Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons.
Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons.
复制标题
使用临床与管理数据进行风险调整对医院脓毒症死亡率比较的影响。
DOI：
10.1093/ofid/ofaa213
复制
发表时间：
2020
影响因子：
4.2
通讯作者：
Klompas,Michael
中科院分区：
文献类型：
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作者：
Rhee,Chanu;Li,Zhonghe;Wang,Rui;Song,Yue;Kadri,SameerS;Septimus,EdwardJ;Chen,Huai-Chun;Fram,David;Jin,Robert;Poland,Russell;Sands,Kenneth;Klompas,Michael
一个可靠的风险调整脓毒症的结果措施，可以补充目前的国家过程中的指标，确定离群医院和催化额外的改善护理。然而，目前还不清楚将临床数据整合到风险调整模型中是否可以识别出与仅管理数据相比类似的高绩效和低绩效医院，方法我们根据美国疾病控制和预防中心成人脓毒症事件（ASE）死亡率对200家美国医院进行了排名，并评估了应用后排名的变化（1）管理风险调整模型，包括人口统计学、合并症和严重疾病代码;（2）综合临床和管理模型，用实验室结果、血管加压药和机械通气替代疾病严重程度代码。我们评估了医院的风险调整后的ASE死亡率之间的协议时，排名到四分位使用加权kappa statistics（к）.ResultsThe队列包括4 009 631住院，其中245 808符合ASE标准。风险调整对排名有很大的影响：22/50的医院（44%）在最差的四分位数使用粗死亡率转移到更好的四分位数后，行政风险调整，和进一步的21/50（42%）的医院在最差的四分位数使用行政风险调整后转移到更好的四分位数合并临床数据。相反，14/50（28%）的医院在最好的四分位数使用行政风险调整转移到更差的四分位数与临床数据。当风险调整后使用行政与临床数据的医院四分位数排名之间的总体协议是温和的（к = 0.55）conclusionsextracting临床数据到风险调整实质上改变了医院的败血症死亡率的排名相比，单独使用行政数据。在比较医院脓毒症死亡率之前，有必要使用管理和临床数据进行综合风险调整。
BackgroundA reliable risk-adjusted sepsis outcome measure could complement current national process metrics by identifying outlier hospitals and catalyzing additional improvements in care. However, it is unclear whether integrating clinical data into risk adjustment models identifies similar high- and low-performing hospitals compared with administrative data alone, which are simpler to acquire and analyze.MethodsWe ranked 200 US hospitals by their Centers for Disease Control and Prevention Adult Sepsis Event (ASE) mortality rates and assessed how rankings changed after applying (1) an administrative risk adjustment model incorporating demographics, comorbidities, and codes for severe illness and (2) an integrated clinical and administrative model replacing severity-of-illness codes with laboratory results, vasopressors, and mechanical ventilation. We assessed agreement between hospitals’ risk-adjusted ASE mortality rates when ranked into quartiles using weighted kappa statistics (к).ResultsThe cohort included 4 009 631 hospitalizations, of which 245 808 met ASE criteria. Risk-adjustment had a large effect on rankings: 22/50 hospitals (44%) in the worst quartile using crude mortality rates shifted into better quartiles after administrative risk adjustment, and a further 21/50 (42%) of hospitals in the worst quartile using administrative risk adjustment shifted to better quartiles after incorporating clinical data. Conversely, 14/50 (28%) hospitals in the best quartile using administrative risk adjustment shifted to worse quartiles with clinical data. Overall agreement between hospital quartile rankings when risk-adjusted using administrative vs clinical data was moderate (к = 0.55).ConclusionsIncorporating clinical data into risk adjustment substantially changes rankings of hospitals’ sepsis mortality rates compared with using administrative data alone. Comprehensive risk adjustment using both administrative and clinical data is necessary before comparing hospitals by sepsis mortality rates.