Chang Hansol, Yu Jae Yong, Lee Geun Hyeong, Heo Sejin, Lee Se Uk, Hwang Sung Yeon, Yoon Hee, Cha Won Chul, Shin Tae Gun, Sim Min Seob, Jo Ik Joon, Kim Taerim
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 115 Irwon-ro Gangnam-gu, Seoul, 06355, South Korea.
Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, 115 Irwon-ro Gangnam-gu, Seoul, 06355, South Korea.
Heliyon. 2023 Aug 17;9(8):e19210. doi: 10.1016/j.heliyon.2023.e19210. eCollection 2023 Aug.
This study developed a clinical support system based on federated learning to predict the need for a revised Korea Triage Acuity Scale (KTAS) to facilitate triage.
This was a retrospective study that used data from 11,952,887 patients in the Korean National Emergency Department Information System (NEDIS) from 2016 to 2018 for model development. Separate cohorts were created based on the emergency medical center level in the NEDIS: regional emergency medical center (REMC), local emergency medical center (LEMC), and local emergency medical institution (LEMI). External and temporal validation used data from emergency department (ED) of the study site from 2019 to 2021. Patient features obtained during the triage process and the initial KTAS scores were used to develop the prediction model. Federated learning was used to rectify the disparity in data quality between EDs. The patient's demographic information, vital signs in triage, mental status, arrival information, and initial KTAS were included in the input feature.
3,626,154 patients' visits were included in the regional emergency medical center cohort; 8,278,081 patients' visits were included in the local emergency medical center cohort; and 48,652 patients' visits were included in the local emergency medical institution cohort. The study site cohort, which is used for external and temporal validation, included 135,780 patients visits. Among the patients in the REMC and study site cohorts, KTAS level 3 patients accounted for the highest proportion at 42.4% and 45.1%, respectively, whereas in the LEMC and LEMI cohorts, KTAS level 4 patients accounted for the highest proportion. The area under the receiver operating characteristic curve for the prediction model was 0.786, 0.750, and 0.770 in the external and temporal validation. Patients with revised KTAS scores had a higher admission rate and ED mortality rate than those with unaltered KTAS scores.
This novel system might accurately predict the likelihood of KTAS acuity revision and support clinician-based triage.
本研究开发了一种基于联邦学习的临床支持系统，用于预测修订韩国分诊 acuity 量表（KTAS）的必要性，以促进分诊。
这是一项回顾性研究，使用了2016年至2018年韩国国家急诊科信息系统（NEDIS）中11,952,887例患者的数据进行模型开发。根据NEDIS中的急救医疗中心级别创建了单独的队列：区域急救医疗中心（REMC）、当地急救医疗中心（LEMC）和当地急救医疗机构（LEMI）。外部和时间验证使用了研究地点急诊科2019年至2021年的数据。分诊过程中获得的患者特征和初始KTAS分数用于开发预测模型。联邦学习用于纠正急诊科之间数据质量的差异。输入特征包括患者的人口统计学信息、分诊时的生命体征、精神状态、到达信息和初始KTAS。
区域急救医疗中心队列纳入了3,626,154例患者就诊；当地急救医疗中心队列纳入了8,278,081例患者就诊；当地急救医疗机构队列纳入了48,652例患者就诊。用于外部和时间验证的研究地点队列包括135,780例患者就诊。在REMC和研究地点队列的患者中，KTAS 3级患者的比例最高，分别为42.4%和45.1%，而在LEMC和LEMI队列中，KTAS 4级患者的比例最高。预测模型在外部和时间验证中的受试者工作特征曲线下面积分别为0.786、0.750和0.770。KTAS分数修订的患者比KTAS分数未改变的患者有更高的入院率和急诊科死亡率。
这种新型系统可能准确预测KTAS acuity修订的可能性，并支持基于临床医生的分诊。
