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Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients
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 stu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5433010/ https://www.ncbi.nlm.nih.gov/pubmed/28506290 http://dx.doi.org/10.1186/s13054-017-1688-z |
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author | Krinsley, James S. Wasser, Thomas Kang, Gina Bagshaw, Sean M. |
author_facet | Krinsley, James S. Wasser, Thomas Kang, Gina Bagshaw, Sean M. |
author_sort | Krinsley, James S. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13054-017-1688-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5433010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54330102017-05-17 Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients Krinsley, James S. Wasser, Thomas Kang, Gina Bagshaw, Sean M. Crit Care Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13054-017-1688-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-15 /pmc/articles/PMC5433010/ /pubmed/28506290 http://dx.doi.org/10.1186/s13054-017-1688-z Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Krinsley, James S. Wasser, Thomas Kang, Gina Bagshaw, Sean M. Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients |
title | 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_short | 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 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5433010/ https://www.ncbi.nlm.nih.gov/pubmed/28506290 http://dx.doi.org/10.1186/s13054-017-1688-z |
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