An explainable Gradient Boosting machine learning model predicted ICU readmission or death within 7 days of discharge with an AUROC of 0.78, potentially enabling a 14% relative risk reduction.
Cohort (n=18,034)
No
Does an explainable machine learning model accurately predict the composite of ICU readmission or death within 7 days of ICU discharge in adult ICU patients?
An explainable machine learning model using routinely collected ICU data demonstrated good discrimination for predicting 7-day post-discharge readmission or mortality, potentially enabling targeted discharge management to reduce relative risk by 14%.
Effect estimate: AUROC 0.78 (95% CI 0.75-0.81)
Unexpected ICU readmission is associated with longer length of stay and increased mortality. To prevent ICU readmission and death after ICU discharge, our team of intensivists and data scientists aimed to use AmsterdamUMCdb to develop an explainable machine learning-based real-time bedside decision support tool. DERIVATION COHORT: Data from patients admitted to a mixed surgical-medical academic medical center ICU from 2004 to 2016. VALIDATION COHORT: Data from 2016 to 2019 from the same center. PREDICTION MODEL: Patient characteristics, clinical observations, physiologic measurements, laboratory studies, and treatment data were considered as model features. Different supervised learning algorithms were trained to predict ICU readmission and/or death, both within 7 days from ICU discharge, using 10-fold cross-validation. Feature importance was determined using SHapley Additive exPlanations, and readmission probability-time curves were constructed to identify subgroups. Explainability was established by presenting individualized risk trends and feature importance. RESULTS: = 3,929). The most predictive features included common physiologic parameters but also less apparent variables like nutritional support. At a 6% risk threshold, the model showed a sensitivity (recall) of 0.72, specificity of 0.70, and a positive predictive value (precision) of 0.15. Impact analysis using probability-time curves and the 6% risk threshold identified specific patient groups at risk and the potential of a change in discharge management to reduce relative risk by 14%. CONCLUSIONS: We developed an explainable machine learning model that may aid in identifying patients at high risk for readmission and mortality after ICU discharge using the first freely available European critical care database, AmsterdamUMCdb. Impact analysis showed that a relative risk reduction of 14% could be achievable, which might have significant impact on patients and society. ICU data sharing facilitates collaboration between intensivists and data scientists to accelerate model development.
Thoral et al. (Wed,) conducted a cohort in Critically ill patients (ICU admissions) (n=18,034). Explainable machine learning model (Gradient Boosting) was evaluated on ICU readmission and/or death within 7 days of ICU discharge (AUROC 0.78, 95% CI 0.75-0.81). An explainable Gradient Boosting machine learning model predicted ICU readmission or death within 7 days of discharge with an AUROC of 0.78, potentially enabling a 14% relative risk reduction.