ArticleJournal of the American College of Cardiology2023
Adjusting for Congenital Heart Surgery Risk Using Administrative Data.
Article in Journal of the American College of Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Public Reporting and Case Selection in Congenital Heart Surgery: Signals From a Multicenter Observational Study.Journal of the American College of Cardiology · 2026Observational
- Center Volume Not Associated with Survival Benefit of Inter-Hospital Transfer for Pediatric Cardiac Surgery.Pediatric cardiology · 2026Article
- Center Volume and Failure to Rescue in Pediatric Cardiac Surgery: Is There a Relationship?The Annals of thoracic surgery · 2026Article
- Longitudinal assessment of health-related quality of life in patients with adult congenital heart disease undergoing cardiac surgery.JTCVS open · 2024Article
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7 authors.
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Abstract
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.
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