Abstract：
Congenital 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.
We 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.
Within 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.
Of 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.
We 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.
We 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.
Within 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.
Of 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.
We 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.
摘要：
背景：先天性心脏手术（CHS）包括异质人群的患者和手术。针对患者和程序特征进行调整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。
目的：我们试图使用新开发的ICD-10管理数据的先天性心脏手术风险分层（RACHS-2）方法开发CHS的风险调整模型。
方法：在2019年儿童住院患者数据库中，我们确定了所有可以分配RACHS-2评分的CHSs。采用分层逻辑回归（集中于医院）来确定与住院死亡率相关的患者和手术特征。使用2017年24个州住院数据库的数据进行模型验证。
结果：在2019年儿童住院数据库中的5,902,538例加权出院中，确定了22,310例小儿心脏手术，并分配了RACHS-2评分。543例（2.4％）发生住院死亡率。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,先天性心脏病以外的复杂慢性疾病的存在进一步将模型判别提高到0.87(P<0.001)。验证队列中的辨别也非常好,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，该模型考虑了行政数据中与住院死亡率相关的患者和程序特征，包括新开发的RACHS-2。我们的风险模型对于用于卫生服务研究和质量改进计划至关重要。
目的：我们试图使用新开发的ICD-10管理数据的先天性心脏手术风险分层（RACHS-2）方法开发CHS的风险调整模型。
方法：在2019年儿童住院患者数据库中，我们确定了所有可以分配RACHS-2评分的CHSs。采用分层逻辑回归（集中于医院）来确定与住院死亡率相关的患者和手术特征。使用2017年24个州住院数据库的数据进行模型验证。
结果：在2019年儿童住院数据库中的5,902,538例加权出院中，确定了22,310例小儿心脏手术，并分配了RACHS-2评分。543例（2.4％）发生住院死亡率。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,先天性心脏病以外的复杂慢性疾病的存在进一步将模型判别提高到0.87(P<0.001)。验证队列中的辨别也非常好,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，该模型考虑了行政数据中与住院死亡率相关的患者和程序特征，包括新开发的RACHS-2。我们的风险模型对于用于卫生服务研究和质量改进计划至关重要。
