Intensive Care Medicine 2014 40 (8) 1097

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Allan GarlandKendiss OlafsonClare D. RamseyMarina Yogendran

Randall Fransoo

Distinct determinants of long-term

and short-term survival in critical illness

Received: 30 December 2013Accepted: 19 May 2014

Published online: 11 July 2014 Springer-Verlag Berlin Heidelberg andESICM 2014

Take-home message: Short-term mortalityafter onset of critical illness is determinedmainly by type and severity of the acuteillness. Age and comorbid conditions exertsmall influences on short-term mortality, butare the main determinants of long-termmortality among those who survive in theshort term.

Electronic supplementary materialThe online version of this article(doi:10.1007/s00134-014-3348-y) containssupplementary material, which is availableto authorized users.

A. Garland ()) K. Olafson C. D. RamseyDepartment of Internal Medicine,University of Manitoba, 820 Sherbrook St.,Room GF-222, Winnipeg, MB R3A1R9,Canadae-mail: [email protected]

A. Garland R. Fransoo

Department of Community Health Sciences,University of Manitoba, Winnipeg, MB,Canada

A. Garland M. Yogendran R. FransooDepartment of Manitoba Center for HealthPolicy, University of Manitoba,

408-727 McDermot Ave., Winnipeg,MB R3E3P5, Canada

Abstract Purpose: To identifythe determinants of short-term andlong-term survival in adult patientsadmitted to intensive care units(ICUs).Methods: This population-based, observational cohort studyincluded all eleven adult ICUs in theWinnipeg Health Region of Mani-toba, Canada, analyzing initial ICU

admissions during the period19992010 of all ManitobansC17 years old. Analysis includedKaplanMeier survival curves andmultivariable regression models of30-day mortality and post-90-daysurvival among those who survived today 90. We used likelihood ratios tocompare the predictive power ofclusters of variables in these models.Results: After 33,324 initial ICUadmissions, mortality rates within 30and 90 days were 15.9 and 19.5 %,respectively. The survival curvedemonstrated an early phase with ahigh rate of death, followed by a

markedly lower death rate that wasonly clearly established after several

months. 30-day mortality was pre-dominantly determined bycharacteristics of the acute illness;with its relative contribution set at1.00, the next largest contributorswere age (0.19) and comorbidity(0.16). In contrast, post-90-day mor-tality was mainly determined by age(relative contribution 1.00) andcomorbidity (0.95); the next largestcontributor was characteristics ofacute illness (0.28). Conclu-sions: We observed two phases of

survival related to critical illness.Short-term mortality was mainlydetermined by the acute illness, butits effect decayed relatively rapidly.Mortality beyond 3 months, amongthose who survived to that point, wasmainly determined by age andcomorbidity. Recognition of thesefindings is relevant to discussionswith patients and surrogates aboutachievable goals of care.

Keywords Prognosis Critical careOutcome assessment Mortality End of life care

Introduction

Among the most important things done by physicians car-ing for critically ill patients in intensive care units (ICUs) isdiscussing treatment plans with patients and their

surrogates. A necessary part of devising care plans withachievable goals is accurately prognosticating the chanceof survival. However, despite decades of development ofICU risk stratification systems such as SAPS, APACHE,and MPM [1], their usefulness for this purpose is limited.

Intensive Care Med (2014) 40:10971105DOI 10.1007/s00134-014-3348-y ORI GI NAL
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Such systems require complex calculations, and theirability to predict outcomes for individual patients is poor[2]. Also, they were only designed to address short-termoutcomes, such as in-hospital mortality, which can bemisleading because it is heavily influenced by inter-hos-pital and post-hospital transfer patterns [3]. Most

importantly, short-term outcomes are not patient cen-tered because most individuals place more value onlong-term than short-term survival [4].

Also, even for the short-term outcomes for whichprediction schemas exist, physicians feel they receiveinadequate training in prognostication [5, 6]. Perhapsreflecting this, White et al. [7] found that in more thanone-third of discussions, ICU physicians did not discussthe survival prognosis. In the absence of approaches toshort-term prognostication that are easy to understand anduse at the bedside, ICU practitioners commonly rely ongestalt based upon their personal experiences with short-term outcomes [8].

For all of these reasons, we sought to separatelyidentify the factors that determine long-term and short-term survival after initial episodes of critical illness.

A subtle issue in identifying the basic phenomena atwork is to distinguish between long-term survival startingat the onset of critical illness and long-term survivalamong those who survive in the short term. Prior studieshave failed to clearly separate these phenomena [912].Most studies of long-term survival from critical illnesshave been limited as they evaluate specific patient subsetsand adjust for few confounding variables; none have beenpopulation-based. Furthermore, we are unaware of studiesthat assessed short-term and long-term mortality in thesame cohort.

In this hypothesis-driven study, we sought to over-come all the limitations of prior investigations byevaluating a large, population-based ICU cohort. Wehypothesized that there are two elemental phenomena atwork, with the primary determinants of short-term sur-vival from the onset of critical illness being distinct fromthe main determinants of long-term survival among thosewho survive in the short term. Specifically, we hypothe-sized that the former are the type and severity of acuteillness, while the latter are age and chronic medicalconditions.

This work was the subject of a published conferenceabstract [13].

Methods

We performed a population-based analysis of adultsadmitted to 11 adult ICUs in the Winnipeg Regional HealthAuthority of the Canadian province of Manitoba over130 months during 19992010. Four of the ICUs are withincommunity hospitals; the other seven are in tertiary

teaching hospitals. ICU types are: five medical-surgical,two coronary care, one medical, one surgical/trauma/neu-rosurgical, onecardiac surgical, and onerespiratory. All areclosed-model ICUs staffed by intensivists or cardiologists.These represent all level 1 and level 2 adult ICUs [14] inManitoba, except for a nine-bed medical-surgical ICU in

the city of Brandon, 124 miles away. Manitobas popula-tion was 1.23 million in 2010, and 697,000 (57 %) of thoseresided in the Winnipeg region [15].

Data for this analysis came from the Manitoba Inte-grated Critical Care Database (MICCDB), which has beendescribed previously [16, 17], and links a clinical ICUdatabase to the provinces administrative health data. TheICU database contains detailed information about alladmissions to the 11 included ICUs. Manitoba has uni-versal, single-payer health insurance, and theadministrative data include hospital discharge abstractsand vital status data for all residents. We identified sur-vival status up to a censoring date of November 30, 2011.Creation of the MICCDB included identifying inter-ICUand inter-hospital transfers and merging records that werepart of a single episode of ICU care [17]; these episodesare the units of analysis in this study.

We assessed survival after ICU admission for Mani-toba residents C17 years of age admitted to any of the 11Winnipeg ICUs. In order to study a more homogeneousphenomenon by evaluating outcomes after initial ICUepisodes, and to avoid biased mortality rates due toindividuals being represented multiple times, we onlyconsidered the first ICU episode of each person during thestudy period, and excluded those who had been admittedto any level 1 or level 2 ICU during the prior 10 years.

To assess mortality over time, we first created aKaplanMeier survival curve. For comparison, weapplied standard methods [18] to Manitoba life tables [19]to create the survival curve of an age- and sex-matchedgeneral population cohort. To assess whether the ICUpatients mortality rate eventually became similar to thatof the general population, we used Fishers Z-test tocompare the slopes from years 6 to 12.

We then created three multivariable regression mod-els. For short-term mortality, we used logistic regressionof death within 30 days after ICU admission. For long-term mortality, we used Cox proportional hazardsregression of time to death[90 days after ICU admissionamong those who survived to day 90 and remained in the

province. Our choice to begin the long-term phase at90 days after ICU admission was informed by the Kap-lanMeier curve (Fig. 1). Last, to test the expectation thata mixture of short-term and long-term determinants wouldbe seen in analysis spanning the early and late phases, weperformed Cox regression of time to death starting fromICU admission. The three regression models includedidentical explanatory variables, including demographics,comorbid conditions, ICU admission details, and type andseverity of acute illness.

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Demographics were age, sex, and socioeconomic sta-tus (SES). SES was divided into categories based onaverage household income in the postal code of residence[20]. Ten of the categories were quintiles, with rural andurban residents categorized separately. The 11th SEScategory, referred to as Not Calculated, included peo-ple living in areas where the census does not calculateaverage household income (mainly nursing homes andother chronic care facilities, with a smaller portion inprisons).

For comorbidities, the 31 conditions described byElixhauser et al. [21] were identified from hospital abstractsusing ICD-9-CM and ICD-10 coding [22]. These were

derived using all diagnoses listed in the index hospitaliza-tion and all hospitalizations up to 1 year prior to it [23]. Wecollapsed the categories of diabetes with and withoutchronic complications because this distinction was flawedin Manitoba before 2006. Also, the HIV/AIDS categorywas not included due to small numbers (n = 61, 0.2 %).

ICU admission details were year (categorized intoapproximately equal time intervals as 19992002,200306, or 200709), timing (weekday vs. weekend;days vs. nights, defined as 6pm8am), hospital type(community vs. tertiary/teaching), and pre-ICU location(emergency department, ward, or operating room/post-anesthesia unit).

Primary ICU admission diagnosis was derived fromthat field in the ICU database. This custom system of2,065 diagnoses was categorized by mapping to the cor-responding ICD-9-CM chapters. The eight chapters with\100 observations each were collapsed into a singlemiscellaneous category containing 187 observations.We used five measures of severity and type of acute ill-ness: (a) Glasgow Coma Scale score (GCS), categorizedas 35, 68, 912, 1314, and 15; (b) APACHE II AcutePhysiology Score [1], excluding its neurologic subscore

(APSminusNeuro), since GCS was included separately;and use during the initial two calendar days in ICU of(c) invasive mechanical ventilation; (d) any vasoactiveagents; and (e) any form of renal dialysis.

To allow for nonlinear relationships between mortalityand the continuous covariates of age and APSminus-

Neuro, these were included in the models as 4-knotrestricted cubic splines; the resulting relationships wereevaluated statistically and graphically [24]. We ensuredthe absence of multicollinearity among the independentvariables using variance inflation factors [25]. Propor-tional hazards for Cox regression was verified by log-minus-log plots and time-dependent covariates [26];variables in violation were included as time-dependentcovariates.

The central analysis for this study quantified the incre-mental predictive power of five clusters of variables: (1) age,(2) sex and SES, (3) comorbid conditions, (4) acute illnesscharacteristics (diagnosis, GCS, APSminusNeuro, pre-ICUlocation, need for mechanical ventilation, vasoactive drugs,and dialysis), and (5) the remaining model variables(admission timing, admission year, hospital type). Becauseof variable scaling and other issues, regression coefficientmagnitudes and p-values do not provide this information.Instead, we adapted the approach espoused by Harrell [27]and used likelihood ratios of nested models to calculate aunitless index of the incremental predictive value of modelvariables [27]. For each of the three models,for eachvariablecluster, we calculated the likelihood ratio of the modelincluding all explanatory variables and the likelihood ratiofor the reduced nested model excluding only the variablecluster of interest. The quotient of the likelihood ratio (QLR)of the reduced model to the full model is a measure of theincremental explanatory power of the variable cluster ofinterest added to all the other variables in the model [27]. Tofacilitate comparison, we scaled these difference ratios sothat, within each model, the scaled value for the mostinfluential variable cluster equaled 1.000.

For analysis, we used SAS 9.2 (SAS Institute, Cary,NC, USA). Unless indicated otherwise, the values pre-sented are the mean standard deviation (SD). p-values\0.05 were considered statistically significant.This study was approved by the Health Research EthicsBoard of the University of Manitoba and the ManitobaHealth Information Privacy Committee.

Results

During the study period, 34,306 individuals had initialICU episodes. Of these, 982 (2.9 %) had missing pre-ICUlocations and were excluded, leaving 33,324 episodes foranalysis (see Table1 and eTable 1 in the Electronicsupplementary material, ESM). Sixty percent were male,average age was 63.5 16.2 years, urban residents and

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Observed ICU cohort

Age- & sex-matchedgeneral population

30 days

90 days

Surviva

lFraction

Years after ICU Admission

Fig. 1 KaplanMeier curve of survival after first admission tointensive care units, including 95 % confidence intervals, with acomparison of survival for an age- and sex-matched generalpopulation cohort

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lower SES categories were more common, and mostpatients were admitted from emergency departments intotertiary hospital ICUs. The most common admissiondiagnosis category was cardiovascular disorders, followedby the category including sepsis and its complications,and respiratory disorders. Average APACHE II score was

15.3 7.9 and, during the initial two ICU days, 57 %received invasive mechanical ventilation, 46 % neededvasoactive drugs, and 5 % received renal dialysis. Median(interquartile range) ICU and hospital lengths of staywere, respectively, 2.4 days (1.14.6) and 11 days (624).In this cohort, 5,285 (15.9 %) died within 30 days and

6,484 (19.5 %) died within 90 days. Excluding 283patients who survived to 90 days but had left the prov-ince, 26,557 patients were included in the long-termanalysis (Table1, eTable 1 of the ESM); of these, 1,432(5.05 %) were still in the hospital and 42 (0.16 %) werestill in ICU at day 90.

The survival curve (Fig.1) demonstrates two distinctphases of mortality associated with critical illness. Theearly phase has a high rate of death, which is followed bya markedly lower death rate that is only clearly estab-lished several months after ICU admission. Mediansurvival was 9.2 years (95 % CI, 8.99.5). For years 6 to

Table 1 Patient characteristicsfor selected variables Variable All patients

(n = 33,324)Survivors to 90 daysafter ICU admission(n = 26,557)

Female sex 13,506 (40.5) 10,448 (39.3)Age (years), mean SD 63.5 16.2 62.4 16.0

Comorbid conditionsHypertension, uncomplicated 12,498 (37.5) 10,118 (38.1)Arrhythmia 9,338 (28.0) 7,228 (27.2)Diabetes 8,447 (25.3) 6,699 (25.2)Congestive heart failure 7,355 (22.1) 5,332 (20.1)Chronic pulmonary disease 4,159 (12.5) 3,072 (11.6)Fluid and electrolyte disorders 3,033 (9.1) 2,075 (7.8)Valvular heart disease 2,762 (8.3) 2,384 (9.0)Peripheral vascular disease 2,658 (8.0) 2,011 (7.6)Renal failure 2,564 (7.7) 1,691 (6.4)Other neurologic disorders 2,424 (7.3) 1,385 (5.2)Solid tumor without metastasis 2,241 (6.7) 964 (3.6)Hypertension, complicated 1,927 (5.8) 1,386 (5.2)Alcohol abuse 1,793 (5.4) 1,267 (4.8)

Pre-ICU locationEmergency department 17,532 (52.6) 14,163 (53.3)

Operating room or post-anesthesia unit 9,860 (29.6) 8,670 (32.6)Ward 5,932 (17.8) 3,724 (14.0)Hospital typeTertiary/teaching hospital 21,197 (63.6) 17,105 (64.4)Community hospital 12,127 (36.4) 9,452 (35.6)

Admission diagnosis categoryCirculatory system 20,140 (60.4) 17,256 (65.0)Symptoms, signs, and ill-defined conditionsa 3,925 (11.8) 2,281 (8.6)Respiratory system 3,557 (10.7) 2,588 (9.7)Injury and poisoning 2,542 (7.6) 2,084 (7.8)Digestive system 1,143 (3.4) 757 (2.9)Nervous system and sense organs 634 (1.9) 478 (1.8)Endocrine, nutritional, metabolic, immunity 525 (1.6) 433 (1.6)Genitourinary system 361 (1.1) 279 (1.1)Others (see eTable 1 of the ESM) 497 (1.5) 401 (1.5)APACHE II score, mean SD 15.3 7.9 13.3 6.2

Glasgow Coma Scale score35 1,910 (5.7) 461 (1.7)68 1,456 (4.4) 804 (3.0)912 2,303 (6.9) 1,507 (5.7)1314 2,965 (8.9) 2,096 (7.9)15 24,690 (74.1) 21,689 (81.7)

Mechanical ventilation ICU day 1 and/or 2 19,091 (57.3) 13,897 (52.3)Vasoactive drugs ICU day 1 and/or 2 15,387 (46.2) 11,204 (42.2)Renal dialysis ICU day 1 and/or 2 1,789 (5.4) 1,213 (4.6)

See eTable 1 of the ESM for full resultsValues are given as number (%) unless otherwise indicateda Includes sepsis-related diagnoses

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12, the survival curve slope for ICU patients was notdifferent from that of the age- and sex-matched generalpopulation cohort (p = 0.99).

Numerous variables were associated with 30-day andpost 90-day mortality (Table2, eTable 2 of the ESM;Fig.2, eFigure 1 of the ESM). The effects of three

comorbid conditions violated the proportional hazardsassumption and were included in the post-90-day modelas time-dependent covariates (eTable 2 of the ESM). Therelationship of age to mortality was significant and non-linear in both models, with a rapidly rising risk of deathabove 70 years of age (Fig. 2). While APSminusNeurowas significant in both models, it was much more stronglyrelated to 30-day than to post-90-day mortality (eFigure 1of the ESM). A number of comorbid conditions wereassociated with higher mortality in both models. Theassociation of a few chronic conditions with lower mor-tality likely represents the effect of coding bias, whereinmilder chronic conditions are less likely to be coded formore severely ill patients [21].

There were notable differences between determinantsin the 30-day and post-90-day mortality models. Sex wasonly significant in the post-90-day model, with a higherhazard for death among men, as in the general population.After adjusting for other variables, mortality to 30 dayswas associated with the use while in ICU of mechanicalventilation and vasoactive agents, but not dialysis, whilethe reverse was true for post-90-day mortality. GCS onlyhad a consistent doseresponse relationship with 30-daymortality. While many acute diagnostic categories weresignificant in both models, there were some notable dif-ferential effects, e.g., compared to the reference group ofcardiovascular diagnoses, patients admitted for genito-urinary disorders had significantly lower 30-day mortalitybut significantly higher post-90 day mortality.

However, the magnitude and p-values of coefficients inregression equations do not directly indicate their predic-tive power. Instead, Table 3 shows that death at 30 daysafter ICU admission was predominantly determined by thecharacteristics of the acute illness; the other four variableclusters had much lower predictive power. In contrast,mortality beyond 90 days after ICU admission, amongpatients who survived to that time point, was mainlydetermined by age and comorbidity. As expected, factorsthat were predictive of death in a model that included boththe short-term and long-term phases were a mixture of

those from the two purer models: acute illness, age, andcomorbidity were the major determinants.

Discussion

Since people generally place more value on long-term thanshort-term survival [4], conversations with ICU patients ortheir surrogates about achievable goals and care plans are

inadequate if they focus only on the acute illness and itsshort-term consequences without explicitly addressingsubsequent survival. By better delineating, in a simplefashion, the factors relevant to short-term vs. long-termsurvival, our findings can help inform such discussions.

Though several prior studies attempted to evaluate

determinants of long-term outcomes, we are not aware ofany that avoided mixing short-term with long-termdeterminants. For some, this occurred because theyevaluated long-term survival starting from the time ofICU admission [9, 10]. Even those that attempted toexclude the short-term period used starting points thatwere still within the envelope of the short-term determi-nants of mortality. Specifically, Fig.1 indicates that ittakes approximately 3 months after ICU admission formost of the influence exerted by the acute illness to dis-appear, while those earlier studies began their long-termanalyses at the time of ICU or hospital discharge [11,12,28].

For these reasons, only approximate comparisons canbe made between our study and two previous studies oflong-term mortality that also assessed the predictivepower of different variables. Those studies, which usedstarting points either 5 days after ICU admission [12] orat ICU discharge [11], both reported the following trendfor strength of predictive power: age[ comorbidities[severity of acute illness. In contrast, our model of mor-tality starting from ICU admission showed the followingtrend: characteristics of acute illness C age C comor-bidity. The reduced contribution of acute illness in thetwo prior studies likely reflects the beginnings of itsdecaying importance, even when starting from a timepoint less than 1 week after onset of critical illness.

Among other comparisons with existing literature,Williams et al. [28] reported determinants of post-hospitalmortality similar to those seen in our post-90-day mor-tality model (eTable 2 of the ESM), though furthercomparisons are difficult because those investigators didnot perform analysis assessing the predictive power of thevariables. In contrast to our findings for unselected ICUpatients, traumatic brain injury patients were found tohave higher long-term mortality than that seen for amatched general population cohort [29]. And, as in Fig. 2,prior studies have also found that the influence of age onlong-term mortality becomes extreme among the oldestpatients [3032].

We can more readily compare our model of 30-daymortality to existing studies of short-term mortality [3336]. Like our study, those that assessed the predictivepower of different variables all found that the strongestpredictors were the characteristics of the acute illness,with lesser contributions from age and comorbidity [3335]. In a multicenter study of ICU survivors admitted forinfections, Azoulay et al. [36] found determinants of in-hospital mortality that were similar to our 30-day mor-tality results.

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Our study has strengths and limitations. It is a large,

population-based study assessing consecutive, unselectedpatients admitted over a substantial time span to all typesof ICUs. Although we did not have detailed clinical datafrom one of the 12 ICUs in the province, this would not beexpected to substantially change the overall results, sincethat ICU accounts for only 6.5 % of all ICU bed-days inManitoba [37]. We evaluated a wide and robust range ofpotential determinants of mortality. This is the first studywith a methodology that permitted a clear delineation of

short-term from long-term influences on outcome, and it

did so in the same patient cohort. Two major limitationsare that our dataset did not allow us to address theprognosis for functional recovery, or the fact that someacute illnesses result in new chronic illnesses (e.g., majorstroke). The former is relevant because functional andquality of life outcomes are important influences onpatients treatment decisions [38], and are known to bedegraded among ICU survivors [39]. The latter is relevantbecause such new chronic illnesses may substantially

Table 2 Regression models of mortality after initial ICU admission for selected variables

Independent variable Death within 30 days after ICUadmission

Death[ 90 days after ICU admission for90-day survivors

OR 95 % CI HR 95 % CI

Age * (See Fig.2, text) * (See Fig.2, text)

APSminusNeuroa * (See eFigure 1 of the ESM,text)

* (See eFigure 1 of the ESM, text)

Female sex 0.956 0.887, 1.031 0.854* 0.815, 0.894Socioeconomic status * *Urban 1 (lowest urban) (reference) 1.000 1.000 Urban 5 (highest urban) 0.877 0.758, 1.016 0.719* 0.658, 0.787Rural 1 (lowest rural) 0.870 0.727, 1.042 0.974 0.875, 1.085Rural 5 (highest rural) 0.930 0.777, 1.113 0.785* 0.701, 0.879Not calculated 0.647* 0.523, 0.801 1.405* 1.255, 1.574

Comorbid conditionsMetastatic cancer 2.880* 2.453, 3.381 4.679* 4.205, 5.207Liver disease 2.475* 2.124, 2.884 1.715* 1.509, 1.950Other neurologic disorders 1.943* 1.728, 2.185 1.234* 1.120, 1.360Pulmonary circulation disorders 1.480* 1.240, 1.766 1.142* 1.007, 1.295Congestive heart failure 1.247* 1.140, 1.363 1.611* 1.532, 1.695Chronic pulmonary disease 1.132* 1.018, 1.257 1.510* 1.424, 1.602

Renal failure 1.079 0.934, 1.248 1.659* 1.516, 1.817Diabetes 0.986 0.901, 1.078 1.386* 1.317, 1.459Alcohol abuse 0.838* 0.714, 0.983 1.264* 1.135, 1.408Hypertension, uncomplicated 0.799* 0.734, 0.871 0.733* 0.680, 0.790

Year of ICU admission (reference: 19992002) * *20072009 0.878* 0.798, 0.966 0.705* 0.655, 0.759

Timing of ICU admission (reference: weekday daytime) * *Weekday evening 1.143* 1.049, 1.247 1.077* 1.022, 1.134Weekend daytime 1.154* 1.015, 1.313 1.086* 1.004, 1.175

Pre-ICU location (reference: emergency department) * *Operating room or post-anesthesia unit 0.474* 0.424, 0.530 0.693* 0.648, 0.740Ward 1.189 1.085, 1.301 1.043 0.980, 1.110

Admission diagnosis (reference: circulatory system) * *Digestive system 1.629* 1.361, 1.949 1.491* 1.330, 1.672Respiratory system 1.400* 1.237, 1.584 1.547* 1.432, 1.671Symptoms, signs, and ill-defined conditionsb 1.318* 1.180, 1.472 1.392* 1.287, 1.506

Endocrine, nutritional, metabolic, immunity 0.818 0.605, 1.105 1.227* 1.042, 1.445Genitourinary system 0.628* 0.444, 0.887 1.375* 1.145, 1.651

Glasgow Coma Scale score (reference: 15) * *912 1.983* 1.751, 2.246 1.183* 1.076, 1.30135 5.271* 4.619, 6.016 0.994 0.829, 1.192

Mechanical ventilation ICU day 1 and/or 2 1.415* 1.284, 1.560 0.991 0.938, 1.047Vasoactive drugs ICU day 1 and/or 2 1.201* 1.103, 1.308 0.952 0.904, 1.002Renal dialysis ICU day 1 and/or 2 1.157 0.994, 1.347 1.356* 1.221, 1.504

See eTable 2 of the ESM for full resultsORodds ratio, HR hazard ratio* p\ 0.05

a APACHE II Acute Physiology Score excluding its neurologicsubscoreb Includes sepsis-related diagnoses

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influence post-90-day survival. Thus, by limiting consid-eration to mortality outcomes, our analysis does notinclude the full range of personal values that patients usein making treatment decisions. Also, we only consideredeach patients initial ICU admission. Finally, some knowndeterminants of mortality were not present in our data,such as prehospital living site, prehospital functionalstatus, post-hospital discharge location [40], and bio-chemical markers [41].

Conclusions

We demonstrated that there are two distinct phases ofsurvival related to critical illness. Death in the monthafter admission to ICU was mainly determined by the

type and severity of acute illness, but this factor was lessimportant when examining mortality after 90 days ofsurvival. Age and comorbidity were only minor con-tributors to short-term survival. However, among thosewho survived to 90 days after illness onset, subsequentsurvival was primarily determined by comorbid condi-tions and age, as in the general population. Whilesurvival to 3090 days is not unimportant, existing at thetransition between the two clearcut phases, its determi-

nants will necessarily be a combination of those of theshort-term and long-term phasesit does not have itsown distinct determinants.

Acknowledgments This work was funded by a grant from theUniversity of Manitoba Research Grants Program.

Conflicts of interest On behalf of all authors, the correspondingauthor states that there is no conflict of interest.

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