Adjusting for Congenital Heart Surgery Risk Using Administrative Data

Abstract

Background: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: congenital heart surgery; outcomes; risk-adjustment.

Figure 1.. Calibration Plot in the Validation Cohort.

Calibration was assessed in the validation cohort (e.g., agreement between observed outcomes and predictions). The plot had a slope of 0.98 (standard error [SE] 0.05; p-value [for difference from 1]= 0.68) and an intercept of 0.003 (SE 0.002; p-value [for difference from 0]= 0.28).

Central Illustration.. Predictors of In-Hospital Mortality and Receiver Operating Characteristic Curve for the Final Multivariable Model.

The final multivariable model inclusive of RACHS-2 category, patient age, insurance provider, and complex chronic condition category showed excellent discrimination with a model C-statistic of 0.87 (95% CI: 0.86, 0.89).