A, Mirrett S, Reller LB, Weinstein MP. Detection of blood stream infection in adults: How many blood cultures are needed? J Clin Microbiol 45 (11): 3546-8.

38. Report of the National Neonatal Perinatal Database (National Neonatology Forum) 2002-03.

39. Harald Kluge. Atlas of CSF cytology.(2007) Thieme. Pp 45-46 (ISBN 978-3-13-143161-5)

40. Pearson, Justin, Fuller, Geraint. Lumbar punctures and CSF analysis. Medicine. 40(8): 459-462.

41. Nigrovic LE, Chiang VW. Cost Analysis of enteroviral polymerase chain reaction in infants with fever and CSF pleocytosis. Arch of Pediatrics and Adolescent Medicine. 154(8): 817-821.

42. Rand, Kenneth; Houck, Herbert Lawrence, Robert. Real time PCR detection of Herpes simplex virus in CSF and cost savings from earlier hospital discharge. The Journal of Molecular Diagnostics: JMD.7 (4): 511-516.

43. Hedges JC, Singer CA, Gerphoffer WT. Mitogen activated protein Kinases regulate cytokine gene expression in human airway myocytes. Am. J. Respir. Cell Mol. Boil. 23(1):86-94.

44. Haeada A, Sekido N, Akahoshi T, Wada T, Mukaida. Essential involvement of interlukin 8 in acute inflammation. Journal of Leukocyte biology 56(5): 559-64.

45. Peppys MB, Hirsschfield GM. C-reactive protein; A critical Update. The Journal of clinical investibgation. 111(12); 1805-12.

46. Christ-Crain M, Jaccard D, Bingisser R. Effect of procalcitonin guided treatment on antibiotic use and outcome in lower respiratory tract infection. Lancet: 363(9409): 600-607. 47. Locksley RM, Killeen N, Lenardo MJ (2001). "The TNF and TNF receptor superfamilies: integrating mammalian biology". Cell. 104 (4): 487–501.

48. Solovjov DA, Pluskota E, Plow EF (January 2005). "Distinct roles for the alpha and beta subunits in the functions of integrin alphaMbeta2". The Journal of Biological Chemistry. 280 (2): 1336–45

49. Hulett M, Hogarth P (1998). "The second and third extracellular domains of FcgammaRI (CD64) confer the unique high affinity binding of IgG2a". Mol Immunol. 35 (14-15): 989–96.

50. Ord DC, Ernst TJ, Zhou LJ, et al. (1990). "Structure of the gene encoding the human leukocyte adhesion molecule-1 (TQ1, Leu-8) of lymphocytes and neutrophils". J. Biol. Chem.265 (14): 7760–7.

51. Jacob B Seidelin, Ole HN, Jenes strom, Soluble L-selectin levels predicts survival. Inten Care Medicine (2002) 28:1613-18.)

52. Parson, Melissa, Cytokine Storm in the Pediatric Oncology Patient (section "Differential Diagnoses and Workup", Journal of Pediatric Oncology Nursing, 27(5) Aug/Sep 2010, 253–258.

53. Goldstein B, Giroir B, Randolph A (2005). "International pediatric sepsis consensus conference: definitions for sepsis and organ dysfunction in pediatrics". Pediatric Critical Care Medicine.6 (1): 2–8.

54. Rippe, James M.; Irwin, Richard S.; Cerra, Frank B (1999). Irwin and Rippe's intensive care medicine. Philadelphia: Lippincott-Raven.

55. Marino, Paul L. (1998). The ICU book. Baltimore: Williams & Wilkins.

56. Ellizebet B, Desanka D, Renato G. Multiple Organ Failure in sepsis. The Brazilian Journal of Infectious Diseases 2001;5(3):103-110.

57. Palis J, Segel GB. Developmental biology of erythropoiesis. Blood Rev. 1998; 12:106–114.

58. Hermansen MC Nucleated red blood cells in the fetus and newborn. Arch Dis Child Fetal Neonatal Ed.2001;84:F211–F215. 59. Manroe BL, Weinberg AG, Rosenfeld CR et al The neonatal blood count in health and disease, I: reference values for neutrophilic cells. J Pediatr. 1979;95:89–98.

60. Christensen RD ed. Hematologic Problems of the Neonate. Philadelphia, PA: Saunders; 2000.

61. Ozbek N, Gürakan B, Kayiran SM. Complete blood cell counts in capillary and venous blood of healthy term newborns. Acta Haematol. 2000; 103:226–228.

62. Salsbury DC. Anemia of prematurity. Neonatal Netw. 2001;20:13– 20.Coulombel L, Dehan M, Tchernia G et al. The number of polymorphonuclear leukocytes in relation to gestational age in the newborn. Acta Paediatr Scand. 1979;68:709–711.

63. Mouzinho A, Rosenfeld CR, Sanchez PJ, et al. Revised reference ranges for circulating neutrophils in very-low-birth-weight neonates. Pediatrics. 1994;94:76–82.

64. Coffin CM, Hamilton MS, Pysher TJ, et al. Pediatric laboratory medicine: current challenges and future opportunities. Am J Clin Pathol. 2002;117:683–690.

65. Maria A. Proytcheva, MD . Issues in Neonatal Cellular Analysis. Am J Clin Pathol 2009;131:560-573.

66. Ernst DJ, Ballance LO, Becan-McBride KE, et al. Procedures and Devices for the Collection of Diagnostic Capillary Blood Specimens; Approved Standard. 5th ed. Vol 24. Wayne, PA: NCCLS; 2004

67. Paes B, Janes M, Vegh P, et al. A comparative study of heelstick devices for infant blood collection. Am J Dis Child. 1993;147:346-348.

68. Janes M, Pinelli J, Landry S, et al. Comparison of capillary blood sampling using an automated incision device with and without warming the heel. J Perinatol. 2002;22:154-158.

69. Kayiran SM, Ozbek N, Turan M, et al. Significant differences between capillary and venous complete blood counts in the neonatal period. Clin Lab Haematol. 2003;25:9-16. 70. Thurlbeck SM, McIntosh N. Preterm blood counts vary with sampling site. Arch Dis Child. 1987;62:74-75.

71. Bourner G, Dhaliwal J, Sumner J. Performance evaluation of the latest fully automated hematology analyzers in a large, commercial laboratory setting: a 4-way, side-by-side study. Lab Hematol. 2005;11:285-297.

72. Soldin SJ, Brugnara C, Wong EC, eds. Pediatric Reference Intervals. 5th ed. Washington, DC: AACC Press; 2005.

73. Alur P, Devapatla SS, Super DM, et al. Impact of race and gestational age on red blood cell indices in very low birth weight infants. Pediatrics. 2000;106:306-310.

74. Wiedmeier SE, Henry E, Sola-Visner MC, Christensen RD. Platelet reference ranges for neonates, defined using data from over 47,000 patients in a multihospital healthcare system. J Perinatol. 2009;29(2):130- 136.

75. Martha Sola-Visner. Platelets in the neonatal period: developmental differences in platelet production, function, and hemostasis and the potential impact of therapies. ASH Education Book December 8, 2012vol. 2012 no. 1 506-511.

76. Sola-Visner MC, Christensen RD, Hutson AD, Rimsza LM. Megakaryocyte size and concentration in the bone marrow of thrombocytopenic and nonthrombocytopenic neonates. Pediatr Res. 2007;61(4):479-484.

77. Stanworth SJ, Clarke P, Watts T, et al. Prospective, observational study of outcomes in neonates with severe thrombocytopenia. Pediatrics. 2009;124(5):e826-834.

78. Sitaru AG, Holzhauer S, Speer CP, et al. Neonatal platelets from cord blood and peripheral blood. Platelets. 2005;16(3-4): 203-210.

79. Andrew M, Paes B, Johnston M. Development of the hemostatic system in the neonate and young infant. Am J Pediatr Hematol Oncol. 1990;12(1):95-104. 80. Linder N, Shenkman B, Levin E, et al. Deposition of whole blood platelets on extracellular matrix under flow conditions in preterm infants. Arch Dis Child Fetal Neonatal Ed. 2002;86(2): F127-130.

81. Saxonhouse MA, Garner R, Mammel L, et al. Closure times measured by the platelet function analyzer PFA-100 are longer in neonatal blood compared to cord blood samples. Neonatology. 2010;97(3):242-249.

82. Strauss T, Maayan-Metzger A, Simchen MJ, et al. Impaired platelet function in neonates born to mothers with diabetes or hypertension during pregnancy. Klin Padiatr. 2010;222(3):154- 157.

83. Gombos MM1, Bienkowski RS, Gochman RF, Billett HH. The absolute neutrophil count: is it the best indicator for occult bacteremia in infants? Am J Clin Pathol. 1998 Feb;109(2):221-5.

84. Al-Gwaiz LA1, Babay HH.The diagnostic value of absolute neutrophil count, band count and morphologic changes of neutrophils in predicting bacterial infections. Med Princ Pract. 2007;16(5):344-7.

85. Neonatal bacteraemia diagnosis and management (editorial) (1997) Br Med J 2:1385-1386.

86. Rodwel RL, Leslie AL, Tudehope DI. Early diagnosis of neonatal sepsis using a haematological scoring system. J. Pediatr 1988; 112: 761-767.

87. PhilipAGS, Hewitt JR. Early diagnosis of neonatal sepsis Pediatrics. 1980; 65:1036-1041

88. Basu S, Guruprasad, Narang A, Garewal G. Diagnosis of sepsis in the high risk neonate using a haematological scoring system. Indian J Hemato Blood Transfusion. 1999; 17:32-34.

89. Akenzua GI, Hui YT, MilerR. Neutrophil and band counts in the diagnosis of neonatal infection. Pediattrics 1974; 54:38-42.

ANNEXURES

PROFORMA

Name

Age

Sex

Ward

Ip.No.

Gestational age

Birth weight

PROM

Maternal fever

Resuscitation need

Meconium stained amniotic fluid

Prolonged labour

Foul smelling liquor

Mode of delivery

Prematurity Temperature (Hypothermia / Hyperthermia)

Activity

Rashes

Bleeding

Sclerema

LIST OF ABBREVIATIONS

RBC RED BLOOD CELL

WBC WHITE BLOOD CELL

PCV PACKED CELL VOLUME

MCV MEAN CORPUSCULAR VOLUME

MCH MEAN CORPUSCULAR HAEMOGLOBIN

MEAN CORPUSCULAR HAEMOGLOBIN MCHC CONCENTRATION

CHCM CELLULAR HAEMOGLOBIN CONCENTRATION MEAN

CSF CEREBROSPINAL FLUID

CRP C- REACTIVE PROTEIN

SIRS SYSTEMIC INFLAMMATORY RESPONSE SYNDROME

DIC DISSEMINATED INTRAVASCULAR COAGULATION

NF-Kb NUCLEAR FACTOR KAPPA b

SGA SMALL FOR GESTATIONAL AGE

AGA APPROPRIATE FOR GESTATIONAL AGE

VwF VON WILLEBRAND FACTOR

IVH INTRAVASCULAR HAEMORRHAGE

MASTER CHART

Prolonged Foul Total Total Immature I M Age Gestational Birth Maternal Resuscitation Blood I T PMN Degenerative Platelet Name Sex PROM MSAF labour smelling Prematurity Temperature Activity WBC PMN PMN PMN Score sepsis CRP (DOL) age weight fever need Culture ratio changes inPMN count (>24hrs) liquor count count count ratio Devi 10days mch term 2.2kg no no yes yes no no no no poor s.aureus 5700 1425 40 0.05 0.19 nil 82000 3 possible positive Keerthika 10days fch preterm 1.48kg No No No No No No Yes No Normal no growth 12000 2400 61 0.05 0.2 nil 61000 3 possible negative Leelavathy 10days mch term 2.8kg No No Yes No No No No No Normal nogrowth 9500 5225 274 0.4 0.8 nil 90000 4 possible negative Poongodi 12days fch preterm 1.4kg no no yes no no no yes no poor E.coli 16100 8694 120 0.17 2.5 nil 43000 4 possible negative Ramathal 12days fch preterm 1.8kg No No Yes No No No No No Normal cnstaph 23800 16898 145 0.11 0.26 vacuoles 326000 3 possible negative Sivaranjani 13days mch term 2.8kg no no no no no no no no normal nogrowth 7000 2450 147 0.17 0.5 vacuoles 99000 4 possible positive Mythili 19days fch preterm 2.3kg No No Yes yes No No No No Poor klebsiella 12800 10624 1792 0.4 1.4 nil 315000 3 possible negative Anisha 1day fch term 3kg No No No Yes No No No No Normal nogrowth 7900 3397 126 0.18 0.4 nil 211000 3 possible positive Rishwana 1day mch preterm 1.9kg No No Yes No No No No No Poor nogrowth 9300 5580 450 0.15 0.6 nil 80000 3 possible negative Sathya 1day mch preterm 1.3kg no no no yes no no yes no normal klebsiella 9700 6111 914 0.16 0.4 nil 230000 4 possible positive Sudhapriya 1day fch term 2.7kg No No Yes No No No No No poor nogrowth 15000 7050 181 0.15 0.4 nil 190000 4 possible positive Shanthi 1day mch preterm 1.3kg no no yes no no no yes no poor klebsiella 14600 7446 144 0.15 0.3 nil 270000 3 possible positive selvaraj Mallika 1day fch term 2.8kg No No Yes No No No No No poor nogrowth 20000 13400 1550 0.2 0.2 nil 126000 3 possible negative Rajeshwari 1day mch term 2.8kg No No No No No No No No Poor no growth 22400 16800 261 0.13 0.3 nil 135000 4 possible negative Pavithra 1day fch term 2.7kg No No Yes No No No No No Poor no growth 29000 23200 855 0.14 0.4 nil 170000 4 possible negative Nandhini 2day mch term 2.3kg No No No No No No Yes No Poor klebsiella 5043 2622 401 0.2 0.2 nil 284000 3 possible negative Vijaya 2days mch preterm 1.4kg No No No No No No Yes No Poor no growth 3700 1887 874 0.2 0.2 nil 320000 3 possible negative Anitha 2days mch preterm 1.5kg No No Yes No No No Yes No Poor cnstaph 7100 3976 776 0.09 0.1 vacuoles 188000 4 possible positive Rubina 2days mch term 2.7kg No No Yes No No No No No poor nogrowth 8800 4488 241 0.11 0.26 nil 101000 3 possible positive Rajbanisha 3days fch term 3.1kg no no yes no no no no No Normal nogrowth 8000 2400 1315 0.13 0.35 nil 122000 4 possible negative Malathi 3days mch term 3kg No No No Yes Yes No No No Poor klebsiella 9400 4888 2447 0.2 0.3 nil 350000 3 possible negative Paronika 3days mch term 3kg no no yes no No No No No poor klebsiella 10700 5885 3420 0.09 0.1 nil 267000 3 possible negative Malathi 3days mch term 2.8kg No No Yes yes No No No No Poor klebsiella 23000 17250 510 0.14 0.41 nil 267000 4 possible negative Maheswari 5days mch preterm 1.4kg no no yes no no no yes no poor nogrowth 7700 3850 938 0.64 1.8 nil 74000 4 possible positive Hemalatha 6days mch term 3kg No No No Yes no No No No Normal no growth 11047 3755 7604 0.15 0.17 nil 349000 3 possible negative Valarmathi 6days fch term 2.6kg No No Yes No No No No No poor nogrowth 10300 5459 7417 0.03 0.21 nil 165000 3 possible positive Vennila 8days fch term 2.9kg No No Yes No No No No No poor nogrowth 8600 3698 10982 0.19 0.2 nil 203000 3 possible negative Lalithadevi 3days mch preterm 1.3kg no no yes yes no no yes no poor klebsiella 22000 21560 6362 0.29 0.4 nil 247000 3 possible negative uma 10days mch preterm 800g no no yes no no no yes no poor klebsiella 4000 6400 67 0.1 0.16 nil 340000 0 unlikely positive Sajnaa 11days fch preterm 1.5kg No No No yes No No No No Normal klebsiella 15000 10950 72 0.13 0.16 nil 275000 1 unlikely negative Bajavathi 1day mch preterm 1.4kg No No Yes No No No Yes No Poor klebsiella 20000 2800 255 0.06 0.2 nil 45000 1 unlikely negative Pandiyammal 1day fch preterm 2.6kg No No No No No No No No Normal nogrowth 8500 3400 200 0.03 0.11 nil 120000 1 unlikely negative Radhamani 1day mch term 2.9kg No No Yes No No No No No poor nogrowth 7900 3634 305 0.07 0.26 nil 130000 1 unlikely negative Megala 1day mch term 2.6kg No No Yes No No No No No poor nogrowth 9700 3686 510 0.12 0.3 nil 251000 0 unlikely positive Kalaiselvi 1day fch preterm 750g No No Yes No No No No No poor klebsiella 6000 3900 172 0.02 0.06 nil 155000 0 unlikely positive Kanimozhi 1day fch term 3.3kg No No No No No No No No Normal nogrowth 12000 4560 213 0.08 0.26 nil 185000 0 unlikely negative Sumayabanu 1day fch term 3kg No No Yes No No No No No Normal nogrowth 15600 5148 109 0.03 0.1 nil 240000 0 unlikely negative Sudhapriya 1day mch term 1.6kg No No Yes Yes No No Yes no Poor no growth 15200 5168 147 0.08 0.34 nil 243000 1 unlikely negative Prolonged Foul Total Total Immature I M Age Gestational Birth Maternal Resuscitation Blood I T PMN Degenerative Platelet Name Sex PROM MSAF labour smelling Prematurity Temperature Activity WBC PMN PMN PMN Score sepsis CRP (DOL) age weight fever need Culture ratio changes inPMN count (>24hrs) liquor count count count ratio Lakshmi 1day fch preterm 1.5kg No No Yes No No No Yes No Poor klebsiella 7300 5475 258 0.07 0.08 nil 266000 0 unlikely negative Sangeetha 1day fch preterm 2.3kg No No Yes No No No No No Normal nogrowth 10300 5974 462 0.05 0.14 nil 70000 1 unlikely negative Rani 1day fch term 3.1kg No No No No No No No No Normal nogrowth 9600 6048 1170 0.06 0.19 nil 195000 0 unlikely negative Devanayaki 1day fch preterm 2.05kg Yes No Yes No No No No No Normal no growth 9607 6148 446 0.06 0.15 nil 253000 0 unlikely negative Nithya 1day fch term 2.5kg No No Yes No No No Yes No poor nogrowth 11700 6201 251 0.05 0.17 nil 250000 0 unlikely negative Charlin gupta 1day fch preterm 2.4kg No No No No No No No No Normal no growth 10800 6264 314 0.04 0.12 nil 112000 1 unlikely negative Sumathi 1day mch preterm 2.1kg No No Yes No No No No No Poor acinetobacter 9500 7125 136 0.05 0.15 nil 210000 0 unlikely negative Selvi 1day fch preterm 2.1kg No No Yes No No No No No Normal no growth 13100 7205 273 0.12 0.22 nil 225000 2 unlikely negative Shanthi selvaraj 1day mch preterm 4.2kg No No Yes No No No No No Normal no growth 13035 8081 605 0.04 0.09 nil 311000 0 unlikely negative Annakodi 1day mch preterm 1.8kg No No Yes No No No No No Normal klebsiella 14400 10080 918 0.03 0.06 nil 290000 1 unlikely negative Radhamani 1day fch preterm 1.7kg No No Yes No No No Yes No Poor klebsiella 18000 10440 411 0.03 0.11 nil 225000 0 unlikely negative Ramya 1day mch term 3kg No No No No No No No No Normal no growth 5700 18200 327 0.03 0.09 nil 93000 1 unlikely positive Soni 1day fch term 2.8kg No No No Yes No No No no Normal no growth 24716 19772 2628 0.05 0.12 nil 185000 0 unlikely negative Lalitha 1day mch term 2.5kg no no yes no no no no no poor klebsiella 22000 21120 892 0.1 0.2 nil 80500 1 unlikely negative Vijayakumari 2days mch preterm 1.4kg No No Yes No No No No No Poor nogrowth 7500 2250 2177 0.11 0.54 nil 291000 1 unlikely negative Murshitha 2days mch term 3kg No No Yes No No No No No poor nogrowth 12600 4536 609 0.09 0.2 nil 85000 2 unlikely negative Chitra 2days fch preterm 1.8kg No No No No No No Yes No Normal no growth 11300 7571 1222 0.07 0.17 nil 130000 1 unlikely negative Kavitha 3days fch preterm 1.4kg No No Yes No No No Yes no Poor no growth 2700 810 2151 0.05 0.18 nil 180000 0 unlikely negative Logeswari 3days fch preterm 1.3kg No No Yes No No No Yes No Normal no growth 3185 1019 682 0.05 0.33 nil 333000 0 unlikely negative Raihana parveen3days fch preterm 1.4kg No No Yes No No No Yes no Poor no growth 5600 1960 496 0.03 0.08 nil 333000 0 unlikely negative Revathy 3days fch preterm 2.6kg No No No No No No No No Normal nogrowth 8600 3440 1316 0.05 0.42 nil 317000 1 unlikely negative Aruna 3days fch term 3kg No No Yes No No No No No Normal nogrowth 10300 4532 640 0.06 0.17 nil 160000 0 unlikely negative Asiya 3days fch term 2.6kg No No No No No No No No Normal no growth 8300 5146 1669 0.02 0.09 nil 126000 1 unlikely negative Priyanka 3days fch term 2.9kg No No Yes No No No No No poor nogrowth 12200 5734 282 0.05 0.09 nil 69000 2 unlikely negative Kanaga 3days fch preterm 2.2 kg No No Yes No No No Yes No Normal no growth 11000 6270 2681 0.05 0.08 nil 200000 1 unlikely negative Manevda 3days fch preterm 2kg No No Yes No No No No No Normal klebsiella 17500 14000 757 0.05 0.14 nil 130000 2 unlikely negative Bagadeswari 3days fch preterm 1.8kg No No Yes No No No No No Poor s.aureus 25300 17204 782 0.05 0.35 nil 152000 1 unlikely negative Lalitha 3days mch term 2.5kg no no yes no no no no no poor klebsiella 22000 21120 427 0.04 0.09 nil 190000 0 unlikely negative Manju 4days mch term 2.7kg No No No Yes No No No No Normal no growth 11700 6201 831 0.03 0.09 nil 165000 0 unlikely negative Latha 5days mch term 3.1kg No No Yes No No No No No Normal nogrowth 14500 5800 6300 0.07 0.11 nil 168000 2 unlikely negative Keerthiha 10days fch preterm 1.38kg No No No No No No Yes No Normal no growth 12100 2420 256 0.3 0.61 nil 50000 5 very likely positive Therasa 10days mch term 2.5kg no no yes no no no no no poor nogrowth 6100 4636 717 0.1 1.6 nil 17000 5 very likely negative Gunasundari 14days fch preterm 850g no no yes no no no yes no poor klebsiella 12100 4840 1258 0.3 1.2 nil 30000 6 very likely positive Boomika 1day fch term 2.7kg No No Yes No No No No No poor nogrowth 9000 3150 231 0.2 0.6 vacuoles 115000 5 very likely negative Sunaiya banu 1day fch term 1.4kg No No No No No No Yes No Normal no growth 14518 3339 510 0.4 1.7 nil 102000 5 very likely negative Indirani 1day fch preterm 2kg Yes No Yes yes No No No No Poor strepto 10800 7246 2086 0.3 0.6 nil 132000 5 very likely positive Dhivya 1day mch term 2.6kg No No Yes yes No No No No Poor klebsiella 15000 9000 18572 0.4 1.7 nil 120000 5 very likely negative Nashrin esath 1day fch preterm 2.15kg No No No No No No Yes No Normal no growth 23100 10395 1020 0.3 0.5 vacuoles 204000 5 very likely negative Amutha 1day mch preterm 1.4kg No No Yes No No No No No Poor pseud aeur 17000 13260 154 0.19 0.4 granules 289000 5 very likely positive Badriya 1day mch term 3.6kg no no yes yes no no no no Normal pseud aeur 26600 24472 972 0.3 2 granules 79000 6 very likely negative Prolonged Foul Total Total Immature I M Age Gestational Birth Maternal Resuscitation Blood I T PMN Degenerative Platelet Name Sex PROM MSAF labour smelling Prematurity Temperature Activity WBC PMN PMN PMN Score sepsis CRP (DOL) age weight fever need Culture ratio changes inPMN count (>24hrs) liquor count count count ratio Sasikala 2day fch preterm 2.3kg No No Yes No No No No No Normal nogrowth 6000 3000 290 0.3 0.9 vacuoles 203000 5 very likely negative Geetha 2days mch preterm 2.3kg No No Yes No No No No No Poor nogrowth 9300 4185 725 0.3 5.5 nil 95000 5 very likely negative Savithri 3days fch preterm 2.2kg No No Yes yes No No No No Poor nogrowth 9500 5700 678 0.5 1.7 nil 112000 5 very likely negative Anbumala 3days fch term 2.9kg No No Yes No No No No No poor nogrowth 10500 6090 2882 0.4 0.6 nil 140000 5 very likely positive Damayanthi 3days fch preterm 2.3kg No No Yes No No No No No Poor klebsiella 15000 11250 1116 0.2 0.4 nil 173000 5 very likely negative Priya 4days mch term 2.8kg No No Yes No No No No No poor nogrowth 12000 4800 1825 0.5 2.6 nil 75000 5 very likely positive Sharmila 4days mch preterm 1.7kg No No Yes No No No No No Poor nogrowth 7500 5100 3600 0.4 2 granules 83000 6 very likely negative Gayathri 4days mch term 2.5kg Yes No Yes No No No No No Normal no growth 10200 5100 2016 0.3 0.6 vacuoles 130000 6 very likely positive Jothi 4days mch preterm 1.5kg No No Yes yes No No No No Poor E.coli 11500 6440 3758 0.2 0.6 nil 118000 5 very likely negative Bhuvaneswari 4days fch term 2.7kg No No Yes No No No No No poor nogrowth 11700 6786 5630 0.4 1.2 nil 115000 5 very likely negative Ammu 4days fch preterm 1.2kg no no yes no no no yes no poor s.aureus 22000 14740 5803 0.45 1.2 granules 170000 6 very likely positive Anandhi 5days mch term 3.3kg No No Yes No No No No No Normal nogrowth 8100 2835 3375 0.3 1.6 nil 123000 5 very likely negative Nadhiya 5days mch term 2.8kg No No Yes No No No No No poor nogrowth 7600 2888 5701 0.3 0.5 vacuoles 300000 5 very likely positive Kokila 5days mch term 2.9kg No No No No No No No No Normal nogrowth 9300 6045 4535 0.49 1.18 granules 100000 7 very likely negative Sumathi 5days mch preterm 1.4kg no no yes no no no yes no poor nogrowth 12900 8514 3390 0.49 1.18 granules 111000 7 very likely positive Karpagam 5days mch term 3kg no no yes no No No No No poor klebsiella 24000 20400 8395 0.2 0.5 vacuoles 165000 6 very likely negative Nasreen 6days mch term 2.7kg No No Yes No No No No No poor nogrowth 8900 3560 2520 0.16 0.2 granules 346000 5 very likely negative Sumathi 6days mch preterm 2.2kg no no yes no no no no no poor no growth 5200 4056 8085 0.42 1.3 vacuoles 160000 6 very likely positive Thingalmozhi 6days mch preterm 2.3kg No No No No No No No No Normal nogrowth 10600 5830 9464 0.4 1.7 vacuoles 266000 6 very likely positive Badri 7days mch preterm 1.8kg No No Yes No No No No No Poor nogrowth 8800 5720 3954 0.47 1.29 granules 96000 7 very likely positive Thenmathy 7days fch preterm 1.5kg No No Yes No No No Yes No Poor no growth 14000 8540 9792 0.5 4 granules 330000 6 very likely negative Sivakarthika 8days fch term 3.3kg No No No No No No No No Poor no growth 12600 6678 10982 0.4 2.2 vacuoles 175000 6 very likely positive Saratha 8days mch preterm 2.4kg No No No No No No No No Normal klebsiella 19000 14630 3665 0.25 0.39 granules 191000 6 very likely positive Nilofar 8days mch term 3.2kg no no yes no no no no no poor cnstaph 26700 23229 8120 0.28 0.5 granules 283000 6 very likely positive Priya 9days mch preterm 1.3kg No No Yes No No No No No Poor cnstaph 24000 15840 6736 0.35 0.7 vacuoles 245000 6 very likely positive xg;g[jy; gotk;

bgah; : taJ : ghypdk; :

Kfthp :

muR nfhit kUj;Jtf; fy;Y}hpapy; neha; Fwpapay; kUj;jt Jiwapy; gl;;l nkw;gog;g[ gapYk; khztp kU. k. g{h]zpkh mth;fs; nkw;bfhs;Sk;

“gr]rps']FHe]ijapd] neha] bjhw]iw ,uj]j ghpnrhijapd] \yk] tpiuthf fz]lwpjy] [” gw]wpa Ma]t[ kw;Wk; midj;J tpsf;f';fisa[k; nfl;Lf; bfhz;L vdJ re;njf';fis bjspt[g;gLj;jpf; bfhz;nld; vd;gij bjhptpj;Jf; bfhs;fpnwd;. vd] FHe]ijia ,e;j Ma;tpy; KG rk;kjj;JlDk;/ Ra rpe;jida[lDk; fye;J bfhs;s rk;kjpf;fpnwd;.

,e;j ma;tpy; vd] FHe]ij gw;wpa midj;J tptu';fs; ghJfhf;fg;gLtJld; ,jd;

Kot[fs; Ma;tpjHpy; btspaplg;gLtjpy; Ml;nrgid ,y;iy vd;gij bjhptpj;Jf; bfhs;fpnwd;. ve;j neuj;jpYk; ,e;j Ma;tpy; ,Ue;J vd] FHe]ijia tpyfpf; bfhs;s vdf;F chpik cz;L vd;gija[k; mwpntd;.

,lk; : njjp : bgw]nwhh] ifbahg;gk; / nuif