Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients

Abstract Background Functional status (FS) before intensive care unit (ICU) admission is associated with short-term and long-term outcomes among critically ill patients. However, measures of FS are generally not integrated into ICU-specific mortality prediction models. Methods This retrospective coh...

Full description

Bibliographic Details
Main Authors: James S. Krinsley, Thomas Wasser, Gina Kang, Sean M. Bagshaw
Format: Article
Language:English
Published: BMC 2017-05-01
Series:Critical Care
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13054-017-1688-z
id doaj-e2e2700d092042ecac10ee2bee1178bc
record_format Article
spelling doaj-e2e2700d092042ecac10ee2bee1178bc2020-11-24T21:32:39ZengBMCCritical Care1364-85352017-05-012111910.1186/s13054-017-1688-zPre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patientsJames S. Krinsley0Thomas Wasser1Gina Kang2Sean M. Bagshaw3Division of Critical Care, Department of Medicine, Stamford Hospital, Columbia University College of Physicians and SurgeonsBiostatisics Consult-StatDepartment of Medicine, Stamford Hospital, Columbia University College of Physicians and SurgeonsDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of AlbertaAbstract Background Functional status (FS) before intensive care unit (ICU) admission is associated with short-term and long-term outcomes among critically ill patients. However, measures of FS are generally not integrated into ICU-specific mortality prediction models. Methods This retrospective cohort study used prospectively collected data from 9638 consecutive patients admitted to a single ICU between 1 October 2005 and 30 September 2015. For each ICU admission, FS was prospectively determined and classified into three discrete categories based on performance of basic daily living activities (FS1 - fully independent; FS2 - partly dependent; FS3 - completely dependent). We prospectively calculated Acute Physiology and Chronic Health Evaluation (APACHE) IV predicted mortality percentage (APIV PM) for each admission and calculated observed-expected mortality ratios (OEMR), stratified by FS category and APIV PM. We calculated area under the receiver operator characteristic curve (AUC) for APIV PM and mortality for the entire cohort and the three FS categories. Results Patients had a median (IQR) age of 67 (52–80) years and mean (SD) APIV PM was 18.3% (24.3%). Of these, 7714 (80.0%) were classified as FS1, 1728 (17.9%) as FS2 and 196 (2.0%) as FS3. FS1 patients were younger, had less comorbid disease, and lower APIV PM compared to FS2 and FS3. The OEMR were significantly lower for FS1 (0.67) than FS2 (0.93) or FS3 (0.90) (p < 0.0001 for both comparisons). Among patients with APIV PM 0–10%, 10–25%, 25–50% and ≥50% the OEMR for FS1 were 0.33, 0.49, 0.61 and 0.86. The AUC (95% CI) for APIV PM and mortality for FS1, FS2 and FS3 were 0.924 (0.914–0.933), 0.837 (0.816–0.858) and 0.775 (0.705–0.8456), respectively (p < 0.001 for each comparison). Multivariable analysis demonstrated that FS2 (OR 2.18 (1.84–2.57) (p < 0.0001)) and FS3 (OR 1.99 (1.34–2.96) (p = 0.0006)) were independently associated with increased risk of mortality. Conclusions Baseline FS prior to critical illness is a strong independent predictor of mortality and impacts the relationship between observed and APIV PM in those with lower illness severity. Future iterations of mortality prediction models should integrate a baseline measure of FS to improve performance.http://link.springer.com/article/10.1186/s13054-017-1688-zFunctional statusCritically illMortalityMortality prediction modelsAcute physiology and chronic health evaluation IV
collection DOAJ
language English
format Article
sources DOAJ
author James S. Krinsley
Thomas Wasser
Gina Kang
Sean M. Bagshaw
spellingShingle James S. Krinsley
Thomas Wasser
Gina Kang
Sean M. Bagshaw
Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients
Critical Care
Functional status
Critically ill
Mortality
Mortality prediction models
Acute physiology and chronic health evaluation IV
author_facet James S. Krinsley
Thomas Wasser
Gina Kang
Sean M. Bagshaw
author_sort James S. Krinsley
title Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients
title_short Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients
title_full Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients
title_fullStr Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients
title_full_unstemmed Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients
title_sort pre-admission functional status impacts the performance of the apache iv model of mortality prediction in critically ill patients
publisher BMC
series Critical Care
issn 1364-8535
publishDate 2017-05-01
description Abstract Background Functional status (FS) before intensive care unit (ICU) admission is associated with short-term and long-term outcomes among critically ill patients. However, measures of FS are generally not integrated into ICU-specific mortality prediction models. Methods This retrospective cohort study used prospectively collected data from 9638 consecutive patients admitted to a single ICU between 1 October 2005 and 30 September 2015. For each ICU admission, FS was prospectively determined and classified into three discrete categories based on performance of basic daily living activities (FS1 - fully independent; FS2 - partly dependent; FS3 - completely dependent). We prospectively calculated Acute Physiology and Chronic Health Evaluation (APACHE) IV predicted mortality percentage (APIV PM) for each admission and calculated observed-expected mortality ratios (OEMR), stratified by FS category and APIV PM. We calculated area under the receiver operator characteristic curve (AUC) for APIV PM and mortality for the entire cohort and the three FS categories. Results Patients had a median (IQR) age of 67 (52–80) years and mean (SD) APIV PM was 18.3% (24.3%). Of these, 7714 (80.0%) were classified as FS1, 1728 (17.9%) as FS2 and 196 (2.0%) as FS3. FS1 patients were younger, had less comorbid disease, and lower APIV PM compared to FS2 and FS3. The OEMR were significantly lower for FS1 (0.67) than FS2 (0.93) or FS3 (0.90) (p < 0.0001 for both comparisons). Among patients with APIV PM 0–10%, 10–25%, 25–50% and ≥50% the OEMR for FS1 were 0.33, 0.49, 0.61 and 0.86. The AUC (95% CI) for APIV PM and mortality for FS1, FS2 and FS3 were 0.924 (0.914–0.933), 0.837 (0.816–0.858) and 0.775 (0.705–0.8456), respectively (p < 0.001 for each comparison). Multivariable analysis demonstrated that FS2 (OR 2.18 (1.84–2.57) (p < 0.0001)) and FS3 (OR 1.99 (1.34–2.96) (p = 0.0006)) were independently associated with increased risk of mortality. Conclusions Baseline FS prior to critical illness is a strong independent predictor of mortality and impacts the relationship between observed and APIV PM in those with lower illness severity. Future iterations of mortality prediction models should integrate a baseline measure of FS to improve performance.
topic Functional status
Critically ill
Mortality
Mortality prediction models
Acute physiology and chronic health evaluation IV
url http://link.springer.com/article/10.1186/s13054-017-1688-z
work_keys_str_mv AT jamesskrinsley preadmissionfunctionalstatusimpactstheperformanceoftheapacheivmodelofmortalitypredictionincriticallyillpatients
AT thomaswasser preadmissionfunctionalstatusimpactstheperformanceoftheapacheivmodelofmortalitypredictionincriticallyillpatients
AT ginakang preadmissionfunctionalstatusimpactstheperformanceoftheapacheivmodelofmortalitypredictionincriticallyillpatients
AT seanmbagshaw preadmissionfunctionalstatusimpactstheperformanceoftheapacheivmodelofmortalitypredictionincriticallyillpatients
_version_ 1725956732419571712