住院病人死亡风险实时预警系统的研究与应用卡塔尔世界杯8强波胆分析利用电子病历数据进行前瞻性研究%A Ye,Chengyin %A Wang,Oliver %A Liu,Modi %A Zheng,Le %A Xia,Minjie %A Hao,Shiying %A Jin,Bo %A Jin,Hua %A Zhu,Chunqing %A Huang,Chao Jung %A Gao,Peng %A Ellrodt,Gray %A Brennan,Denny %A Stearns,Frank %A Sylvester,Karl G %A Widen,Eric %A McElhinney,Doff B %A Ling，雪峰%+斯坦福大学外科，斯坦福大学巴斯德大道300号格兰特大厦S370，美国加州，94305,1 6504279198，bxling@stanford.edu %K住院患者%K死亡率%K风险评估%K电子健康记录%K机器学习%D 2019 %7 05.07.2019 %9背景:一些住院患者的病情迅速恶化可归因于疾病进展或入院后分诊和护理分配水平不完善。早期预警系统(EWS)可识别随后院内死亡高风险患者，是确保患者安全和护理质量以及减少可避免伤害和成本的有效工具。目的:本研究的目的是对实时EWS进行前瞻性验证，以预测住院期间死亡率高的患者。方法:收集两家急性伯克希尔卫生系统医院的全系统电子病历(EMR)数据，包括2015年1月1日至2017年9月30日期间入院的54,246名住院患者，其中2.30%(1248/54,246)导致院内死亡。探索和比较了多种机器学习方法(线性和非线性)。选择基于树的随机森林方法来开发院内死亡率评估的预测应用。在构建模型后，我们前瞻性地验证了算法作为住院患者死亡率的实时EWS。结果:EWS算法对患者入院后住院死亡概率的日常和长期风险进行评分，并将其分层为不同的风险组。 In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS. Conclusions: In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients’ better health outcomes in target medical facilities. %M 31278734 %R 10.2196/13719 %U //www.mybigtv.com/2019/7/e13719/ %U https://doi.org/10.2196/13719 %U http://www.ncbi.nlm.nih.gov/pubmed/31278734

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