Incorporating Laboratory Values Into a Machine Learning Model Improves In-Hospital Mortality Predictions After Rapid Response Team Call

Peter M Reardon; Enea Parimbelli; Szymon Wilk; Wojtek Michalowski; Kyle Murphy; Jennifer Shen; Brent Herritt; Benjamin Gershkovich; Peter Tanuseputro; Kwadwo Kyeremanteng

doi:10.1097/CCE.0000000000000023

Incorporating Laboratory Values Into a Machine Learning Model Improves In-Hospital Mortality Predictions After Rapid Response Team Call

Crit Care Explor. 2019 Jul 16;1(7):e0023. doi: 10.1097/CCE.0000000000000023. eCollection 2019 Jul.

Authors

Peter M Reardon^{1

2}, Enea Parimbelli³, Szymon Wilk^{3

4}, Wojtek Michalowski³, Kyle Murphy¹, Jennifer Shen¹, Brent Herritt¹, Benjamin Gershkovich¹, Peter Tanuseputro^{5

6

7

8}, Kwadwo Kyeremanteng^{1

5

6

9}

Affiliations

¹ Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada.
² Department of Emergency Medicine, University of Ottawa, Ottawa, ON, Canada.
³ Telfer School of Management, University of Ottawa, Ottawa, ON, Canada.
⁴ Institute of Computing Science, Poznan University of Technology, Poznan, Poland.
⁵ Division of Palliative Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada.
⁶ Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
⁷ Institute for Clinical and Evaluative Sciences, Ottawa, ON, Canada.
⁸ Bruyere Research Institute, Ottawa, ON, Canada.
⁹ Institut du Savoir Montfort, Montfort Hospital, Ottawa, ON, Canada.

Abstract

Objectives: Machine learning models have been used to predict mortality among patients requiring rapid response team activation. The goal of our study was to assess the impact of adding laboratory values into the model.

Design: A gradient boosted decision tree model was derived and internally validated to predict a primary outcome of in-hospital mortality. The base model was then augmented with laboratory values.

Setting: Two tertiary care hospitals within The Ottawa Hospital network.

Patients: Inpatients over the age of 18 years who experienced a rapid response team activation between January 1, 2015, and May 31, 2016.

Interventions: None.

Measurements and main results: A total of 2,061 rapid response team activations occurred during the study period. The in-hospital mortality rate was 29.4%. Patients who died were older (median age, 72 vs 68 yr; p < 0.001), had a longer length of stay (length of stay) prior to rapid response team activation (4 vs 2 d; p < 0.001), and more often had respiratory distress (31% vs 22%; p < 0.001). Our base model without laboratory values performed with an area under the receiver operating curve of 0.71 (95% CI, 0.71-0.72). When the base model was augmented with laboratory values, the area under the receiver operating curve improved to 0.77 (95% CI, 0.77-0.78). Important mortality predictors in the base model were age, estimated ratio of Pao₂ to Fio₂ (calculated using oxygen saturation and estimated Fio₂), length of stay prior to rapid response team activation, and systolic blood pressure.

Conclusions: Machine learning models can identify rapid response team patients at a high risk of mortality and potentially supplement clinical decision making. Incorporating laboratory values into model development significantly improved predictive performance in this study.

Keywords: critical care; machine learning model; mortality; rapid response team; resuscitation.