Preadmission quality of life can predict mortality in ICU - a prospective cohort study Ramin I. Bukan, Ann M. Møller, Mattias A.S. Henning, Katrine B. Mortensen, Tobias W. Klausen, Tina Waldau PII: DOI: Reference:
S0883-9441(14)00240-8 doi: 10.1016/j.jcrc.2014.06.009 YJCRC 51552
To appear in:
Journal of Critical Care
Received date: Revised date: Accepted date:
15 April 2014 6 June 2014 10 June 2014
Please cite this article as: Bukan Ramin I., Møller Ann M., Henning Mattias A.S., Mortensen Katrine B., Klausen Tobias W., Waldau Tina, Preadmission quality of life can predict mortality in ICU - a prospective cohort study, Journal of Critical Care (2014), doi: 10.1016/j.jcrc.2014.06.009
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ACCEPTED MANUSCRIPT TITLE:
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Preadmission quality of life can predict mortality in ICU - a prospective cohort study
AUTHORS Ramin I. Bukan1
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Ann M. Møller1
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Mattias A.S. Henning1 Katrine B. Mortensen2
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Tobias W. Klausen3
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Tina Waldau1
INSTITUTIONAL AFFILIATIONS: 1Herlev
University Hospital, Department of anesthesiology I, Herlev Ringvej 75, 2730 Herlev,
2University
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Denmark
of Copenhagen, Faculty of Health and Medical Science, Blegdamsvej 3B, 2200
3Herlev
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Copenhagen N, Denmark
University Hospital, Clinical research department of hematology, Herlev Ringvej 75, 2730
Herlev, Denmark
E-MAIL ADRESSES: Ramin I. Bukan: [email protected] Ann M. Møller: [email protected] Mattias A.S. Henning: [email protected] Katrine B. Mortensen: [email protected]
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ACCEPTED MANUSCRIPT Tobias W. Klausen: [email protected]
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Tina Waldau: [email protected]
CORRESPONDING AUTHOR: E-mail address: [email protected]
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Telephone: 0045 30615391
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Address: Herlev University Hospital, Department of anesthesiology I, Herlev Ringvej 75, 2730
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Herlev, Denmark
CONFLICT OF INTEREST AND SOURCE OF FUNDING:
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The authors declare no conflict of interest and received no financial support.
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ACCEPTED MANUSCRIPT ABSTRACT:
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Purpose: We sought to investigate whether preadmission quality of life could act as a predictor of mortality among patients admitted to the intensive care unit (ICU).
Materials and Methods: A prospective observational study of all patients above the age of 18
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admitted to the ICU with a length of stay longer than 24 hours. Short form 36 (SF-36) and Acute Physiology and Chronic Health Evaluation II (APACHE II) were employed. Mortality was assessed
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during ICU admission, 30- and 90 days hereafter
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Results: We included 318 patients. No patients were lost to follow-up. Using the physical component summary (PCS) of Short form 12 (SF-12) as a predictor of ICU mortality, the AUC (0.70, CI 0.62-0.77) was comparable to that of APACHE II (0.74, CI 0.67-0.82). The difference
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between SF-12 and SF-36 was non-significant. Conclusions: Preadmission quality of life, assessed by SF-36 and SF-12, is as good at predicting ICU-, 30- and 90 days mortality as APACHE II in patients admitted to the ICU for longer than 24
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hours. This indicates that estimation of preadmission quality of life, potentially available in the pre-
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ICU setting, could aid decision-making regarding ICU admission, and deserves more attention by those caring for critically ill patients.
KEYWORDS:
Quality of Life Intensive Care Units Health Surveys Mortality
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ACCEPTED MANUSCRIPT INTRODUCTION Since the establishment of intensive therapy during the polio epidemic in the middle of the
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previous century demographic development has caused a steadily rising demand for intensive care, enhanced by the increasing complexity of the therapeutic options offered by modern medicine. Intensive care units (ICU) often have difficulties meeting the increasing requirements for
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intensive care. Several strategies for ICU admission exist; e.g. the prioritization model, the
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diagnosis model and the objective parameters model. The prioritization model, based on the expected benefit of admission, is the most used [1] Likewise; patient preferences regarding ICU
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admittance are often based on the probability of a positive outcome [2]. However, assessing whether a critically ill patient would benefit from intensive care remains a difficult task for the
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patient, the relatives and the physician [3]. This is aggravated further by the fact that intensive care treatment, rendered with no prospect of success, is an immense emotional burden for both patient and relatives and a socioeconomic burden for the society as well. Therefore, validated strategies
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that can aid physicians in admission decision-making are in demand. The ELDICUS study, a prospective observational multicenter study published in 2012,
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sought to develop a decision rule for intensive care units based on 28-day mortality rates of patients given or refused intensive care [4]. The 'triage score' included clinical, laboratory, and physiological variables and data from severity scores. The study provided data, which, if applied in a clinical setting, could help identify patients who would die even if admitted to the intensive care unit and others who would survive even if refused. The study identified performance status, measured by the Karnofsky Scale, as a significant variable. Notably, the ELDICUS study did not include variables concerning the perceived health-related quality of life (HRQOL) of patients. Theoretically, HRQOL reflects components of 'physiological reserve' and could, corresponding to performance status, act as a predictor of mortality [5].
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ACCEPTED MANUSCRIPT Short Form 36 (SF-36) is a generic standardised questionnaire employed in order to assess general HRQOL [6]. Welsh et al (1999)[7] and Hofhuis et al. (2007)[5] examined 6 weeks and 6
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months mortality as endpoints, and found that the questionnaire is as capable as the clinical rating scale Acute Physiology and Chronic Health Evaluation II (APACHE II) in predicting mortality. Though the results are interesting, it comes as no surprise that 'physiological reserve' can act as a
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predictor of mortality in the long run. Instead we hypothesize, that HRQOL, as a component of
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'physiological reserve', can function as a predictor of imminent death as well as long-term mortality. The aim of our study was to investigate whether preadmission quality of life, assessed by
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SF-36, could act as a predictor of mortality among patients admitted to the ICU, and hereby indicate
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whether the measurement could potentially aid decision-making regarding ICU admission .
MATERIALS AND METHODS
We consecutively screened all patients above the age of 18 admitted to the ICU of Herlev
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University Hospital in Denmark in the period of November 2011 to February 2013, with a length of stay longer than 24 hours. The department is a university-affiliated, 12 bed unit, with patients of
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mixed medical- and surgical origin. We asked patients to complete the SF-36 questionnaire based on their remembrance of health status four weeks prior to the current ICU admission. Close relatives completed the SF-36 questionnaire when patients were either unconscious or otherwise cognitively impaired. By means of either hand-outs or interviews the patients or relatives completed the SF-36 form in an average time of 15-20 minutes. Either the patient or the primary contact person gave informed consent. No interviews were performed until 24 hours after ICU admission. Exclusion criteria were ‘no informed consent’, ‘participation in randomised ICU study’, ‘SF-36 questionnaire not completed within 72 hours after ICU admission’, ‘no personal
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ACCEPTED MANUSCRIPT identification number’, ‘non-sufficient Danish language skills’ and ’prior inclusion due to readmission’.
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The ICU staff routinely measured the severity of illness by using APACHE II [8]. APACHE II is a severity-of-disease classification system, and is applied within 24 hours of admission. An integer score from 0 to 71 is computed based on several measurements (age, acute- and chronic
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physiology); higher scores correspond to more severe disease and a higher risk of death.
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Two interviewers attended the SF-36 completion. Short form 36 (SF-36v2®) is a generic standardised questionnaire employed in order to assess the general health related quality of life [9].
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The questionnaire consists of eight multi-item dimensions (physical functioning, role limitation due to physical problems, bodily pain, general health, vitality, social functioning, role limitation due to
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emotional problems and mental health) represented by 36 questions. We entered the replies into QualityMetric Software 4,5 (SF-36v2® Health Survey, 2008, QualityMetric Incorporated, Lincoln, USA, ISBN: 1-891180-17-0) in order to compute scores according to predefined guidelines. The
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better functioning level the higher score, ranging from 0 to 100. Short form 12 (SF-12) is a shorter version of the questionnaire, generated from the answers given to 12 questions, representing all
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eight dimensions of SF-36. The physical component summary (PCS) is a summary measure representing physical functioning. This study employed the Danish translation, conducted and validated by Bjorner et al [10]. The use of proxies in order to complete the SF-36 questionnaire is a validated approach [11, 12, 13]. The interviewers were blinded for the APACHE II score. The ICU staffs were blinded for the SF-36 score. Mortality was assessed during ICU admission (ICU mortality), 30 days after admittance to ICU (30 days mortality) and 90 days after admission (90 days mortality). The gathering, analysis and publication of data were approved by the Danish Data
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ACCEPTED MANUSCRIPT Protection Agency.
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Statistical analysis
Baseline characteristics were presented using median and range. Categorical variables were described with frequencies and percentages. When analysing ICU mortality, 30 days mortality and
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90 days mortality we used a logistic regression model due to the dichotomous outcome. Each
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variable, including age, sex, excessive alcohol consumption (> 7 drinks per week for women and > 14 drinks per week for men), medical or surgical background, APACHE II score, and the two SF-36
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and SF-12 scores, were analysed univariately.
In order to analyse whether the variables could act as predictors of death in patient
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subgroups, we constructed five models based on the results of the univariate logistic regression analysis, each predicting the three mortality measures. In model A we included APACHE II, age and gender. In model B we included SF-36 PCS, age and gender. In model C we included SF-36
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PCS, APACHE II, age and gender. In model D we included SF-12 PCS, age and gender. In model E we included SF-12 PCS, APACHE II, age and gender.
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To assess the ability to discriminate between survivors and non-survivors, the models were analysed using logistic regression with an 'enter method'. Hereafter, we drew receiver operating characteristic (ROC) curves using the model predictors, and calculated areas under the curves (AUC) including confidence intervals. We calculated classification tables using the model predictions as outcome, hereby assessing sensitivity of observed deaths, labelled by the model as predicted death, and specificity for a predicted death being an observed death. All p-values were two-sided and p-values below 0.05 were considered significant. SPSS version 19.0 (IBM corp., Armonk, NY, USA) and R version 3.0.1 (R Foundation for Statistical Computing, Vienna, Austria) were used for all calculations.
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ACCEPTED MANUSCRIPT RESULTS During the study period, we screened 464 patients, of whom we included 318; resulting in
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an inclusion rate of 68,5% (Figure 1). Of the included patients, 17% died during ICU admission (n = 55). Thirty days after ICU admission 86 patients had died (27%), and 90 days after the ICU
up. Baseline characteristics are illustrated in table 1.
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admission date a total of 111 patients were no longer alive (35%). No patients were lost to follow-
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Due to either unconsciousness or cognitive impairment preadmission quality of life was assessed by patients themselves in only 12% (n = 40). Proxies completed the residual SF-36 forms.
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The design of the prediction models was based on the significant findings of the univariate logistic regression analysis (Table 2). Because they proved non-significant when associated with
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ICU mortality, excessive alcohol consumption, reason for admission and the mental component summary (MCS) in SF-36 and SF-12 were not included in the prediction models. Using SF-12 PCS as a potential predictor of ICU mortality, the AUC for model D (AUC
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0.70, CI 0.62-0.77) was comparable to that of the APACHE II score (model A; AUC 0.74, CI 0.670.82), and only slightly better in model E (AUC 0.76, CI 0.70-0.83), in which the two factors were
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combined (Table 3, Figure 2).
Though SF-12 PCS tends to be slightly better at predicting ICU mortality than SF-36 PCS, the difference between the two is non-significant, also when regarding 30- and 90 days mortality (Table 3, Figure 3). Comparable results were obtained when calculating odds ratios and assessing SF-36 PCS and SF-12 as predictors of ICU mortality (Table 4). The sensitivity and specificity analysis shows, that there are no significant differences between the models (A-E), when assessing their ability to correctly identify patients that will survive ICU treatment and those who will not (Table 3).
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ACCEPTED MANUSCRIPT DISCUSSION Our results reveal that preadmission quality of life is as good at predicting ICU-, 30 days-
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and 90 days mortality as APACHE II in patients admitted to the ICU for longer than 24 hours. SF12 PCS, the physical component of the 12-item questions in the SF-36, was equal to the physical component of SF-36 in predicting mortality.
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Furthermore, due to the fact that incorporating preadmission quality of life into prediction
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models did not improve the predictive capacity of the established model APACHE II, we argue, that SF-36 should not aid APACHE II in assessing hospital mortality after ICU admission in clinical
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ICU practice.
APACHE II incorporates several physiological factors and is, with a prediction rate of AUC
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0.76%, only 'fair' at predicting hospital mortality in critically ill patients [14]. One could therefore argue, that APACHE II is not an ideal measurement to be used as a 'golden standard'. However, it is still the most accurate measurement employed in the daily clinical setting of our study, and
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therefore the most realistic measurement to compare with. In our study, both APACHE II and SF-36 were assessed 24 hours after ICU admission.
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However, contrary to APACHE II, which is only available 24 hours after ICU admission, assessment of preadmission quality of life by SF-36 is obtainable in a pre-ICU setting as soon as the patient or a proxy can be questioned, which is it an advantage if the measurement should aid ICU admission decision-making in future trials. However, assessing preadmission quality of life by SF36 is time-consuming (15-20 minutes) and may burden the patient or proxy with onerous questions. The SF-12 PCS, on the other hand, is calculated from 12 questions and therefore easier and less time-consuming to complete. SF-12 PCS, being equal to the physical component of SF-36 in predicting mortality, may therefore have a more favourable prospect in the pre-ICU setting. Several studies [5, 7, 15-22] address whether preadmission quality of life can act as an
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ACCEPTED MANUSCRIPT indicator of final outcome, but only a few studies [5, 7] focus on the association between SF-36 and patient mortality in an ICU setting.
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In accordance with our results, Welsh et al.[7] found that preadmission quality of life is associated with mortality in an ICU setting. Their cohort consisted of 199 patients admitted to an ICU longer than 48 hours, however, an inclusion rate of 9% suggests some selection bias. Foremost,
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sampling of subjects was performed on a ‘convenience basis’, and secondly, patients were not
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included if they had an expected ICU length of stay less than 48 hours. Furthermore, their results were not subjected to multivariate analysis, and might therefore be affected by unnoticed
and 6 months mortality instead.
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confounders. Additionally, the authors did not investigate death during ICU admission, but 6 weeks
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Hofhuis et al.[5] sought to determine whether SF-36 could predict mortality among patients admitted to the ICU. Their cohort consisted of 451 patients admitted to an ICU longer than 48 hours, with an inclusion rate at 21%. The authors found that the joined 36-score (MCS and PCS)
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and ‘general health’ were comparable to APACHE II when predicting 6-month mortality. Yet, in several aspects our study diverts from theirs. Our inclusion rate of 68,5% is higher
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than both Welsh et al. and Hofhuis et al. This may reflect differences in the inclusion process. Due to ethical concerns we chose not to screen any patients until 24 hours after ICU admission, giving the patient and proxies time to acclimatized to the ICU environment. However, even if patients were interviewed in the full period from ICU admission till 72 hours hereafter, the inclusion rate would still have been considerably higher than in the studies of Welsh et al. and Hofhuis et al (Figure 1). Unlike the study of Hofhuis at al., we found the physical component summary to be better than the mental component summary to predict mortality, and furthermore, SF-36 PCS proved just as good at predicting mortality as the joined SF-36 score (MCS and PCS) of Hofhuis et al. (AUC
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ACCEPTED MANUSCRIPT 0.76 and 0.77, respectively). The matter of the most appropriate component summary, MCS contra PCS, in the attempt to predict mortality in an ICU setting needs further investigation.
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Neither Welsh et al. nor Hofhuis et al. investigated ICU mortality, which is an important outcome when assessing whether HRQOL can function as a predictor of imminent death as well as long-term mortality, and hereby indicate if preadmission quality of life before ICU admission could
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potentially act as a contribution to decision-making regarding ICU admission in future trials.
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Though we conducted a long-term prospective study with a high inclusion rate, some aspects may be the focus of criticism.
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By means of hand-outs or interviews, patients or relatives completed the SF-36 form. The SF-36 is designed and validated as self-administered patient questionnaires. However, in our study
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the patients under study did not complete the majority of SF-36 forms. One could therefore argue that the information obtained may not accurately reflect the HRQOL of the patient. Even though the use of proxies in assessing HRQOL in an ICU setting has been validated [11, 12, 13], some studies
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performed in other patient populations have mixed results [23, 24]. Although the use of proxies may bias our results, we believe that this inclusion technique is the most appropriate in an ICU setting, in
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which patients are often mentally or physically incapable of completing a questionnaire unaided. Because it often is a delicate matter to ask patients and relatives to recollect in times of grief and shock, only patients with an ICU stay longer than 24 hours were asked whether they wanted to participate in the study, leaving them time to acclimatize to the ICU environment. The selection bias makes it rather difficult to postulate any definitive conclusions regarding preadmission quality of life as a predictor of death in ICU. However, it is worth mentioning that involvement of the relatives in acquiring information may ultimately be of help when difficult decisions need to be made, as the relatives may achieve an improved insight into the patient's condition and hence a better acceptance of the patient's outcome.
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ACCEPTED MANUSCRIPT The longer an inclusion period after ICU admission, the larger the risk of recall bias, in which memory of preadmission quality of life being is affected by the current ICU admission.
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However, with an inclusion period ending only 72 hours after ICU admission, our study does meet the risk of recall bias to some extent.
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CONCLUSIONS
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We found that preadmission quality of life can act as a predictor of mortality amongst patients admitted to ICU. Preadmission quality of life, assessed by SF-36 and SF-12, is as good at
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predicting ICU-, 30 days- and 90 days mortality as APACHE II in patients admitted to the ICU for longer than 24 hours.
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This indicates that estimation of preadmission quality of life, potentially available in the preICU setting, could aid decision-making regarding ICU admission, and deserves more attention by those caring for critically ill patients.
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However, even though preadmission quality of life could act as a contribution to decisionmaking, the models of our study have limited ability to predict mortality on an individual level.
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Therefore one should be careful of conveying our results to a clinical setting. Preadmission quality of life does not provide information regarding severity of disease and should not be the only aid when deciding whether a patient should be admitted to the ICU. However the measurement does provide an estimation of the patient's physiological reserve and could therefore potentially contribute to existing models concerning ICU admission. Also, an estimation of quality of life could improve the counselling of patient and relatives in times of critical illness. Though determining ICU admission criteria is a difficult and ethically problematic task, resent studies, such as the ELDICUS study, have sought to do just that. Based on the results of our study, we encourage future studies to include a SF-36 score in the information traditionally given at
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ACCEPTED MANUSCRIPT the time of decision-making regarding ICU admission, hereby examining whether the measurement
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could result in an altered ICU mortality rate.
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ACCEPTED MANUSCRIPT REFERENCES
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Figure 1 Flow diagram of patient selection and inclusion
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Figure Captions
Figure 2 Receiver operationg characteristic analysis of SF-12 and APACHE II scores in relation to ICU mortality
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Figure 3 Receiver operationg characteristic analysis of SF-36 and APACHE II scores in relation to
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ICU mortality
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Table 1 Patient demographics and clinical characteristics
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Table Legends
Table 2 Univariate logistic regression analysis of ICU mortality, 30 days mortality and 90 days mortality
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Table 3 Receiver operating characteristics and binary classification test performance of the
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mortality prediction models according to ICU mortality, 30 days mortality and 90 days mortality
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Table 4 Regression models for ICU mortality, 30 days mortality and 90 days mortality
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Fig. 1
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Fig. 2
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Fig. 3
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ACCEPTED MANUSCRIPT Table 1 Age, years, median
68 [18-91]
Female
154 (48.4)
Male
164 (51.6)
Excessive alcohol consumption 121 (38.1)
No
197 (61.9)
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Yes
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Gender
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Reason for admission Medical
238 (74.8) 74 (23.3)
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Surgery, acute Surgery, planned
6 (1.9) 25 [2-67]
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APACHE II score, median SF-36 score, median PCS MCS
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SF-12 PCS
45 [5-69]
38 [15-70] 47 [4-69]
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MCS
39 [17-70]
(Percent), [Range], Excessive alcohol consumption = > 7 drinks per week for women and > 14 drinks per week for men, APACHE II = Acute Physiology and Chronic Health Evaluation II, SF-36 = Short form 36, PCS = physical component summary, MCS = mental component summary, SF-12 = Short form 12.
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ACCEPTED MANUSCRIPT Table 2 ICU mortality
30 days mortality
90 days mortality
95% CI
P Value
OR
95% CI
P Value
OR
95% CI
P Value
Age
1.02
1.00-1.05
0.03
1.04
0.02-1.06
< 0.0001
1.05
1.03-1.08
< 0.0001
Gender*
1.76
0.97-3.28
0.06
1.41
0.85-2.34
0.17
1.52
0.96-2.44
0.08
0.87
0.45-1.63
0.67
0.77
0.44-1.31
0.33
0.77
0.46-1.27
0.31
0.80
0.38-1.57
0.53
0.86
0.47-1.52
0.61
0.99
0.58-1.67
0.98
1.08
1.05-1.12
< 0.0001
1.08
< 0.0001
1.08
1.05-1.11
< 0.0001
PCS
0.96
0.93-0.98
0.0009
MCS
1.00
0.98-1.02
0.91
PCS
0.95
0.93-0.98
0.0007
MCS
1.00
0.98-1.02
0.97
Excessive alcohol
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consumption** Reason for
APACHEII
SF-12
0.94
0.91-0.96
< 0.0001
0.94
0.92-0.96
< 0.0001
0.99
0.98-1.01
0.44
0.99
0.97-1.01
0.27
0.94
0.91-0.96
< 0.0001
0.94
0.92-0.97
< 0.0001
1.00
0.98-1.01
0.70
0.99
0.97-1.01
0.33
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SF-36
1.05-1.12
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admission***
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OR
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Variable
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ICU = intensive care unit, OR = odds ratio, CI = confidence interval, *’Female’ reference, **’No’ reference, ***’Medical’ reference, Excessive alcohol consumption = > 7 drinks per week for women and > 14 drinks per week for men, APACHE II = Acute Physiology and Chronic Health
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Evaluation II, SF-36 = Short form 36, PCS = physical component summary, MCS = mental component summary, SF-12 = Short form 12.
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ACCEPTED MANUSCRIPT Table 3 AUC
Sensitivity
Specificity
(95% CI)
(95% CI)
(95% CI)
NPV
Accuracy
Model Aa
0.74 (0.67-0.82)
0.04 (0.00-0.13)
0.98 (0.96-0.99)
0.29
0.83
0.82
Model Bb
0.69 (0.62-0.76)
0.00 (0.00-0.07)
1.00 (0.98-1.00)
0.00
0.83
0.82
Model Cc
0.76 (0.69-0.83)
0.09 (0.03-0.20)
0.98 (0.96-1.00)
0.56
0.84
0.83
Model Dd,*
0.70 (0.62-0.77)
-
1.00 (0.98-1.00)
-
0.83
0.83
Model Ee
0.76 (0.70-0.83)
0.11 (0.04-0.23)
0.98 (0.96-1.00)
0.60
0.84
0.83
Model Aa
0.74 (0.68-0.80)
0.21 (0.13-0.31)
0.95 (0.91-0.97)
0.60
0.76
0.75
Model Bb
0.73 (0.67-0.79)
0.21 (0.13-0.31)
0.92 (0.87-0.95)
0.49
0.76
0.73
Model Cc
0.78 (0.73-0.84)
0.34 (0.24-0.45)
0.92 (0.88-0.95)
0.62
0.79
0.76
Model Dd
0.74 (0.68-0.80)
0.17 (0.10-0.27)
0.93 (0.89-0.96)
0.48
0.75
0.73
Model Ee
0.79 (0.73-0.84)
0.35 (0.25-0.46)
0.93 (0.89-0.96)
0.66
0.79
0.77
0.75 (0.69-0.81)
0.41 (0.32-0.51)
0.83 (0.77-0.88)
0.57
0.72
0.68
0.75 (0.69-0.80)
0.49 (0.39-0.59)
0.87 (0.82-0.91)
0.68
0.76
0.74
0.78 (0.73-0.84)
0.51 (0.41-0.61)
0.85 (0.79-0.90)
0.65
0.76
0.73
0.75 (0.69-0.80)
0.47 (0.38-0.57)
0.88 (0.83-0.92)
0.69
0.75
0.74
0.78 (0.73-0.84)
0.47 (0.38-0.57)
0.86 (0.80-0.90)
0.65
0.75
0.72
Model Cc Model Dd Model Ee
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Model Bb
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Model Aa
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30 days mortality
90 days mortality
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ICU mortality
PPV
AUC = area under the curve, CI = confidence interval, PPV = positive predictive value, NPV = negative predictive value, ICU = intensive care unit, a
Model A: APACHE II, age and gender, bModel B: SF-36 PCS, age and gender, cModel C: SF-36 PCS, APACHE II, age and gender, dModel D: SF-
12 PCS, age and gender, *the model does not predict mortality, eModel E: SF-12 PCS, APACHE II, age and gender, APACHE II = Acute Physiology and Chronic Health Evaluation II, SF-36 = Short form 36, PCS = physical component summary.
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ACCEPTED MANUSCRIPT Table 4 ICU mortality
30 days mortality
90 days mortality
OR
95% CI
P Value
OR
95% CI
95% CI
P Value
Age
1.02
0.99-1.04
0.17
1.04
1.01-1.06
0.001
1.05
1.03-1.07
< 0.0001
Gender*
1.87
1.00-3.60
0.05
1.51
0.88-2.59
0.14
1.70
1.02-2.85
0.04
APACHE II
1.08
1.05-1.11
< 0.0001
1.08
1.05-1.11
< 0.0001
1.07
1.04-1.11
< 0.0001
Age
1.02
1.00-1.05
0.09
1.04
1.02-1.06
0.0006
1.05
1.03-1.08
< 0.0001
Gender*
2.10
1.10-4.00
0.02
1.88
1.09-3.26
0.02
2.14
1.27-3.63
0.004
SF-36 PCS
0.96
0.93-0.98
0.002
0.94
0.91-0.96
< 0.0001
0.94
0.92-0.97
< 0.0001
Age
1.02
0.99-1.04
0.22
1.04
1.01-1.06
0.003
1.05
1.03-1.07
< 0.0001
Gender*
2.12
1.10-4.08
0.02
1.84
1.04-3.25
0.03
2.06
1.20-3.54
0.01
APACHE II
1.07
1.04-1.11
< 0.0001
1.07
1.03-1.10
< 0.0001
1.06
1.03-1.10
< 0.0001
SF-36 PCS
0.97
0.94-1.00
0.02
0.95
0.92-0.97
< 0.0001
0.95
0.93-0.98
< 0.0001
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Model D
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Model C
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Model B
OR
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Model A
P Value
1.02
1.00-1.05
0.10
1.04
1.01-1.06
0.001
1.05
1.03-1.07
< 0.0001
Gender*
2.18
1.16-4.13
0.02
1.81
1.04-3.14
0.04
2.01
1.19-3.41
0.01
0.95
0.92-0.98
0.001
0.94
0.91-0.96
< 0.0001
0.94
0.92-0.97
< 0.0001
Age
1.02
0.99-1.04
0.27
1.03
1.01-1.06
0.01
1.05
1.02-1.07
< 0.0001
Gender*
2.12
1.09-4.13
0.02
1.71
0.06-3.04
0.07
1.87
1.09-3.23
0.02
APACHE II
1.08
1.04-1.11
< 0.0001
1.07
1.04-1.11
< 0.0001
1.07
1.04-1.10
< 0.0001
SF-12 PCS
0.96
0.93-0.99
0.01
0.94
0.92-0.97
< 0.0001
0.95
0.93-0.98
< 0.0002
SF-12 PCS Model E
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Age
ICU = intensive care unit, OR = odds ratio, CI = confidence interval, *’Female’ reference, APACHE II = Acute Physiology and Chronic Health Evaluation II, SF-36 = Short form 36, PCS = physical component summary, SF-12 = Short form 12.
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S0883-9441(14)00240-8 doi: 10.1016/j.jcrc.2014.06.009 YJCRC 51552
To appear in:
Journal of Critical Care
Received date: Revised date: Accepted date:
15 April 2014 6 June 2014 10 June 2014
Please cite this article as: Bukan Ramin I., Møller Ann M., Henning Mattias A.S., Mortensen Katrine B., Klausen Tobias W., Waldau Tina, Preadmission quality of life can predict mortality in ICU - a prospective cohort study, Journal of Critical Care (2014), doi: 10.1016/j.jcrc.2014.06.009
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT TITLE:
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Preadmission quality of life can predict mortality in ICU - a prospective cohort study
AUTHORS Ramin I. Bukan1
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Ann M. Møller1
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Mattias A.S. Henning1 Katrine B. Mortensen2
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Tobias W. Klausen3
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Tina Waldau1
INSTITUTIONAL AFFILIATIONS: 1Herlev
University Hospital, Department of anesthesiology I, Herlev Ringvej 75, 2730 Herlev,
2University
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Denmark
of Copenhagen, Faculty of Health and Medical Science, Blegdamsvej 3B, 2200
3Herlev
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Copenhagen N, Denmark
University Hospital, Clinical research department of hematology, Herlev Ringvej 75, 2730
Herlev, Denmark
E-MAIL ADRESSES: Ramin I. Bukan: [email protected] Ann M. Møller: [email protected] Mattias A.S. Henning: [email protected] Katrine B. Mortensen: [email protected]
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ACCEPTED MANUSCRIPT Tobias W. Klausen: [email protected]
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Tina Waldau: [email protected]
CORRESPONDING AUTHOR: E-mail address: [email protected]
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Telephone: 0045 30615391
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Address: Herlev University Hospital, Department of anesthesiology I, Herlev Ringvej 75, 2730
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Herlev, Denmark
CONFLICT OF INTEREST AND SOURCE OF FUNDING:
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The authors declare no conflict of interest and received no financial support.
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ACCEPTED MANUSCRIPT ABSTRACT:
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Purpose: We sought to investigate whether preadmission quality of life could act as a predictor of mortality among patients admitted to the intensive care unit (ICU).
Materials and Methods: A prospective observational study of all patients above the age of 18
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admitted to the ICU with a length of stay longer than 24 hours. Short form 36 (SF-36) and Acute Physiology and Chronic Health Evaluation II (APACHE II) were employed. Mortality was assessed
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during ICU admission, 30- and 90 days hereafter
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Results: We included 318 patients. No patients were lost to follow-up. Using the physical component summary (PCS) of Short form 12 (SF-12) as a predictor of ICU mortality, the AUC (0.70, CI 0.62-0.77) was comparable to that of APACHE II (0.74, CI 0.67-0.82). The difference
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between SF-12 and SF-36 was non-significant. Conclusions: Preadmission quality of life, assessed by SF-36 and SF-12, is as good at predicting ICU-, 30- and 90 days mortality as APACHE II in patients admitted to the ICU for longer than 24
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hours. This indicates that estimation of preadmission quality of life, potentially available in the pre-
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ICU setting, could aid decision-making regarding ICU admission, and deserves more attention by those caring for critically ill patients.
KEYWORDS:
Quality of Life Intensive Care Units Health Surveys Mortality
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ACCEPTED MANUSCRIPT INTRODUCTION Since the establishment of intensive therapy during the polio epidemic in the middle of the
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previous century demographic development has caused a steadily rising demand for intensive care, enhanced by the increasing complexity of the therapeutic options offered by modern medicine. Intensive care units (ICU) often have difficulties meeting the increasing requirements for
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intensive care. Several strategies for ICU admission exist; e.g. the prioritization model, the
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diagnosis model and the objective parameters model. The prioritization model, based on the expected benefit of admission, is the most used [1] Likewise; patient preferences regarding ICU
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admittance are often based on the probability of a positive outcome [2]. However, assessing whether a critically ill patient would benefit from intensive care remains a difficult task for the
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patient, the relatives and the physician [3]. This is aggravated further by the fact that intensive care treatment, rendered with no prospect of success, is an immense emotional burden for both patient and relatives and a socioeconomic burden for the society as well. Therefore, validated strategies
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that can aid physicians in admission decision-making are in demand. The ELDICUS study, a prospective observational multicenter study published in 2012,
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sought to develop a decision rule for intensive care units based on 28-day mortality rates of patients given or refused intensive care [4]. The 'triage score' included clinical, laboratory, and physiological variables and data from severity scores. The study provided data, which, if applied in a clinical setting, could help identify patients who would die even if admitted to the intensive care unit and others who would survive even if refused. The study identified performance status, measured by the Karnofsky Scale, as a significant variable. Notably, the ELDICUS study did not include variables concerning the perceived health-related quality of life (HRQOL) of patients. Theoretically, HRQOL reflects components of 'physiological reserve' and could, corresponding to performance status, act as a predictor of mortality [5].
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ACCEPTED MANUSCRIPT Short Form 36 (SF-36) is a generic standardised questionnaire employed in order to assess general HRQOL [6]. Welsh et al (1999)[7] and Hofhuis et al. (2007)[5] examined 6 weeks and 6
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months mortality as endpoints, and found that the questionnaire is as capable as the clinical rating scale Acute Physiology and Chronic Health Evaluation II (APACHE II) in predicting mortality. Though the results are interesting, it comes as no surprise that 'physiological reserve' can act as a
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predictor of mortality in the long run. Instead we hypothesize, that HRQOL, as a component of
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'physiological reserve', can function as a predictor of imminent death as well as long-term mortality. The aim of our study was to investigate whether preadmission quality of life, assessed by
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SF-36, could act as a predictor of mortality among patients admitted to the ICU, and hereby indicate
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whether the measurement could potentially aid decision-making regarding ICU admission .
MATERIALS AND METHODS
We consecutively screened all patients above the age of 18 admitted to the ICU of Herlev
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University Hospital in Denmark in the period of November 2011 to February 2013, with a length of stay longer than 24 hours. The department is a university-affiliated, 12 bed unit, with patients of
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mixed medical- and surgical origin. We asked patients to complete the SF-36 questionnaire based on their remembrance of health status four weeks prior to the current ICU admission. Close relatives completed the SF-36 questionnaire when patients were either unconscious or otherwise cognitively impaired. By means of either hand-outs or interviews the patients or relatives completed the SF-36 form in an average time of 15-20 minutes. Either the patient or the primary contact person gave informed consent. No interviews were performed until 24 hours after ICU admission. Exclusion criteria were ‘no informed consent’, ‘participation in randomised ICU study’, ‘SF-36 questionnaire not completed within 72 hours after ICU admission’, ‘no personal
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ACCEPTED MANUSCRIPT identification number’, ‘non-sufficient Danish language skills’ and ’prior inclusion due to readmission’.
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The ICU staff routinely measured the severity of illness by using APACHE II [8]. APACHE II is a severity-of-disease classification system, and is applied within 24 hours of admission. An integer score from 0 to 71 is computed based on several measurements (age, acute- and chronic
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physiology); higher scores correspond to more severe disease and a higher risk of death.
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Two interviewers attended the SF-36 completion. Short form 36 (SF-36v2®) is a generic standardised questionnaire employed in order to assess the general health related quality of life [9].
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The questionnaire consists of eight multi-item dimensions (physical functioning, role limitation due to physical problems, bodily pain, general health, vitality, social functioning, role limitation due to
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emotional problems and mental health) represented by 36 questions. We entered the replies into QualityMetric Software 4,5 (SF-36v2® Health Survey, 2008, QualityMetric Incorporated, Lincoln, USA, ISBN: 1-891180-17-0) in order to compute scores according to predefined guidelines. The
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better functioning level the higher score, ranging from 0 to 100. Short form 12 (SF-12) is a shorter version of the questionnaire, generated from the answers given to 12 questions, representing all
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eight dimensions of SF-36. The physical component summary (PCS) is a summary measure representing physical functioning. This study employed the Danish translation, conducted and validated by Bjorner et al [10]. The use of proxies in order to complete the SF-36 questionnaire is a validated approach [11, 12, 13]. The interviewers were blinded for the APACHE II score. The ICU staffs were blinded for the SF-36 score. Mortality was assessed during ICU admission (ICU mortality), 30 days after admittance to ICU (30 days mortality) and 90 days after admission (90 days mortality). The gathering, analysis and publication of data were approved by the Danish Data
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ACCEPTED MANUSCRIPT Protection Agency.
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Statistical analysis
Baseline characteristics were presented using median and range. Categorical variables were described with frequencies and percentages. When analysing ICU mortality, 30 days mortality and
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90 days mortality we used a logistic regression model due to the dichotomous outcome. Each
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variable, including age, sex, excessive alcohol consumption (> 7 drinks per week for women and > 14 drinks per week for men), medical or surgical background, APACHE II score, and the two SF-36
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and SF-12 scores, were analysed univariately.
In order to analyse whether the variables could act as predictors of death in patient
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subgroups, we constructed five models based on the results of the univariate logistic regression analysis, each predicting the three mortality measures. In model A we included APACHE II, age and gender. In model B we included SF-36 PCS, age and gender. In model C we included SF-36
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PCS, APACHE II, age and gender. In model D we included SF-12 PCS, age and gender. In model E we included SF-12 PCS, APACHE II, age and gender.
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To assess the ability to discriminate between survivors and non-survivors, the models were analysed using logistic regression with an 'enter method'. Hereafter, we drew receiver operating characteristic (ROC) curves using the model predictors, and calculated areas under the curves (AUC) including confidence intervals. We calculated classification tables using the model predictions as outcome, hereby assessing sensitivity of observed deaths, labelled by the model as predicted death, and specificity for a predicted death being an observed death. All p-values were two-sided and p-values below 0.05 were considered significant. SPSS version 19.0 (IBM corp., Armonk, NY, USA) and R version 3.0.1 (R Foundation for Statistical Computing, Vienna, Austria) were used for all calculations.
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ACCEPTED MANUSCRIPT RESULTS During the study period, we screened 464 patients, of whom we included 318; resulting in
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an inclusion rate of 68,5% (Figure 1). Of the included patients, 17% died during ICU admission (n = 55). Thirty days after ICU admission 86 patients had died (27%), and 90 days after the ICU
up. Baseline characteristics are illustrated in table 1.
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admission date a total of 111 patients were no longer alive (35%). No patients were lost to follow-
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Due to either unconsciousness or cognitive impairment preadmission quality of life was assessed by patients themselves in only 12% (n = 40). Proxies completed the residual SF-36 forms.
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The design of the prediction models was based on the significant findings of the univariate logistic regression analysis (Table 2). Because they proved non-significant when associated with
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ICU mortality, excessive alcohol consumption, reason for admission and the mental component summary (MCS) in SF-36 and SF-12 were not included in the prediction models. Using SF-12 PCS as a potential predictor of ICU mortality, the AUC for model D (AUC
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0.70, CI 0.62-0.77) was comparable to that of the APACHE II score (model A; AUC 0.74, CI 0.670.82), and only slightly better in model E (AUC 0.76, CI 0.70-0.83), in which the two factors were
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combined (Table 3, Figure 2).
Though SF-12 PCS tends to be slightly better at predicting ICU mortality than SF-36 PCS, the difference between the two is non-significant, also when regarding 30- and 90 days mortality (Table 3, Figure 3). Comparable results were obtained when calculating odds ratios and assessing SF-36 PCS and SF-12 as predictors of ICU mortality (Table 4). The sensitivity and specificity analysis shows, that there are no significant differences between the models (A-E), when assessing their ability to correctly identify patients that will survive ICU treatment and those who will not (Table 3).
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ACCEPTED MANUSCRIPT DISCUSSION Our results reveal that preadmission quality of life is as good at predicting ICU-, 30 days-
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and 90 days mortality as APACHE II in patients admitted to the ICU for longer than 24 hours. SF12 PCS, the physical component of the 12-item questions in the SF-36, was equal to the physical component of SF-36 in predicting mortality.
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Furthermore, due to the fact that incorporating preadmission quality of life into prediction
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models did not improve the predictive capacity of the established model APACHE II, we argue, that SF-36 should not aid APACHE II in assessing hospital mortality after ICU admission in clinical
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ICU practice.
APACHE II incorporates several physiological factors and is, with a prediction rate of AUC
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0.76%, only 'fair' at predicting hospital mortality in critically ill patients [14]. One could therefore argue, that APACHE II is not an ideal measurement to be used as a 'golden standard'. However, it is still the most accurate measurement employed in the daily clinical setting of our study, and
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therefore the most realistic measurement to compare with. In our study, both APACHE II and SF-36 were assessed 24 hours after ICU admission.
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However, contrary to APACHE II, which is only available 24 hours after ICU admission, assessment of preadmission quality of life by SF-36 is obtainable in a pre-ICU setting as soon as the patient or a proxy can be questioned, which is it an advantage if the measurement should aid ICU admission decision-making in future trials. However, assessing preadmission quality of life by SF36 is time-consuming (15-20 minutes) and may burden the patient or proxy with onerous questions. The SF-12 PCS, on the other hand, is calculated from 12 questions and therefore easier and less time-consuming to complete. SF-12 PCS, being equal to the physical component of SF-36 in predicting mortality, may therefore have a more favourable prospect in the pre-ICU setting. Several studies [5, 7, 15-22] address whether preadmission quality of life can act as an
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ACCEPTED MANUSCRIPT indicator of final outcome, but only a few studies [5, 7] focus on the association between SF-36 and patient mortality in an ICU setting.
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In accordance with our results, Welsh et al.[7] found that preadmission quality of life is associated with mortality in an ICU setting. Their cohort consisted of 199 patients admitted to an ICU longer than 48 hours, however, an inclusion rate of 9% suggests some selection bias. Foremost,
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sampling of subjects was performed on a ‘convenience basis’, and secondly, patients were not
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included if they had an expected ICU length of stay less than 48 hours. Furthermore, their results were not subjected to multivariate analysis, and might therefore be affected by unnoticed
and 6 months mortality instead.
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confounders. Additionally, the authors did not investigate death during ICU admission, but 6 weeks
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Hofhuis et al.[5] sought to determine whether SF-36 could predict mortality among patients admitted to the ICU. Their cohort consisted of 451 patients admitted to an ICU longer than 48 hours, with an inclusion rate at 21%. The authors found that the joined 36-score (MCS and PCS)
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and ‘general health’ were comparable to APACHE II when predicting 6-month mortality. Yet, in several aspects our study diverts from theirs. Our inclusion rate of 68,5% is higher
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than both Welsh et al. and Hofhuis et al. This may reflect differences in the inclusion process. Due to ethical concerns we chose not to screen any patients until 24 hours after ICU admission, giving the patient and proxies time to acclimatized to the ICU environment. However, even if patients were interviewed in the full period from ICU admission till 72 hours hereafter, the inclusion rate would still have been considerably higher than in the studies of Welsh et al. and Hofhuis et al (Figure 1). Unlike the study of Hofhuis at al., we found the physical component summary to be better than the mental component summary to predict mortality, and furthermore, SF-36 PCS proved just as good at predicting mortality as the joined SF-36 score (MCS and PCS) of Hofhuis et al. (AUC
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ACCEPTED MANUSCRIPT 0.76 and 0.77, respectively). The matter of the most appropriate component summary, MCS contra PCS, in the attempt to predict mortality in an ICU setting needs further investigation.
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Neither Welsh et al. nor Hofhuis et al. investigated ICU mortality, which is an important outcome when assessing whether HRQOL can function as a predictor of imminent death as well as long-term mortality, and hereby indicate if preadmission quality of life before ICU admission could
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potentially act as a contribution to decision-making regarding ICU admission in future trials.
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Though we conducted a long-term prospective study with a high inclusion rate, some aspects may be the focus of criticism.
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By means of hand-outs or interviews, patients or relatives completed the SF-36 form. The SF-36 is designed and validated as self-administered patient questionnaires. However, in our study
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the patients under study did not complete the majority of SF-36 forms. One could therefore argue that the information obtained may not accurately reflect the HRQOL of the patient. Even though the use of proxies in assessing HRQOL in an ICU setting has been validated [11, 12, 13], some studies
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performed in other patient populations have mixed results [23, 24]. Although the use of proxies may bias our results, we believe that this inclusion technique is the most appropriate in an ICU setting, in
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which patients are often mentally or physically incapable of completing a questionnaire unaided. Because it often is a delicate matter to ask patients and relatives to recollect in times of grief and shock, only patients with an ICU stay longer than 24 hours were asked whether they wanted to participate in the study, leaving them time to acclimatize to the ICU environment. The selection bias makes it rather difficult to postulate any definitive conclusions regarding preadmission quality of life as a predictor of death in ICU. However, it is worth mentioning that involvement of the relatives in acquiring information may ultimately be of help when difficult decisions need to be made, as the relatives may achieve an improved insight into the patient's condition and hence a better acceptance of the patient's outcome.
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ACCEPTED MANUSCRIPT The longer an inclusion period after ICU admission, the larger the risk of recall bias, in which memory of preadmission quality of life being is affected by the current ICU admission.
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However, with an inclusion period ending only 72 hours after ICU admission, our study does meet the risk of recall bias to some extent.
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CONCLUSIONS
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We found that preadmission quality of life can act as a predictor of mortality amongst patients admitted to ICU. Preadmission quality of life, assessed by SF-36 and SF-12, is as good at
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predicting ICU-, 30 days- and 90 days mortality as APACHE II in patients admitted to the ICU for longer than 24 hours.
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This indicates that estimation of preadmission quality of life, potentially available in the preICU setting, could aid decision-making regarding ICU admission, and deserves more attention by those caring for critically ill patients.
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However, even though preadmission quality of life could act as a contribution to decisionmaking, the models of our study have limited ability to predict mortality on an individual level.
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Therefore one should be careful of conveying our results to a clinical setting. Preadmission quality of life does not provide information regarding severity of disease and should not be the only aid when deciding whether a patient should be admitted to the ICU. However the measurement does provide an estimation of the patient's physiological reserve and could therefore potentially contribute to existing models concerning ICU admission. Also, an estimation of quality of life could improve the counselling of patient and relatives in times of critical illness. Though determining ICU admission criteria is a difficult and ethically problematic task, resent studies, such as the ELDICUS study, have sought to do just that. Based on the results of our study, we encourage future studies to include a SF-36 score in the information traditionally given at
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ACCEPTED MANUSCRIPT the time of decision-making regarding ICU admission, hereby examining whether the measurement
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could result in an altered ICU mortality rate.
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ACCEPTED MANUSCRIPT REFERENCES
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[1] Egol A, Fromm R, Guntupalli KK, et al. Guidelines for intensive care unit admission, discharge, and triage. Task Force of the American College of Critical Care Medicine, Society of Critical Care Medicine. Crit Care Med 1999;27:633-638.
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seriously ill patients. Engl J Med 2002;346:1061-1066.
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[2] Fried TR, Bradley EH, Towle VR, Allore HN. Understanding the treatment preferences of
[3] Knaus WA, Wagner DP, Zimmerman JE, Draper EA. Variations in mortality and length of stay
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in intensive care units. Ann Intern Med 1993;118:753-761.
[4] Sprung CL, Baras M, Iapichino G, et al. The Eldicus prospective, observational study of triage
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decision making in European intensive care units: part I - European Intensive Care Admission Triage Scores. Crit Care Med 2012;40:125-131. [5] Hofhuis JG, Spronk PE, van Stel HF, Schrijvers AJ, Bakker J. Quality of life before intensive
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care unit admission is a predictor of survival. Crit Care 2007;11:78-79. [6] Ware JE, Kosinski M, Bayliss MS, McHorney CA, Rogers WH, Raczek AH. Comparison of
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methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. Med Care 1995;33:264-279. [7] Welsh CH, Thompson KRN, Long-Krug S. Evaluation of patient-perceived health status using the Medical Outcomes Survey Short-Form 36 in an intensive care unit population. Crit Care Med 1999;27:1466-1471. [8] Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med 1985;13:818-829. [9] Ware JE Jr, Kosinski M, Bjorner JB, Turner-Bowker DM, Gandek B, Maruish ME. SF-36v2
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Health Survey: Administration guide for clinical trial investigators. Lincoln: QualityMetric
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ACCEPTED MANUSCRIPT Incorporated; 2008. [10] Bjorner JB, Damsgaard MT, Watt T, Groenvold M. Tests of data quality, scaling assumptions,
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and reliability of the Danish SF-36. J Clin Epidemiol 1998;51:1001-1011.
[11] Capuzzo M, Grasselli C, Carrer S, Gritti G, Alvisi R. Quality of life before intensive care admission: agreement between patient and relative assessment. Intensive Care Med 2000;26:1288-
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[12] Rogers J, Ridley S, Chrispin P, Scotton H, Lloyd D. Reliability of the next of kins' estimates of critically ill patients' quality of life. Anaesthesia 1997;52:1137-1143.
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[13] Hofhuis J, Hautvast JL, Schrijvers AJ, Bakker J. Quality of life on admission to the intensive care: can we query the relatives? Intensive Care Med 2003;29:974-979.
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[14] Livingston BM, MacKirdy FN, Howie JC, Jones R, Norrie JD. Assessment of the performance of five intensive care scoring models within a large Scottish database. Crit Care Med 2000;28:18201827.
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[15] DeOreo PB. Hemodialysis patient-assessed functional health status predicts continued survival, hospitalization, and dialysis-attendance compliance. Am J Kidney Dis 1997;30:204-212.
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[16] Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. Association among SF36 quality of life measures and nutrition, hospitalization, and mortality in hemodialysis. J Am Soc Nephrol 2001;12:2797-2806. [17] Lowrie EG, Curtin RB, LePain N, Schatell D. Medical outcomes study short form-36: a consistent and powerful predictor of morbidity and mortality in dialysis patients. Am J Kidney Dis 2003;41:1286-1292. [18] Rumsfeld JS, MaWhinney S, McCarthy M Jr, et al. Health-related quality of life as a predictor of mortality following coronary artery bypass graft surgery. Participants of the Department of Veterans Affairs Cooperative Study Group on Processes, Structures, and Outcomes of Care in
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hospitalization in 5,025 patients with congestive heart failure. SOLVD Investigations. Studies of Left Ventricular Dysfunction Investigators. Am J Cardiol 1996;78:890-895.
[20] Maisey NR, Norman A, Watson M, Allen MJ, Hill ME, Cunningham D. Baseline quality of
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life predicts survival in patients with advanced colorectal cancer. Eur J Cancer 2002;38:1351-1357.
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[21] Yinnon A, Zimran A, Hershko C. Quality of life and survival following intensive medical care. QJ Med 1989;71:347-357.
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[22] Rivera-Fernandez R, Sanchez-Cruz JJ, Abizanda-Campos R, Vazquez-Mata G. Quality of life before intensive care unit admission and its influence on resource utilization and mortality rate. Crit
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[23] Pierre U, Wood-Dauphinee S, Korner-Bitensky N, Gayton D, Hanley J. Proxy use of the Canadian SF-36 in rating health status of the disabled elderly. J Clin Epidemiol 1998;51:983-990.
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[24] McPherson CJ, Addington-Hall JM. Judging the quality of care at the end of life: can proxies
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Figure 1 Flow diagram of patient selection and inclusion
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Figure Captions
Figure 2 Receiver operationg characteristic analysis of SF-12 and APACHE II scores in relation to ICU mortality
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Figure 3 Receiver operationg characteristic analysis of SF-36 and APACHE II scores in relation to
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ICU mortality
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Table 1 Patient demographics and clinical characteristics
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Table Legends
Table 2 Univariate logistic regression analysis of ICU mortality, 30 days mortality and 90 days mortality
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Table 3 Receiver operating characteristics and binary classification test performance of the
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mortality prediction models according to ICU mortality, 30 days mortality and 90 days mortality
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Table 4 Regression models for ICU mortality, 30 days mortality and 90 days mortality
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Fig. 2
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Fig. 3
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ACCEPTED MANUSCRIPT Table 1 Age, years, median
68 [18-91]
Female
154 (48.4)
Male
164 (51.6)
Excessive alcohol consumption 121 (38.1)
No
197 (61.9)
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Yes
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Gender
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Reason for admission Medical
238 (74.8) 74 (23.3)
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Surgery, acute Surgery, planned
6 (1.9) 25 [2-67]
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APACHE II score, median SF-36 score, median PCS MCS
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SF-12 PCS
45 [5-69]
38 [15-70] 47 [4-69]
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MCS
39 [17-70]
(Percent), [Range], Excessive alcohol consumption = > 7 drinks per week for women and > 14 drinks per week for men, APACHE II = Acute Physiology and Chronic Health Evaluation II, SF-36 = Short form 36, PCS = physical component summary, MCS = mental component summary, SF-12 = Short form 12.
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ACCEPTED MANUSCRIPT Table 2 ICU mortality
30 days mortality
90 days mortality
95% CI
P Value
OR
95% CI
P Value
OR
95% CI
P Value
Age
1.02
1.00-1.05
0.03
1.04
0.02-1.06
< 0.0001
1.05
1.03-1.08
< 0.0001
Gender*
1.76
0.97-3.28
0.06
1.41
0.85-2.34
0.17
1.52
0.96-2.44
0.08
0.87
0.45-1.63
0.67
0.77
0.44-1.31
0.33
0.77
0.46-1.27
0.31
0.80
0.38-1.57
0.53
0.86
0.47-1.52
0.61
0.99
0.58-1.67
0.98
1.08
1.05-1.12
< 0.0001
1.08
< 0.0001
1.08
1.05-1.11
< 0.0001
PCS
0.96
0.93-0.98
0.0009
MCS
1.00
0.98-1.02
0.91
PCS
0.95
0.93-0.98
0.0007
MCS
1.00
0.98-1.02
0.97
Excessive alcohol
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consumption** Reason for
APACHEII
SF-12
0.94
0.91-0.96
< 0.0001
0.94
0.92-0.96
< 0.0001
0.99
0.98-1.01
0.44
0.99
0.97-1.01
0.27
0.94
0.91-0.96
< 0.0001
0.94
0.92-0.97
< 0.0001
1.00
0.98-1.01
0.70
0.99
0.97-1.01
0.33
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SF-36
1.05-1.12
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admission***
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OR
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Variable
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ICU = intensive care unit, OR = odds ratio, CI = confidence interval, *’Female’ reference, **’No’ reference, ***’Medical’ reference, Excessive alcohol consumption = > 7 drinks per week for women and > 14 drinks per week for men, APACHE II = Acute Physiology and Chronic Health
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Evaluation II, SF-36 = Short form 36, PCS = physical component summary, MCS = mental component summary, SF-12 = Short form 12.
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ACCEPTED MANUSCRIPT Table 3 AUC
Sensitivity
Specificity
(95% CI)
(95% CI)
(95% CI)
NPV
Accuracy
Model Aa
0.74 (0.67-0.82)
0.04 (0.00-0.13)
0.98 (0.96-0.99)
0.29
0.83
0.82
Model Bb
0.69 (0.62-0.76)
0.00 (0.00-0.07)
1.00 (0.98-1.00)
0.00
0.83
0.82
Model Cc
0.76 (0.69-0.83)
0.09 (0.03-0.20)
0.98 (0.96-1.00)
0.56
0.84
0.83
Model Dd,*
0.70 (0.62-0.77)
-
1.00 (0.98-1.00)
-
0.83
0.83
Model Ee
0.76 (0.70-0.83)
0.11 (0.04-0.23)
0.98 (0.96-1.00)
0.60
0.84
0.83
Model Aa
0.74 (0.68-0.80)
0.21 (0.13-0.31)
0.95 (0.91-0.97)
0.60
0.76
0.75
Model Bb
0.73 (0.67-0.79)
0.21 (0.13-0.31)
0.92 (0.87-0.95)
0.49
0.76
0.73
Model Cc
0.78 (0.73-0.84)
0.34 (0.24-0.45)
0.92 (0.88-0.95)
0.62
0.79
0.76
Model Dd
0.74 (0.68-0.80)
0.17 (0.10-0.27)
0.93 (0.89-0.96)
0.48
0.75
0.73
Model Ee
0.79 (0.73-0.84)
0.35 (0.25-0.46)
0.93 (0.89-0.96)
0.66
0.79
0.77
0.75 (0.69-0.81)
0.41 (0.32-0.51)
0.83 (0.77-0.88)
0.57
0.72
0.68
0.75 (0.69-0.80)
0.49 (0.39-0.59)
0.87 (0.82-0.91)
0.68
0.76
0.74
0.78 (0.73-0.84)
0.51 (0.41-0.61)
0.85 (0.79-0.90)
0.65
0.76
0.73
0.75 (0.69-0.80)
0.47 (0.38-0.57)
0.88 (0.83-0.92)
0.69
0.75
0.74
0.78 (0.73-0.84)
0.47 (0.38-0.57)
0.86 (0.80-0.90)
0.65
0.75
0.72
Model Cc Model Dd Model Ee
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Model Bb
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Model Aa
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30 days mortality
90 days mortality
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ICU mortality
PPV
AUC = area under the curve, CI = confidence interval, PPV = positive predictive value, NPV = negative predictive value, ICU = intensive care unit, a
Model A: APACHE II, age and gender, bModel B: SF-36 PCS, age and gender, cModel C: SF-36 PCS, APACHE II, age and gender, dModel D: SF-
12 PCS, age and gender, *the model does not predict mortality, eModel E: SF-12 PCS, APACHE II, age and gender, APACHE II = Acute Physiology and Chronic Health Evaluation II, SF-36 = Short form 36, PCS = physical component summary.
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ACCEPTED MANUSCRIPT Table 4 ICU mortality
30 days mortality
90 days mortality
OR
95% CI
P Value
OR
95% CI
95% CI
P Value
Age
1.02
0.99-1.04
0.17
1.04
1.01-1.06
0.001
1.05
1.03-1.07
< 0.0001
Gender*
1.87
1.00-3.60
0.05
1.51
0.88-2.59
0.14
1.70
1.02-2.85
0.04
APACHE II
1.08
1.05-1.11
< 0.0001
1.08
1.05-1.11
< 0.0001
1.07
1.04-1.11
< 0.0001
Age
1.02
1.00-1.05
0.09
1.04
1.02-1.06
0.0006
1.05
1.03-1.08
< 0.0001
Gender*
2.10
1.10-4.00
0.02
1.88
1.09-3.26
0.02
2.14
1.27-3.63
0.004
SF-36 PCS
0.96
0.93-0.98
0.002
0.94
0.91-0.96
< 0.0001
0.94
0.92-0.97
< 0.0001
Age
1.02
0.99-1.04
0.22
1.04
1.01-1.06
0.003
1.05
1.03-1.07
< 0.0001
Gender*
2.12
1.10-4.08
0.02
1.84
1.04-3.25
0.03
2.06
1.20-3.54
0.01
APACHE II
1.07
1.04-1.11
< 0.0001
1.07
1.03-1.10
< 0.0001
1.06
1.03-1.10
< 0.0001
SF-36 PCS
0.97
0.94-1.00
0.02
0.95
0.92-0.97
< 0.0001
0.95
0.93-0.98
< 0.0001
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Model B
OR
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Model A
P Value
1.02
1.00-1.05
0.10
1.04
1.01-1.06
0.001
1.05
1.03-1.07
< 0.0001
Gender*
2.18
1.16-4.13
0.02
1.81
1.04-3.14
0.04
2.01
1.19-3.41
0.01
0.95
0.92-0.98
0.001
0.94
0.91-0.96
< 0.0001
0.94
0.92-0.97
< 0.0001
Age
1.02
0.99-1.04
0.27
1.03
1.01-1.06
0.01
1.05
1.02-1.07
< 0.0001
Gender*
2.12
1.09-4.13
0.02
1.71
0.06-3.04
0.07
1.87
1.09-3.23
0.02
APACHE II
1.08
1.04-1.11
< 0.0001
1.07
1.04-1.11
< 0.0001
1.07
1.04-1.10
< 0.0001
SF-12 PCS
0.96
0.93-0.99
0.01
0.94
0.92-0.97
< 0.0001
0.95
0.93-0.98
< 0.0002
SF-12 PCS Model E
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Age
ICU = intensive care unit, OR = odds ratio, CI = confidence interval, *’Female’ reference, APACHE II = Acute Physiology and Chronic Health Evaluation II, SF-36 = Short form 36, PCS = physical component summary, SF-12 = Short form 12.
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