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Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study
Author(s) -
Jewel Y Lee,
Sevda Molani,
Chen Fang,
Kathleen Jade,
D Shane O'Mahony,
Sergey A. Kornilov,
Lindsay T Mico,
Jennifer Hadlock
Publication year - 2021
Publication title -
jmir medical informatics
Language(s) - English
Resource type - Journals
ISSN - 2291-9694
DOI - 10.2196/29986
Subject(s) - medicine , sepsis , interpretability , intensive care medicine , ambulatory , emergency medicine , medical record , receiver operating characteristic , framingham risk score , machine learning , computer science , disease
Background Sepsis is a life-threatening condition that can rapidly lead to organ damage and death. Existing risk scores predict outcomes for patients who have already become acutely ill. Objective We aimed to develop a model for identifying patients at risk of getting sepsis within 2 years in order to support the reduction of sepsis morbidity and mortality. Methods Machine learning was applied to 2,683,049 electronic health records (EHRs) with over 64 million encounters across five states to develop models for predicting a patient’s risk of getting sepsis within 2 years. Features were selected to be easily obtainable from a patient’s chart in real time during ambulatory encounters. Results The models showed consistent prediction scores, with the highest area under the receiver operating characteristic curve of 0.82 and a positive likelihood ratio of 2.9 achieved with gradient boosting on all features combined. Predictive features included age, sex, ethnicity, average ambulatory heart rate, standard deviation of BMI, and the number of prior medical conditions and procedures. The findings identified both known and potential new risk factors for long-term sepsis. Model variations also illustrated trade-offs between incrementally higher accuracy, implementability, and interpretability. Conclusions Accurate implementable models were developed to predict the 2-year risk of sepsis, using EHR data that is easy to obtain from ambulatory encounters. These results help advance the understanding of sepsis and provide a foundation for future trials of risk-informed preventive care.

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