Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention's Adult Sepsis Event Criteria and Routine Electronic Clinical Data.
Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention's Adult Sepsis Event Criteria and Routine Electronic Clinical Data.
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使用疾病控制和预防中心的成人脓毒症事件标准和常规电子临床数据对脓毒症死亡率进行风险调整，以促进医院比较。
DOI：
10.1097/cce.0000000000000049
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发表时间：
2019
影响因子：
--
通讯作者：
Klompas,Michael
中科院分区：
文献类型：
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作者：
Rhee,Chanu;Wang,Rui;Song,Yue;Zhang,Zilu;Kadri,SameerS;Septimus,EdwardJ;Fram,David;Jin,Robert;Poland,RussellE;Hickok,Jason;Sands,Kenneth;Klompas,Michael
目的：医院级脓毒症死亡率的变异性可能是由于病例组合、护理质量或诊断和编码实践的差异。疾病控制和预防中心的成人脓毒症事件定义可以促进医院之间脓毒症死亡率的客观比较，但需要严格的风险调整工具。我们使用行政和电子健康记录数据开发了成人脓毒症事件的风险调整模型。设计：回顾性队列研究。设置：Cerner HealthFacts的136家美国医院（推导数据集）和137家HCA Healthcare医院（验证数据集）。患者：总共95，154名住院成人患者（推导）和201，997名患者（验证）符合疾病控制和预防中心成人脓毒症事件标准。干预措施：无。测量和主要结果：我们使用行政和电子健康记录数据创建了日益复杂的logistic回归模型，以预测住院死亡率。使用人口统计学、合并症和入院时疾病严重程度编码标志物的管理模型在Cerner队列中实现了0.776（95%CI，0.770-0.783）的受试者工作曲线下面积，在较高基线风险十分位数处校准递减。一个基于电子健康记录的模型将管理数据与实验室结果、血管加压药和机械通气相结合，在推导队列中获得了0.826（95%CI，0.820-0.831）的受试者工作曲线下面积，在验证队列中获得了0.827（95%CI，0.824-0.829）的受试者工作曲线下面积，校准效果优于管理模型。添加生命体征和格拉斯哥昏迷评分最低限度地提高performance.Conclusions：模型结合电子健康记录数据准确预测医院死亡率为成人败血症事件的患者，并优于模型单独使用管理数据。利用实验室检查结果、血管加压药和无生命体征的机械通气可以在数据收集需求和模型性能之间实现良好的平衡，但基于电子健康记录的模型必须注意数据质量和可用性的潜在变化。随着这些风险调整模型的持续测试和改进，成人脓毒症事件监测可能会对医院脓毒症结局进行更有意义的比较，并为护理质量提供重要的窗口。
Objectives:Variability in hospital-level sepsis mortality rates may be due to differences in case mix, quality of care, or diagnosis and coding practices. Centers for Disease Control and Prevention’s Adult Sepsis Event definition could facilitate objective comparisons of sepsis mortality rates between hospitals but requires rigorous risk-adjustment tools. We developed risk-adjustment models for Adult Sepsis Events using administrative and electronic health record data.Design:Retrospective cohort study.Setting:One hundred thirty-six US hospitals in Cerner HealthFacts (derivation dataset) and 137 HCA Healthcare hospitals (validation dataset).Patients:A total of 95,154 hospitalized adult patients (derivation) and 201,997 patients (validation) meeting Centers for Disease Control and Prevention Adult Sepsis Event criteria.Interventions:None.Measurements and Main Results:We created logistic regression models of increasing complexity using administrative and electronic health record data to predict in-hospital mortality. An administrative model using demographics, comorbidities, and coded markers of severity of illness at admission achieved an area under the receiver operating curve of 0.776 (95% CI, 0.770–0.783) in the Cerner cohort, with diminishing calibration at higher baseline risk deciles. An electronic health record–based model that integrated administrative data with laboratory results, vasopressors, and mechanical ventilation achieved an area under the receiver operating curve of 0.826 (95% CI, 0.820–0.831) in the derivation cohort and 0.827 (95% CI, 0.824–0.829) in the validation cohort, with better calibration than the administrative model. Adding vital signs and Glasgow Coma Score minimally improved performance.Conclusions:Models incorporating electronic health record data accurately predict hospital mortality for patients with Adult Sepsis Events and outperform models using administrative data alone. Utilizing laboratory test results, vasopressors, and mechanical ventilation without vital signs may achieve a good balance between data collection needs and model performance, but electronic health record–based models must be attentive to potential variability in data quality and availability. With ongoing testing and refinement of these risk-adjustment models, Adult Sepsis Event surveillance may enable more meaningful comparisons of hospital sepsis outcomes and provide an important window into quality of care.