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Risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis
BACKGROUND: Previous studies have suggested that prediction models for mortality should be adjusted for additional risk factors beyond the Acute Physiology and Chronic Health Evaluation (APACHE) score. Our objective was to identify risk factors independent of APACHE II score and construct a predicti...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Paris
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769241/ https://www.ncbi.nlm.nih.gov/pubmed/26921148 http://dx.doi.org/10.1186/s13613-016-0116-x |
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author | Li, Guowei Thabane, Lehana Cook, Deborah J. Lopes, Renato D. Marshall, John C. Guyatt, Gordon Holbrook, Anne Akhtar-Danesh, Noori Fowler, Robert A. Adhikari, Neill K. J. Taylor, Rob Arabi, Yaseen M. Chittock, Dean Dodek, Peter Freitag, Andreas P. Walter, Stephen D. Heels-Ansdell, Diane Levine, Mitchell A. H. |
author_facet | Li, Guowei Thabane, Lehana Cook, Deborah J. Lopes, Renato D. Marshall, John C. Guyatt, Gordon Holbrook, Anne Akhtar-Danesh, Noori Fowler, Robert A. Adhikari, Neill K. J. Taylor, Rob Arabi, Yaseen M. Chittock, Dean Dodek, Peter Freitag, Andreas P. Walter, Stephen D. Heels-Ansdell, Diane Levine, Mitchell A. H. |
author_sort | Li, Guowei |
collection | PubMed |
description | BACKGROUND: Previous studies have suggested that prediction models for mortality should be adjusted for additional risk factors beyond the Acute Physiology and Chronic Health Evaluation (APACHE) score. Our objective was to identify risk factors independent of APACHE II score and construct a prediction model to improve the predictive accuracy for hospital and intensive care unit (ICU) mortality. METHODS: We used data from a multicenter randomized controlled trial (PROTECT, Prophylaxis for Thromboembolism in Critical Care Trial) to build a new prediction model for hospital and ICU mortality. Our primary outcome was all-cause 60-day hospital mortality, and the secondary outcome was all-cause 60-day ICU mortality. RESULTS: We included 3746 critically ill non-trauma medical–surgical patients receiving heparin thromboprophylaxis (43.3 % females) in this study. The new model predicting 60-day hospital mortality incorporated APACHE II score (main effect: hazard ratio (HR) = 0.97 for per-point increase), body mass index (BMI) (main effect: HR = 0.92 for per-point increase), medical admission versus surgical (HR = 1.67), use of inotropes or vasopressors (HR = 1.34), acetylsalicylic acid or clopidogrel (HR = 1.27) and the interaction term between APACHE II score and BMI (HR = 1.002 for per-point increase). This model had a good fit to the data and was well calibrated and internally validated. However, the discriminative ability of the prediction model was unsatisfactory (C index < 0.65). Sensitivity analyses supported the robustness of these findings. Similar results were observed in the new prediction model for 60-day ICU mortality which included APACHE II score, BMI, medical admission and invasive mechanical ventilation. CONCLUSION: Compared with the APACHE II score alone, the new prediction model increases data collection, is more complex but does not substantially improve discriminative ability. Trial registration: ClinicalTrials.gov Identifier: NCT00182143 |
format | Online Article Text |
id | pubmed-4769241 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Paris |
record_format | MEDLINE/PubMed |
spelling | pubmed-47692412016-03-29 Risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis Li, Guowei Thabane, Lehana Cook, Deborah J. Lopes, Renato D. Marshall, John C. Guyatt, Gordon Holbrook, Anne Akhtar-Danesh, Noori Fowler, Robert A. Adhikari, Neill K. J. Taylor, Rob Arabi, Yaseen M. Chittock, Dean Dodek, Peter Freitag, Andreas P. Walter, Stephen D. Heels-Ansdell, Diane Levine, Mitchell A. H. Ann Intensive Care Research BACKGROUND: Previous studies have suggested that prediction models for mortality should be adjusted for additional risk factors beyond the Acute Physiology and Chronic Health Evaluation (APACHE) score. Our objective was to identify risk factors independent of APACHE II score and construct a prediction model to improve the predictive accuracy for hospital and intensive care unit (ICU) mortality. METHODS: We used data from a multicenter randomized controlled trial (PROTECT, Prophylaxis for Thromboembolism in Critical Care Trial) to build a new prediction model for hospital and ICU mortality. Our primary outcome was all-cause 60-day hospital mortality, and the secondary outcome was all-cause 60-day ICU mortality. RESULTS: We included 3746 critically ill non-trauma medical–surgical patients receiving heparin thromboprophylaxis (43.3 % females) in this study. The new model predicting 60-day hospital mortality incorporated APACHE II score (main effect: hazard ratio (HR) = 0.97 for per-point increase), body mass index (BMI) (main effect: HR = 0.92 for per-point increase), medical admission versus surgical (HR = 1.67), use of inotropes or vasopressors (HR = 1.34), acetylsalicylic acid or clopidogrel (HR = 1.27) and the interaction term between APACHE II score and BMI (HR = 1.002 for per-point increase). This model had a good fit to the data and was well calibrated and internally validated. However, the discriminative ability of the prediction model was unsatisfactory (C index < 0.65). Sensitivity analyses supported the robustness of these findings. Similar results were observed in the new prediction model for 60-day ICU mortality which included APACHE II score, BMI, medical admission and invasive mechanical ventilation. CONCLUSION: Compared with the APACHE II score alone, the new prediction model increases data collection, is more complex but does not substantially improve discriminative ability. Trial registration: ClinicalTrials.gov Identifier: NCT00182143 Springer Paris 2016-02-27 /pmc/articles/PMC4769241/ /pubmed/26921148 http://dx.doi.org/10.1186/s13613-016-0116-x Text en © Li et al. 2016 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. |
spellingShingle | Research Li, Guowei Thabane, Lehana Cook, Deborah J. Lopes, Renato D. Marshall, John C. Guyatt, Gordon Holbrook, Anne Akhtar-Danesh, Noori Fowler, Robert A. Adhikari, Neill K. J. Taylor, Rob Arabi, Yaseen M. Chittock, Dean Dodek, Peter Freitag, Andreas P. Walter, Stephen D. Heels-Ansdell, Diane Levine, Mitchell A. H. Risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis |
title | Risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis |
title_full | Risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis |
title_fullStr | Risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis |
title_full_unstemmed | Risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis |
title_short | Risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis |
title_sort | risk factors for and prediction of mortality in critically ill medical–surgical patients receiving heparin thromboprophylaxis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769241/ https://www.ncbi.nlm.nih.gov/pubmed/26921148 http://dx.doi.org/10.1186/s13613-016-0116-x |
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