Yohei Okada1,2,3, Tasuku Matsuyama4, Sachiko Morita5, Naoki Ehara6, Nobuhiro Miyamae7, Takaaki Jo8, Yasuyuki Sumida9, Nobunaga Okada4,10, Makoto Watanabe4, Masahiro Nozawa11, Ayumu Tsuruoka12, Yoshihiro Fujimoto13, Yoshiki Okumura14, Tetsuhisa Kitamura15, Ryoji Iiduka16, Shigeru Ohtsuru17. 1. Department of Primary Care and Emergency Medicine, Graduate School of Medicine, Kyoto University, ShogoinKawaramachi54, Sakyo, Kyoto, 606-8507, Japan. yokada-kyf@umin.ac.jp. 2. Preventive Services, School of Public Health, Kyoto University, Kyoto, Japan. yokada-kyf@umin.ac.jp. 3. Department of Emergency and Critical Care Medicine, Japanese Red Cross Society, Kyoto Daini Hospital, Kyoto, Japan. yokada-kyf@umin.ac.jp. 4. Department of Emergency Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan. 5. Senri Critical Care Medical Center, Saiseikai Senri Hospital, Suita, Japan. 6. Department of Emergency, Japanese Red Cross Society, Kyoto Daiichi Red Cross Hospital, Kyoto, Japan. 7. Department of Emergency Medicine, Rakuwa-kai Otowa Hospital, Kyoto, Japan. 8. Department of Emergency Medicine, Uji-Tokushukai Medical Center, Uji, Japan. 9. Department of Emergency Medicine, North Medical Center, Kyoto Prefectural University of Medicine, Kyoto, Japan. 10. Department of Emergency and Critical Care Medicine, National Hospital Organization, Kyoto Medical Center, Kyoto, Japan. 11. Department of Emergency and Critical Care Medicine, Saiseikai Shiga Hospital, Ritto, Japan. 12. Department of Emergency and Critical Care Medicine, Kyoto Min-Iren Chuo Hospital, Kyoto, Japan. 13. Department of Emergency Medicine, Yodogawa Christian Hospital, Osaka, Japan. 14. Department of Emergency Medicine, Fukuchiyama City Hospital, Fukuchiyama, Japan. 15. Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan. 16. Department of Emergency and Critical Care Medicine, Japanese Red Cross Society, Kyoto Daini Hospital, Kyoto, Japan. 17. Department of Primary Care and Emergency Medicine, Graduate School of Medicine, Kyoto University, ShogoinKawaramachi54, Sakyo, Kyoto, 606-8507, Japan.
Abstract
BACKGROUND: Accidental 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. METHOD: This 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. RESULTS: We 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. CONCLUSION: 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. 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.
BACKGROUND:Accidental 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. METHOD: This 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. RESULTS: We 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. CONCLUSION: 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 hypothermiapatients. 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.
Authors: Jarrod D Frizzell; Li Liang; Phillip J Schulte; Clyde W Yancy; Paul A Heidenreich; Adrian F Hernandez; Deepak L Bhatt; Gregg C Fonarow; Warren K Laskey Journal: JAMA Cardiol Date: 2017-02-01 Impact factor: 14.676
Authors: Richard S Saczkowski; Doug J A Brown; Riyad B Abu-Laban; Guy Fradet; Costas J Schulze; Nick D Kuzak Journal: Resuscitation Date: 2018-03-23 Impact factor: 5.262