TY  - JOUR
AU  - Lee, Jewel Y
AU  - Molani, Sevda
AU  - Fang, Chen
AU  - Jade, Kathleen
AU  - O'Mahony, D Shane
AU  - Kornilov, Sergey A
AU  - Mico, Lindsay T
AU  - Hadlock, Jennifer J
PY  - 2021
DA  - 2021/7/8
TI  - Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study
JO  - JMIR Med Inform
SP  - e29986
VL  - 9
IS  - 7
KW  - sepsis
KW  - machine learning
KW  - electronic health records
KW  - risk prediction
KW  - clinical decision making
KW  - prevention
KW  - risk factors
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2021/7/e29986
UR  - https://doi.org/10.2196/29986
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34086596
DO  - 10.2196/29986
ID  - info:doi/10.2196/29986
ER  -