Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study
Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study
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DOI：
10.1007/s11606-018-4316-y
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发表时间：
2018-06-01
影响因子：
5.7
通讯作者：
Arora, Rashi
中科院分区：
文献类型：
--
作者：
Sahni, Nishant;Simon, Gyorgy;Arora, Rashi
预测临床多样化、多情况住院患者队列的死亡是困难的。使用电子病历（EMR）数据确定1年死亡风险的预后模型可以改善临终规划和研究风险调整。确定住院患者的最终人口统计、生命体征和实验室数据是否可用于准确量化1年死亡风险。一项使用与州死亡登记相关联的电子病历数据的回顾性研究。在4年期间，6家医院网络共有59 848名住院病人。最后一组生命体征、全血细胞计数、基本和完全代谢组、人口统计信息和ICD代码。利息结果为1年内死亡。在验证数据集上测量模型性能。随机森林（RF）在判别能力上优于逻辑回归（LR）模型。使用住院最后48小时的人口统计学、生命体征和实验室数据的RF模型预测一年内死亡的AUC为0.86（0.85-0.87）。年龄、尿素氮、血小板计数、血红蛋白和肌酐是RF模型中最重要的变量。单独使用共病变量的模型AUC最低。在死亡概率高的患者组中，RF模型低估的概率小于10%。住院的最后一组EMR数据可用于准确估计多病住院患者队列中的1年死亡风险。
Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electronic medical record (EMR) data to determine 1-year death risk can improve end-of-life planning and risk adjustment for research.Determine if the final set of demographic, vital sign, and laboratory data from a hospitalization can be used to accurately quantify 1-year mortality risk.A retrospective study using electronic medical record data linked with the state death registry.A total of 59,848 hospitalized patients within a six-hospital network over a 4-year period.The last set of vital signs, complete blood count, basic and complete metabolic panel, demographic information, and ICD codes. The outcome of interest was death within 1 year.Model performance was measured on the validation data set. Random forests (RF) outperformed logisitic regression (LR) models in discriminative ability. An RF model that used the final set of demographic, vitals, and laboratory data from the final 48 h of hospitalization had an AUC of 0.86 (0.85-0.87) for predicting death within a year. Age, blood urea nitrogen, platelet count, hemoglobin, and creatinine were the most important variables in the RF model. Models that used comorbidity variables alone had the lowest AUC. In groups of patients with a high probability of death, RF models underestimated the probability by less than 10%.The last set of EMR data from a hospitalization can be used to accurately estimate the risk of 1-year mortality within a cohort of multicondition hospitalized patients.