Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study.

Autor:	Lee JY; Institute for Systems Biology, Seattle, WA, United States., Molani S; Institute for Systems Biology, Seattle, WA, United States., Fang C; Institute for Systems Biology, Seattle, WA, United States., Jade K; Institute for Systems Biology, Seattle, WA, United States., O'Mahony DS; Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA, United States., Kornilov SA; Institute for Systems Biology, Seattle, WA, United States., Mico LT; Providence St Joseph Health, Renton, WA, United States., Hadlock JJ; Institute for Systems Biology, Seattle, WA, United States.
Jazyk:	angličtina
Zdroj:	JMIR medical informatics [JMIR Med Inform] 2021 Jul 08; Vol. 9 (7), pp. e29986. Date of Electronic Publication: 2021 Jul 08.
DOI:	10.2196/29986
Abstrakt:	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. (©Jewel Y Lee, Sevda Molani, Chen Fang, Kathleen Jade, D Shane O'Mahony, Sergey A Kornilov, Lindsay T Mico, Jennifer J Hadlock. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 08.07.2021.)
Databáze:	MEDLINE
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