ArticleJournal of intensive care2021
Machine learning-based prediction models for accidental hypothermia patients.
Article in Journal of intensive care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A multicenter predictive model for inadvertent intraoperative hypothermia management in elderly patients in Southwest China.Scientific reports · 2026Article
- Construction and verification of an early warning model for hypothermia in emergency trauma patients based on the random forest method.American journal of translational research · 2026Article
- Systematic Review of the Intraoperative Hypothermia Risk Prediction Models in Total Joint Arthroplasty Patients.Journal of multidisciplinary healthcare · 2026Review
- Development of a machine learning-based predictive model for intraoperative hypothermia risk during radical surgery for oral cancer.American journal of translational research · 2025Article
- Article
- Development of a Machine Learning-Based Model for Predicting the Incidence of Peripheral Intravenous Catheter-Associated Phlebitis.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2024Article
- Explainable artificial intelligence in emergency medicine: an overview.Clinical and experimental emergency medicine · 2023Article
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
- External validation of 5A score model for predicting in-hospital mortality among the accidental hypothermia patients: JAAM-Hypothermia study 2018-2019 secondary analysis.Journal of intensive care · 2022Article
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Authors and funding
16 authors.
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No grant is acknowledged in the PubMed record.
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.
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