Adjusting for Congenital Heart Surgery Risk Using Administrative Data.
Adjusting for Congenital Heart Surgery Risk Using Administrative Data.
复制标题
使用管理数据调整先天性心脏病手术风险。
DOI：
10.1016/j.jacc.2023.09.826
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发表时间：
2023
影响因子：
24
通讯作者：
Anderson,BrettR
中科院分区：
文献类型：
--
作者：
Jayaram,Natalie;Allen,Philip;Hall,Matthew;Karamlou,Tara;Woo,Joyce;Crook,Sarah;Anderson,BrettR
背景先天性心脏病手术（CHS）包括一个异质性人群的病人和手术。调整患者和程序特征的风险标准化模型可以允许对这些不同的患者和程序进行集体研究。ObjectivesWe试图使用新开发的ICD-10管理数据先天性心脏手术风险分层（RACHS-2）方法来开发CHS的风险调整模型。我们确定了所有可以分配RACHS-2评分的CHS。分层逻辑回归（医院聚集）用于识别与院内死亡率相关的患者和手术特征。使用2017年24个州住院患者数据库的数据进行模型验证。结果在2019年儿童住院患者数据库中的5，902，538例加权出院病例中，识别出22，310例儿科心脏手术并分配了RACHS-2评分。543例（2.4%）病例发生院内死亡。仅使用RACHS-2，死亡率模式的C-统计量为0.81，随着年龄的增加而提高到0.83。最终的多变量模型包括RACHS-2、年龄、付款人和先天性心脏病以外的复杂慢性疾病的存在，进一步将模型区分度提高到0.87（P< 0.001）。在验证队列中的区分度也非常好，C-统计量为0.83。结论我们创建并验证了CHS的风险调整模型，该模型考虑了管理数据中与院内死亡率相关的患者和手术特征，包括新开发的RACHS-2。我们的风险模型将在卫生服务研究和质量改进计划中发挥关键作用。
BackgroundCongenital heart surgery (CHS) encompasses a heterogeneous population of patients and surgeries. Risk standardization models that adjust for patient and procedural characteristics can allow for collective study of these disparate patients and procedures.ObjectivesWe sought to develop a risk-adjustment model for CHS using the newly developed Risk Stratification for Congenital Heart Surgery for ICD-10 Administrative Data (RACHS-2) methodology.MethodsWithin the Kids’ Inpatient Database 2019, we identified all CHSs that could be assigned a RACHS-2 score. Hierarchical logistic regression (clustered on hospital) was used to identify patient and procedural characteristics associated with in-hospital mortality. Model validation was performed using data from 24 State Inpatient Databases during 2017.ResultsOf 5,902,538 total weighted hospital discharges in the Kids’ Inpatient Database 2019, 22,310 pediatric cardiac surgeries were identified and assigned a RACHS-2 score. In-hospital mortality occurred in 543 (2.4%) of cases. Using only RACHS-2, the mortality mode had a C-statistic of 0.81 that improved to 0.83 with the addition of age. A final multivariable model inclusive of RACHS-2, age, payer, and presence of a complex chronic condition outside of congenital heart disease further improved model discrimination to 0.87 (P< 0.001). Discrimination in the validation cohort was also very good with a C-statistic of 0.83.ConclusionsWe created and validated a risk-adjustment model for CHS that accounts for patient and procedural characteristics associated with in-hospital mortality available in administrative data, including the newly developed RACHS-2. Our risk model will be critical for use in health services research and quality improvement initiatives.