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Park SW, Yeo NY, Kang S, Ha T, Kim TH, Lee D, Kim D, Choi S, Kim M, Lee D, Kim D, Kim WJ, Lee SJ, Heo YJ, Moon DH, Han SS, Kim Y, Choi HS, Oh DK, Lee SY, Park M, Lim CM, Heo J. Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study. J Korean Med Sci 2024; 39:e53. [PMID: 38317451 PMCID: PMC10843974 DOI: 10.3346/jkms.2024.39.e53] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/05/2023] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP). RESULTS Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. CONCLUSION Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.
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Affiliation(s)
- Sang Won Park
- Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Korea
- Institute of Medical Science, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Na Young Yeo
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Seonguk Kang
- Department of Convergence Security, Kangwon National University, Chuncheon, Korea
| | - Taejun Ha
- Department of Biomedical Research Institute, Kangwon National University Hospital, Chuncheon, Korea
| | - Tae-Hoon Kim
- University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea
| | - DooHee Lee
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - Dowon Kim
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - Seheon Choi
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - Minkyu Kim
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - DongHoon Lee
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - DoHyeon Kim
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - Woo Jin Kim
- Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Korea
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Seung-Joon Lee
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Yeon-Jeong Heo
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Da Hye Moon
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Seon-Sook Han
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Yoon Kim
- University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, Korea
| | - Hyun-Soo Choi
- University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea
| | - Dong Kyu Oh
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Su Yeon Lee
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - MiHyeon Park
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Chae-Man Lim
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jeongwon Heo
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.
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