Key result
Adding procedure-specific factors to mortality models improves discrimination to 0.095 over standard clinical variables alone.
Why the study?
The value of procedure-specific factors in improving understanding of expected mortality after congenital heart surgery had not been studied.
Does incorporating procedure-specific factors (PSFs) into standard clinical risk models improve mortality discrimination in patients undergoing congenital heart surgery?
Cohort (n=37,282)
Yes
Does incorporating procedure-specific factors (PSFs) into standard clinical risk models improve mortality discrimination in patients undergoing congenital heart surgery?
Incorporating procedure-specific factors into expected mortality models for congenital heart surgery modestly improves model discrimination for certain benchmark operations, which may help refine future risk models and reduce data collection burden.
Answers are grounded in this paper and cite their sources.
May refine risk prediction for benchmark operations; leaves open whether procedure-specific factors should guide clinical decisions or benchmarking.
BACKGROUND: Although collected by The Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSD) since 2013, the value of procedure-specific factors (PSFs) in improving our understanding of expected mortality has not been studied. We evaluated the contribution of PSFs across a large cohort. METHODS: Included were benchmark operations (BMO) for which PSFs are captured across 115 United States centers (2016-2022). We assessed the influence of PSFs on operative mortality discrimination and determined which specific PSFs had the most impact. Advanced modeling strategies were used given the numerous covariates and relatively low mortality rates. A baseline model (including standard STS-CHSD clinical risk variables) was compared with a baseline plus PSF model, with results reported overall and stratified by BMO. RESULTS: Among 37,282 included BMOs, overall operative mortality was 2.6%. The proportion of BMOs with at least 1 PSF recorded as present ranged from 2.7% for ventricular septal defect to 85% for Norwood. Model discrimination increased from 0.088 (baseline) to 0.095 (baseline + PSF) overall, with better discrimination for 5 of 9 BMOs. Results for individual PSFs varied by BMO and analytic method. No PSFs were retained in the final models for ventricular septal defect in any scenario. The greatest number of PSFs were retained for Norwood and truncus arterious repair. CONCLUSIONS: Incorporating PSFs into expected mortality models improved model discrimination for some BMOs, and certain PSFs had an important impact on mortality estimates, whereas others did not. These data can aid in reducing data collection burden and support ongoing refinement of risk models.
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Nathan et al. (2025) conducted a cohort in Congenital heart disease undergoing benchmark operations (n=37,282). Procedure-specific factors (PSFs) added to baseline risk model vs. Baseline model (standard clinical risk variables) was evaluated on Operative mortality model discrimination. Incorporating procedure-specific factors into expected mortality models improved overall model discrimination from 0.088 to 0.095 compared to standard clinical variables alone.
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