Risk-adjusting Hospital Mortality Using a Comprehensive Electronic Record in an Integrated Health Care Delivery System

Objective: Using a comprehensive inpatient electronic medical record, we sought to develop a risk-adjustment methodology applicable to all hospitalized patients. Further, we assessed the impact of specific data elements on model discrimination, explanatory power, calibration, integrated discrimination improvement, net reclassification improvement, performance across different hospital units, and hospital rankings.Design: Retrospective cohort study using logistic regression with split validation.Participants: A total of 248,383 patients who experienced 391,584 hospitalizations between January 1, 2008 and August 31, 2011.Setting: Twenty-one hospitals in an integrated health care delivery system in Northern California.Results: Inpatient and 30-day mortality rates were 3.02% and 5.09%, respectively. In the validation dataset, the greatest improvement in discrimination (increase in c statistic) occurred with the introduction of laboratory data; however, subsequent addition of vital signs and end-of-life care directive data had significant effects on integrated discrimination improvement, net reclassification improvement, and hospital rankings. Use of longitudinally captured comorbidities did not improve model performance when compared with present-on-admission coding. Our final model for inpatient mortality, which included laboratory test results, vital signs, and care directives, had a c statistic of 0.883 and a pseudo-R-2 of 0.295. Results for inpatient and 30-day mortality were virtually identical.Conclusions: Risk-adjustment of hospital mortality using comprehensive electronic medical records is feasible and permits one to develop statistical models that better reflect actual clinician experience. In addition, such models can be used to assess hospital performance across specific subpopulations, including patients admitted to intensive care.

目的：利用一份全面的住院电子病历，我们试图开发一种适用于所有住院患者的风险调整方法。此外，我们评估了特定数据元素对模型判别力、解释力、校准、综合判别改善、净重新分类改善、不同医院科室的表现以及医院排名的影响。 设计：采用逻辑回归及分割验证的回顾性队列研究。 参与者：2008年1月1日至2011年8月31日期间，共有248383名患者经历了391584次住院。 地点：北加利福尼亚一个综合医疗服务体系中的21家医院。 结果：住院死亡率和30天死亡率分别为3.02%和5.09%。在验证数据集中，引入实验室数据时判别力（c统计量增加）改善最大；然而，随后添加生命体征和临终关怀指令数据对综合判别改善、净重新分类改善和医院排名有显著影响。与入院时存在的编码相比，使用纵向获取的合并症并未改善模型性能。我们最终的住院死亡率模型包含实验室检测结果、生命体征和护理指令，其c统计量为0.883，伪R²为0.295。住院死亡率和30天死亡率的结果几乎相同。 结论：利用全面的电子病历对医院死亡率进行风险调整是可行的，并且能够开发出更能反映临床医生实际经验的统计模型。此外，此类模型可用于评估特定亚人群（包括入住重症监护病房的患者）的医院表现。