Risk factors for and prediction of mortality in critically ill medical-surgical patients receiving heparin thromboprophylaxis

Guowei Li; Lehana Thabane; Deborah J Cook; Renato D Lopes; John C Marshall; Gordon Guyatt; Anne Holbrook; Noori Akhtar-Danesh; Robert A Fowler; Neill K J Adhikari; Rob Taylor; Yaseen M Arabi; Dean Chittock; Peter Dodek; Andreas P Freitag; Stephen D Walter; Diane Heels-Ansdell; Mitchell A H Levine

doi:10.1186/s13613-016-0116-x

Risk factors for and prediction of mortality in critically ill medical-surgical patients receiving heparin thromboprophylaxis

Ann Intensive Care. 2016 Dec;6(1):18. doi: 10.1186/s13613-016-0116-x. Epub 2016 Feb 27.

Authors

Guowei Li¹, Lehana Thabane^{1

2}, Deborah J Cook^{1

2

3}, Renato D Lopes⁴, John C Marshall^{5

6}, Gordon Guyatt^{1

3}, Anne Holbrook^{1

2

3}, Noori Akhtar-Danesh^{1

7}, Robert A Fowler^{5

8}, Neill K J Adhikari^{5

8}, Rob Taylor⁹, Yaseen M Arabi¹⁰, Dean Chittock¹¹, Peter Dodek¹², Andreas P Freitag³, Stephen D Walter¹, Diane Heels-Ansdell¹, Mitchell A H Levine^{13

14

15}

Affiliations

¹ Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada.
² Centre for Evaluation of Medicines, St. Joseph's Healthcare Hamilton, McMaster University, 25 Main St. West, Suite 2000, 20th Floor, Hamilton, ON, L8P 1H1, Canada.
³ Department of Medicine, McMaster University, Hamilton, ON, Canada.
⁴ Duke Clinical Research Institute, Duke University, Durham, NC, USA.
⁵ Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada.
⁶ Critical Care Medicine, St. Michael's Hospital, Toronto, ON, Canada.
⁷ School of Nursing, McMaster University, Hamilton, ON, Canada.
⁸ Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
⁹ Mercy Clinic Adult Critical Care, Mercy Hospital Saint Louis, Saint Louis, MO, USA.
¹⁰ King Saud bin Abdulaziz University for Health Sciences and King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
¹¹ Critical Care Medicine, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada.
¹² Center for Health Evaluation and Outcome Sciences and Division of Critical Care Medicine, St. Paul's Hospital and University of British Columbia, Vancouver, BC, Canada.
¹³ Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada. [email protected].
¹⁴ Centre for Evaluation of Medicines, St. Joseph's Healthcare Hamilton, McMaster University, 25 Main St. West, Suite 2000, 20th Floor, Hamilton, ON, L8P 1H1, Canada. [email protected].
¹⁵ Department of Medicine, McMaster University, Hamilton, ON, Canada. [email protected].

Abstract

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.

Keywords: APACHE; Critical care; Intensive care unit; Mortality; Prediction model.

Associated data

ClinicalTrials.gov/NCT00182143