Improving Risk Adjustment in the Assessment of Congenital Heart Center Surgical Quality.

BACKGROUND: Understanding center performance in congenital heart surgery remains challenging, with limitations to standard regression approaches. Modern causal inference methods may improve estimation of expected mortality through better balancing case-mix but have not been studied.

METHODS: Benchmark operations (BMO) across 115 centers (2016-2022) from the STS Congenital Database were included. The standard approach uses mixed-effects logistic regression to estimate center's expected operative mortality inclusive of all patients, even those treated at the center of interest ("target") and those from other centers regardless of how closely aligned with the target. The causal approach creates tailored comparison groups, including only like patients from other centers using weights. We used three different weights (entropy balancing [EBW], stable balancing [SBW], covariate balancing propensity score [CBPS]) and compared to standard regression.

RESULTS: Among 42,579 BMO, overall operative mortality was 2.37%. Centers' mean [25, 75] regression-based expected mortality was lower (2.03% [1.42%, 2.58%]) than weighted estimates (EBW=2.10% [1.64%, 2.67%]; SBW=2.09% [1,57%, 2.69%]; CBPS=2.10% [1.63%, 2.66%]) and less variable. The mean ratio of weighted to regression mortality estimate was 1.03 for all estimates, with 25 and 75 percentiles: EBW [0.89, 1.16]; SBW [0.87, 1.15]; and CBPS [0.92, 1.17]. The distributions of operation type and other risk factors were better aligned with the target center in weighted estimates. The largest differences among methods were observed at smaller volume centers.

CONCLUSIONS: Causal inference methods constructed more tailored comparison groups for estimating center's expected mortality, with better alignment of case-mix. Adoption of newer approaches may be warranted.