A machine learning model predicted in-hospital mortality for adult patients at admission with an AUC of 0.86 (95% CI, 0.83-0.90) in prospective validation.
Observational (n=75,247)
Yes
Does a machine learning model using electronic health record data accurately predict in-hospital mortality in adult patients at the time of admission?
A machine learning model using commonly available electronic health record data at admission demonstrated good prospective and external validation for predicting in-hospital mortality.
Effect estimate: AUC 0.86 (95% CI 0.83-0.90)
Importance: The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-hospital death are both broadly applicable to all adult patients across a health system and readily implementable. Similarly, few have been implemented, and none have been evaluated prospectively and externally validated. Objectives: To prospectively and externally validate a machine learning model that predicts in-hospital mortality for all adult patients at the time of hospital admission and to design the model using commonly available electronic health record data and accessible computational methods. Design, Setting, and Participants: In this prognostic study, electronic health record data from a total of 43 180 hospitalizations representing 31 003 unique adult patients admitted to a quaternary academic hospital (hospital A) from October 1, 2014, to December 31, 2015, formed a training and validation cohort. The model was further validated in additional cohorts spanning from March 1, 2018, to August 31, 2018, using 16 122 hospitalizations representing 13 094 unique adult patients admitted to hospital A, 6586 hospitalizations representing 5613 unique adult patients admitted to hospital B, and 4086 hospitalizations representing 3428 unique adult patients admitted to hospital C. The model was integrated into the production electronic health record system and prospectively validated on a cohort of 5273 hospitalizations representing 4525 unique adult patients admitted to hospital A between February 14, 2019, and April 15, 2019. Main Outcomes and Measures: The main outcome was in-hospital mortality. Model performance was quantified using the area under the receiver operating characteristic curve and area under the precision recall curve. Results: A total of 75 247 hospital admissions (median interquartile range patient age, 59.5 29.0 years; 45.9% involving male patients) were included in the study. The in-hospital mortality rates for the training validation; retrospective validations at hospitals A, B, and C; and prospective validation cohorts were 3.0%, 2.7%, 1.8%, 2.1%, and 1.6%, respectively. The area under the receiver operating characteristic curves were 0.87 (95% CI, 0.83-0.89), 0.85 (95% CI, 0.83-0.87), 0.89 (95% CI, 0.86-0.92), 0.84 (95% CI, 0.80-0.89), and 0.86 (95% CI, 0.83-0.90), respectively. The area under the precision recall curves were 0.29 (95% CI, 0.25-0.37), 0.17 (95% CI, 0.13-0.22), 0.22 (95% CI, 0.14-0.31), 0.13 (95% CI, 0.08-0.21), and 0.14 (95% CI, 0.09-0.21), respectively. Conclusions and Relevance: Prospective and multisite retrospective evaluations of a machine learning model demonstrated good discrimination of in-hospital mortality for adult patients at the time of admission. The data elements, methods, and patient selection make the model implementable at a system level.
Brajer et al. (Fri,) conducted a observational in In-hospital mortality (n=75,247). Machine learning model was evaluated on In-hospital mortality (AUC 0.86, 95% CI 0.83-0.90). A machine learning model predicted in-hospital mortality for adult patients at admission with an AUC of 0.86 (95% CI, 0.83-0.90) in prospective validation.
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