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Survival of Critically Ill Medical Patients Is Time-Critical Survival of Critically Ill Medical Patients is Time-Critical G. DUKE,* J. GREEN,* J. BRIEDIS† *Intensive Care Department, The Northern Hospital, Epping, VICTORIA †Anaesthesia Department, The Northern Hospital, Epping, VICTORIA ABSTRACT Objective: Survival from acute coronary syndromes and major trauma has been shown to depend on timely access to definitive treatment. We sought to identify the significance of intensive care unit (ICU) admission delay (lead-time) on the outcome of critically-ill medical patients with other diagnoses. Methods: From 1 January 1997 to 31 December 2003, a prospective cohort study was performed in critically-ill patients requiring mechanical ventilatory support (MV) and/or renal replacement therapy (RRT), admitted directly to the Northern Hospital ICU within 24 hours of arrival in the emergency department (ED). Patients were excluded if, a) they were admitted following surgery, major trauma or transfer from another hospital, or b) their duration of ICU stay was < 8hours. Data collected included de- identified patient demographics, final diagnosis, APACHE II mortality risk (pm) and lead-time (i.e. difference between times of entrance to the ED and ICU.) The primary outcome measure was hospital discharge status. Results: Six hundred and nineteen consecutive ICU admissions from the ED met the inclusion criteria and required MV (n = 557) and/or RRT (n = 162.) Non-survivors were older (median age 73 vs. 54yrs) and sicker (median pm 0.72 vs. 0.23) compared with survivors. Multivariate analysis using logistic regression identified lead-time as a significant predictor of mortality (RR = 1.06 per hour, 95% CI =1.01 - 1.10; p=0.015) in addition to age, diagnosis and illness severity. Conclusions: ICU admission delay (lead-time) is associated with a greater mortality-risk in critically ill medical patients requiring MV and/or RRT. (Critical Care and Resuscitation 2004; 6: 261-267) Key words: Intensive care, lead time, admission delay Timely resuscitation and definitive care is believed quence sufficient priority may not be afforded this to improve the survival of critically ill patients.1,2 This group. has resulted in the development of mobile emergency The lead-time interval, between arrival in hospital services, triage systems and hospital emergency depart- and admission to an intensive care unit (ICU), is ments (ED) with specific resuscitation areas and highly dependent upon a number of inter-related processes skilled staff. These dedicated, costly and rapid response (Table 1). Resuscitation and stabilisation of critically ill systems have improved the outcome from emergency patients is a significant workload issue8 for most emerg- surgical,3 major trauma4,5 and acute coronary syndro- ency departments making triage, resource allocation9 mes.6,7 The impact of a delay in definitive care on other and timely access,10 complex issues. Does the duration critically ill patients is unclear. of lead-time affect the outcome of critically ill patients Intuitively, survival from any form of acute illness is who are admitted directly to ICU?9,10 We have likely to be time-critical, but the relative importance of previously documented that inter-hospital transfer of timely access to definitive care for patients with acute critically ill patients delays their recovery,11 but we were respiratory or renal failure is not known. As a cones- unable to quantify the impact of the lead-time delay10 to Correspondence to: Dr. G. Duke, Intensive Care Department, The Northern Hospital, 185 Cooper St, Epping, Victoria 3076 (e- mail: [email protected]) 261 G. DUKE, ET AL Critical Care and Resuscitation 2004; 6: 261-267 Table 1. Lead-time components for patients admitted Patient consent was deemed unnecessary due to the to the intensive care unit absence of patient identification and the observational nature of the study using the pre-existing hospital Emergency department triage dataset. The study was unfunded. Data were recorded Initial assessment prospectively, by the authors, and included de-identified Resuscitation and stabilisation patient demographics, date and time of admission and Initial investigations transfer, physiological and laboratory data in ICU Specialist referral and assessment (Table 2), final diagnosis and hospital outcome. Lead- Intensive care referral and assessment time was defined as the time difference between patient Transfer to intensive care unit arrival at the ED and arrival in the ICU. Table 2. Summary of APACHE II variables and 13 methodology ICU admission vis a vis the physiological disturbance that occurs during transportation.12 Variables To our knowledge, no outcome study has directly • Physiological data: temperature, blood pressure, addressed this question. Since a randomised controlled heart rate, respiratory rate, Glasgow coma score. trial of lead-time (i.e. delayed admission to ICU) is • Pathological data: haematocrit, total white cell count, unethical, we undertook a prospective cohort study of sodium, potassium, creatinine, urea, albumin, critically ill patients, with acute respiratory or renal glucose, and arterial pH and PaO2. failure, requiring urgent intensive care services. We • Clinical data: age, diagnosis, presence of acute sought to establish what impact, if any, lead-time has on respiratory and/or renal failure, chronic health status. the outcome of critically ill medical patients at The Northern Hospital (a Melbourne metropolitan Formula for pm score University-affiliated teaching hospital providing acute- logn(pm/1-pm) = -3.517 + 0.146(k1+k2+k3)+k4+k5 care health services except cardiac surgery and organ Where transplantation). • k1 = a variable score based on physiological and pathological data; METHODS • k2 = a variable weighting for age; All adult patients requiring intensive care services at • k3 = a variable weighting for chronic health status; The Northern Hospital between 1 January 1997 and 31 • k4 = a constant weighting for emergency surgical December 2003 were entered in the study if they, a) patients; and were admitted directly from the ED to the ICU within 24 • k5 = a variable weighting for principal diagnostic hours of hospital arrival, and b) required invasive category mechanical ventilatory support (MV) and/or renal replacement therapy (RRT) – therapies not available elsewhere within the hospital. These patients represent a Physiological and laboratory data were used to high-risk group for whom lead-time is more likely to be calculate predicted mortality-risk (p ) as an index of important, and for whom ICU admission is unequivocal. m illness severity using the Acute Physiology and Chronic Patients requiring emergency surgery, major trauma care Health Evaluation (APACHE) II method (Table 2.).13 or urgent coronary intervention were excluded. The highest and lowest values within the first 24 hours All subjects were resuscitated and stabilised within of admission to ICU were collected for each variable, the ED, and referred to the ICU medical team, prior to except the Glasgow coma score (GCS), to optimise transfer to ICU. Suitability for ICU admission was based calculation of illness severity. The GCS was recorded as on the clinical judgement of the on-duty intensive care the lowest value prior to initiation of sedation and/or specialist. intubation in the ED. Raw data and APACHE II derived Entered subjects were excluded from the analysis if; p , for the entire cohort, were submitted to the Adult a) they were subsequently transferred to another hospital m Patient Database14 (the Australian and New Zealand for definitive care; b) MV was initiated after admission Intensive Care Society and government funded national to ICU; c) RRT was initiated more than 24 hours after data repository) for validation, data quality checks and admission to ICU; d) they were less than 16 years of comparison with national benchmarks. Missing or age; or e) they remained in ICU less than 8 hours. The suspicious data (e.g. 00:00 hr), and admission and primary end point was hospital discharge status. transfer times for all subjects, were checked for Hospital ethics committee approval was obtained. accuracy by retrospective case record review. 262 Critical Care and Resuscitation 2004; 6: 261-267 G. DUKE, ET AL Statistical analysis Table 4. Ten most common primary diagnostic Based on our previous observations of a 40% categories increase in relative mortality-risk from delayed Primary Diagnosis n (%) Case fatality admission,11 we calculated (using p = 0.05, and power of rate (%) 80%) that a population of at least 300 patients would be required. Comparison of survivors with non-survivors Drug overdose 111 (18%) 3 (3%) (using Fisher’s exact and Mann-Whitney tests) was Cardiac arrest 84 (14%) 63 (75%) undertaken to identify univariate factors associated with Pneumonia 41 (7%) 11 (27%) in-hospital mortality. Group data are presented as Exacerbation COPD 40 (7%) 8 (20%) median (inter-quartile range) unless otherwise indicated. Septic shock 40 (7%) 20 (50%) Multivariate analysis using a logistic regression model Congestive heart (SPSS)15 was undertaken to identify independent factors failure 36 (6%) 18 (50%) predictive of in-hospital mortality. A stepwise regression Cardiogenic shock 32 (5%) 24 (75%) procedure was used to derive the model. Variables Respiratory arrest 29 (5%) 3 (10%) finally entered into the multivariate analysis included Acute renal failure 22 (4%) 4 (18%) lead-time, patient age and sex, severity of illness Acute asthma 16 (3%) 0 (APACHE II score [APS]), diagnosis, premorbid Subtotal 451 (73%) 154 (34%) (chronic health) status,13 acute renal and respiratory failure the time and the weekday of admission. The cumulative frequency distribution of lead-time RESULTS interval for survivors and non-survivors is displayed in Over the study period 3461 patients were admitted to Figure 1. Four hundred and ninety five patients (80%) the ICU (83% as emergencies) of whom 1593 (46%) were admitted to ICU within 8 hours of arrival in were admitted directly from the ED. After excluding hospital. Admission times were evenly spread across the major trauma and coronary intervention patients, 669 three nursing-shifts of the day (Table 5).
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