Updating mortality risk estimation in intensive care units from high-dimensional electronic health records with incomplete data

BMC Medical Informatics and Decision Making

Research

Bertrand Bouvarel¹  Nathanael Lapidus²  Fabrice Carrat² 

[1] Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France;Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France;AP-HP.Sorbonne Université, Public Health Department, Saint-Antoine Hospital, F75012, Paris, France;

关键词: Clinical decision support systems;    Electronic health records;    Machine learning;    Multiple imputation;    Neural network;   

DOI  :  10.1186/s12911-023-02264-7

 received in 2022-09-27, accepted in 2023-08-16,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe risk of mortality in intensive care units (ICUs) is currently addressed by the implementation of scores using admission data. Their performances are satisfactory when complications occur early after admission; however, they may become irrelevant in the case of long hospital stays. In this study, we developed predictive models of short-term mortality in the ICU from longitudinal data.MethodsUsing data collected throughout patients’ stays of at least 48 h from the MIMIC-III database, several statistical learning approaches were compared, including deep neural networks and penalized regression. Missing data were handled using complete-case analysis or multiple imputation.ResultsComplete-case analyses from 19 predictors showed good discrimination (AUC > 0.77 for several approaches) to predict death between 12 and 24 h onward, yet excluded 75% of patients from the initial target cohort, as data was missing for some of the predictors. Multiple imputation allowed us to include 70 predictors and keep 95% of patients, with similar performances.ConclusionThis proof-of-concept study supports that automated analysis of electronic health records can be of great interest throughout patients’ stays as a surveillance tool. Although this framework relies on a large set of predictors, it is robust to data imputation and may be effective early after admission, when data are still scarce.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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