Abstract

BackgroundAccidental hypothermia is a critical condition with high risks of fatal arrhythmia, multiple organ failure, and mortality; however, there is no established model to predict the mortality. The present study aimed to develop and validate machine learning-based models for predicting in-hospital mortality using easily available data at hospital admission among the patients with accidental hypothermia.MethodThis study was secondary analysis of multi-center retrospective cohort study (J-point registry) including patients with accidental hypothermia. Adult patients with body temperature 35.0 °C or less at emergency department were included. Prediction models for in-hospital mortality using machine learning (lasso, random forest, and gradient boosting tree) were made in development cohort from six hospitals, and the predictive performance were assessed in validation cohort from other six hospitals. As a reference, we compared the SOFA score and 5A score.ResultsWe included total 532 patients in the development cohort [N = 288, six hospitals, in-hospital mortality: 22.0% (64/288)], and the validation cohort [N = 244, six hospitals, in-hospital mortality 27.0% (66/244)]. The C-statistics [95% CI] of the models in validation cohorts were as follows: lasso 0.784 [0.717–0.851] , random forest 0.794[0.735–0.853], gradient boosting tree 0.780 [0.714–0.847], SOFA 0.787 [0.722–0.851], and 5A score 0.750[0.681–0.820]. The calibration plot showed that these models were well calibrated to observed in-hospital mortality. Decision curve analysis indicated that these models obtained clinical net-benefit.ConclusionThis multi-center retrospective cohort study indicated that machine learning-based prediction models could accurately predict in-hospital mortality in validation cohort among the accidental hypothermia patients. These models might be able to support physicians and patient’s decision-making. However, the applicability to clinical settings, and the actual clinical utility is still unclear; thus, further prospective study is warranted to evaluate the clinical usefulness.

Highlights

  • Accidental hypothermia is a critical condition with high risks of fatal arrhythmia, multiple organ failure, and mortality; there is no established model to predict the mortality

  • This multi-center retrospective cohort study indicated that machine learning-based prediction models could accurately predict in-hospital mortality in validation cohort among the accidental hypothermia patients

  • Accidental hypothermia is an unintentional decrease in core body temperature below 35 °C with high risks of fatal arrhythmia, multiple organ failure, and mortality (24–40%) [1,2,3,4]

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Summary

Results

Based on the distribution of outcome by SOFA score in the development cohort, it was reasonable to assume the association between SOFA score and in-hospital mortality as a linear relationship (Supplementary Fig. 1, Additional file 1). Model performance in validation cohorts For discrimination, the C-statistics [95% CI] of the models in validation cohorts were as follows: lasso, 0.784 [0.717-0.851]; random forest, 0.794 [0.735–0.853]; boosting tree, 0.780 [0.714–0.847]; SOFA, 0.787 [0.722– 0.851]; and 5A score, 0.750 [0.681–0.820]. For the visual assessment of the calibration plot in the validation cohort (Fig. 4), the boosting tree model and SOFA were well calibrated to the observed overall range of the predicted in-hospital mortality. The net-benefit values of the models were almost the same, the net-benefit of the gradient boosting tree was slightly higher and that of the 5A score was slightly lower than the others

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