Lee Sungjoo, Hong Sungjun, Cha Won Chul, Kim Kyunga
Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
JMIR Med Inform. 2020 Mar 26;8(3):e16117. doi: 10.2196/16117.
A timely decision in the initial stages for patients with an acute illness is important. However, only a few studies have determined the prognosis of patients based on insufficient laboratory data during the initial stages of treatment.
This study aimed to develop and validate time adaptive prediction models to predict the severity of illness in the emergency department (ED) using highly sparse laboratory test data (test order status and test results) and a machine learning approach.
This retrospective study used ED data from a tertiary academic hospital in Seoul, Korea. Two different models were developed based on laboratory test data: order status only (OSO) and order status and results (OSR) models. A binary composite adverse outcome was used, including mortality or hospitalization in the intensive care unit. Both models were evaluated using various performance criteria, including the area under the receiver operating characteristic curve (AUC) and balanced accuracy (BA). Clinical usefulness was examined by determining the positive likelihood ratio (PLR) and negative likelihood ratio (NLR).
Of 9491 eligible patients in the ED (mean age, 55.2 years, SD 17.7 years; 4839/9491, 51.0% women), the model development cohort and validation cohort included 6645 and 2846 patients, respectively. The OSR model generally exhibited better performance (AUC=0.88, BA=0.81) than the OSO model (AUC=0.80, BA=0.74). The OSR model was more informative than the OSO model to predict patients at low or high risk of adverse outcomes (P<.001 for differences in both PLR and NLR).
Early-stage adverse outcomes for febrile patients could be predicted using machine learning models of highly sparse data including test order status and laboratory test results. This prediction tool could help medical professionals who are simultaneously treating the same patient share information, lead dynamic communication, and consequently prevent medical errors.
对于急性病患者，在初始阶段做出及时决策很重要。然而，仅有少数研究基于治疗初始阶段不充分的实验室数据来确定患者的预后。
本研究旨在开发并验证时间自适应预测模型，以使用高度稀疏的实验室检测数据（检测医嘱状态和检测结果）及机器学习方法来预测急诊科患者的疾病严重程度。
这项回顾性研究使用了韩国首尔一家三级学术医院的急诊科数据。基于实验室检测数据开发了两种不同模型：仅医嘱状态（OSO）模型和医嘱状态与结果（OSR）模型。采用二元复合不良结局，包括死亡或入住重症监护病房。使用包括受试者操作特征曲线下面积（AUC）和平衡准确性（BA）在内的各种性能标准对两种模型进行评估。通过确定阳性似然比（PLR）和阴性似然比（NLR）来检验临床实用性。
在急诊科的9491例符合条件的患者中（平均年龄55.2岁，标准差17.7岁；4839/9491，51.0%为女性），模型开发队列和验证队列分别包括6645例和2846例患者。OSR模型总体表现（AUC = 0.88，BA = 0.81）优于OSO模型（AUC = 0.80，BA = 0.74）。在预测不良结局低风险或高风险患者方面，OSR模型比OSO模型更具信息量（PLR和NLR的差异均P<0.001）。
使用包括检测医嘱状态和实验室检测结果在内的高度稀疏数据的机器学习模型，可以预测发热患者的早期不良结局。这种预测工具可以帮助同时治疗同一患者的医疗专业人员共享信息、进行动态沟通，从而预防医疗差错。
