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Available online http://ccforum.com/content/11/1/R2 Open Access | downloaded: 28.9.2021 https://doi.org/10.7892/boris.23281 source: Critical Care Vol 11 No 1 Bracht et al. length of hospital stay [12]. However, in the study of Pearse Brønshøj, Denmark). For all blood gas analyses, the same and coworkers [12], observational data from a randomized, automated blood gas analyzer was used. PO2 (oxygen pres- controlled interventional trial were used. Although a carefully sure) was corrected for actual body temperature, and inspired defined treatment protocol was applied in their study and fractional oxygen concentration (FiO2) was recorded concom- goals for arterial oxygen saturation, hemoglobin, heart rate, itantly. If an arterial catheter was in place, an arterial blood mean arterial pressure, serum lactate, and urine output were sample was taken at the same time for the same analysis. Sam- the same in all patients, intravenous fluid administration was pling from both sites was repeated six hours later. Persons guided by central venous pressure in one group of patients, involved in the treatment of the patients were blinded to the whereas in others fluid management was guided by stroke vol- results obtained from the central venous blood. ume and supplemented with low-dose dopexamine. With such a design, the predictive nature of ScvO2 may relate both to the Data collection initial cardiovascular dysfunction and to subsequent attempts For data analysis, the patients were divided into five groups to correct it. according to the main clinical problem that necessitated admission to the ICU: sepsis (n = 26), cardiocirculatory dys- In patients with severe sepsis or septic shock admitted to the function/failure (n = 12), respiratory dysfunction/failure (n = emergency department, ScvO2-guided hemodynamic optimi- 14), central nervous system problems (hemorrhage, ischemia, zation has been shown to reduce mortality [13]. Whether other injury) (n = 29), and urgent surgery (n = 17). The 'urgent sur- patient groups may also profit has not yet been determined. gery' group included urgent cardiovascular (n = 8), visceral (n Because central venous catheterization is frequently per- = 6), and orthopedic (n = 3) surgery. Due to the small number formed in unplanned intensive care unit (ICU) admissions, rou- of patients, the subgroups in 'urgent surgery' were not used for tine screening for low ScvO2 could easily be performed, and further analysis. The following data were collected from all ScvO2-guided optimization, if proven beneficial, could be patients: age, gender, Simplified Acute Physiology Score established early during the ICU stay. The goal of this study (SAPS II), length of ICU and hospital stay up to day 28, 28-day was to evaluate whether ScvO2 at admission and six6 hours survival, and patient location after 28 days. Length of stay later is associated with outcome in patients requiring (LOS) in hospital before ICU admission (LOSbefore ICU), in hos- unplanned admission to the ICU. pital (LOShospital), and in ICU (LOSICU) were defined from the patients' records. These data were acquired from the institu- Materials and methods tion's own patient database. LOSbefore ICU was defined as the This study was approved by the Ethics Committee of the Can- time from the patient's arrival to the hospital until ICU admis- ton of Bern, Switzerland, and deferred written informed con- sion. LOShospital was defined as the time from hospital admis- sent was obtained from patients where possible or from a sion to hospital discharge or 28 days, whichever was shorter. close relative. All unplanned admissions between October and LOSICU was defined as the time in the ICU during the study December 2004 were screened for inclusion and exclusion period. The time a patient stayed in the ICU after a readmission criteria in a multidisciplinary 30-bed ICU. was not added to LOSICU. Three categories were applied for the patient's location: dead, still in hospital after 28 days, or at The inclusion criterion was either the presence of or a clinical home/nursing facility. Eight patients were lost to follow-up; indication for a central venous catheter. Exclusion criteria were consequently, their data could not be used for the assessment a contraindication for a central venous catheter and refusal of of the relationship between ScvO2 and outcome. blood products. The clinical indication for a central venous catheter was determined by the attending physician, and Statistical analysis patients were enrolled in the study only if the first blood sample All data were tested for normal distribution with the Kol- from the central venous catheter was obtained within the first mogorov-Smirnov test before further statistical analysis. If the two hours after ICU admission. data were normally distributed, parametric tests were used; otherwise, logarithmic or inverse transformation was per- Protocol formed. If the transformation did not result in normal distribu- All patients were treated according to standard practice for tion, non-parametric tests were applied. Differences between the ICU. Protocols for hemodynamic treatment, weaning from admission and six hours of ICU stay were assessed using Stu- mechanical ventilation, sedation and analgesia, anticoagula- dent's paired t test (normally distributed data) and the Wil- tion, and management of blood glucose and potassium were coxon signed rank test (otherwise). Differences between the used in all patients where applicable. Whenever possible, a five predefined patient groups (sepsis, n = 26; cardiocircula- central venous blood sample was obtained immediately after tory dysfunction/failure, n = 12; respiratory dysfunction/failure, ICU admission, or two hours afterward at the latest, for the n = 14; central nervous system problems, n = 29; and urgent determination of oxygen saturation, blood gases, and hemo- surgery, n = 17) were tested with one-way analysis of variance globin. Blood gas analyses were performed by intermittent (parametric data) or the Kruskal-Wallis test (otherwise). blood sampling and co-oximetry (ABL 725; Radiometer A/S, Receiver operator characteristic (ROC) curves were Page 2 of 8 (page number not for citation purposes) Available online http://ccforum.com/content/11/1/R2 constructed to identify optimal cutoff values for the association patients had diseases of the central nervous system, 26 (27%) of admission ScvO2 and SAPS II, respectively, with 28-day had sepsis, 14 (14%) had respiratory failure, 12 (12%) had mortality. The optimal cutoff was defined as the value associ- circulatory failure, and 17 (17%) had undergone urgent sur- ated with the highest sum of sensitivity and specificity. In addi- gery. Septic patients had the highest number of organ failures tion, univariate analysis was performed to test how 6-hour (Table 1). The 28-day mortality was 18% and there were no ScvO2, SAPS II, and admission category were associated with significant differences between the patient groups. SAPS II 28-day mortality. Differences between patients with high and was higher in non-survivors than in survivors (70 [47 to 92] low admission ScvO2 were tested using the t test for normally versus 39 [11 to 87], p < 0.001). Median (range) LOSICU was distributed data; otherwise, the Mann-Whitney rank sum test 3 (1 to 28) days. There was a significant difference in LOSICU was used. Data on mortality and patient location at 28 days in the different patient groups (p = 0.038). LOShospital up to day were tested with Fisher's exact test. Data are presented as mean ± standard deviation if normally distributed; otherwise, data are presented as median and range. Statistical signifi- cance was assumed at a p value of less than 0.05. Sigma Stat version 3.1 (RockWare, Inc., Golden, CO, USA) was used for statistical analysis. Results Demographic data Of 349 screened patients, 99 were included in the study. One initially included patient had to be excluded because of a miss- ing ScvO2 value at baseline. Table 1 shows the demographic data of all screened patients. Patients included in the study had a higher SAPS II than patients who were screened but not included (43 [11 to 92] versus 29 [6 to 84], p < 0.001). For 276 (79%) of the patients who were not included, the reason for exclusion was absence of a central venous catheter within the first two hours after ICU admission, in 38 patients (11%) informed consent was not available, and in 35 patients (10%) other reasons were present. Median (range) age of all included patients was 63 (19 to 83) years, and median SAPS II was 43 (11 to 92) (Table 1). Twenty-nine (30%) of the included Page 3 of 8 (page number not for citation purposes) Critical Care Vol 11 No 1 Bracht et al. Table 2 Hemodynamic data from the whole collective and the different patient groups Systolic blood pressure Mean arterial blood Heart rate Peripheral oxygen saturation Axillary temperature (mm Hg) pressure (beats per minute) (%) (°C) (mm Hg) n 0 hours 6 hours 0 hours 6 hours 0 hours 6 hours 0 hours 6 hours 0 hours 6 hours All patients 98 120 ± 30 113 ± 23 81 ± 19 76 ± 14 92 ± 24 89 ± 20 97 (56–100) 96 (87–100) 36.8 (33.4–39.1) 37.2 (34.0–39.5) b 68 ±Cardiocirculatory 9 12 95 ± 23 100 ± 18 70 ± 16 97 ± 24 87 ± 15 94 (76–100) 98 (91–100)c 35.9 (35.3–37.3) 37.0 (36.4–39.0) failure Sepsis 26 114 ± 30 106 ± 20 77 ± 20 71 ± 11 99 ± 27 94 ± 25 96 (56–100) 97 (87–100) 36.9 (35.3–39.1) 37.3 (36.0–39.5) CNS disease 29 139 ± 27b 124 ± 27a,b 90 ± 18 83 ± 17b 84 ± 20 88 ± 18 100 (87–100) 98 (90–100) 36.7 (33.4–39.1) 37.4 (35.6–39.4)c Respiratory 14 113 ± 29 116 ± 23 76 ± 17 78 ± 15 99 ± 26 92 ± 25 93 (87–100)b 94 (88–99)b 36.7 (35.5–37.8) 37.5 (35.9–38.6)c failure Other 17 119 ± 23 111 ± 18 81 ± 17 75 ± 8 85 ± 17 84 ± 12 99 (88–100) 96 (90–100) 36.2 (34.2–38.1) 37.2 (34.0–38.2) aPaired t test, p < 0.05; bKruskal-Wallis analysis for variance on ranks, p < 0.05; cWilcoxon signed rank test, p < 0.05.
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  • Hidden Markov Chain Modeling and Analysis for ICU Mortality Prediction

    Hidden Markov Chain Modeling and Analysis for ICU Mortality Prediction

    Abstract ID#: 3147 Category: Health Sciences Undergraduate/Graduate: PhD CHF-AR-HMM and Analysis for Post-ICU Mortality Prediction Yilin Yin, Chun-An Chou Department of Mechanical & Industrial Engineering, Northeastern University Overlook Modeling: Conduct Autoregressive hidden Markov model (AR-HMM) The intensive care unit (ICU) mortality prediction q The post-ICU dynamic risk (mortality) prediction is on demand by patients for timely decision making and consecutive care q Our goal is to predict the mortality within certain days (e.g., 5-10 days since admission) with the last 24-hour physiology data by improving the existing scoring system State-of-the-art Scoring System q Risk scores converted from irregular time-series body vitals and laboratory test data are delivered for predicting mortality q In this case, Simplified Acute Physiology Score (SAPS) II [1] designed for mortality prediction is considered Fig 3. Methodology workflow and CHF-AR-HMM mixture model. Challenges q Risk scoring systems are not capable of capturing the patient’s severity level in a dynamic manner q Current cumulative hazard Markov model method relies on the prior probability, which is highly biased Methodology q We use the mixture model CHF-AR_HMM as combination of autoregressive Hidden Markov model (AR-HMM) [2] with cumulative hazard function (CHF) [3] to discover the latent states (survival or death) dynamic on the aggregated data from the risk scoring system for each patient Data Processing q PhysioNet ICU Challenge 2012 MIMIC II [4] is used for model validation, including first 48-hour ICU monitoring (12 physiology variables and other patient attributes), laboratory tests data since ICU admission from 12000 patients, the demographic and mortality outcome information.