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Published on: August 9, 2024
Causal Machine Learning for Personalized Surgical Decision-Making in Aortic Dissection: A National Fairness-Aware
Atef A Hassan1, Mohamed Ibrahim Elshami2, Ahmed Attia Shaban Soliman3
1Faculty of Medicine, Al-Azhar University, Cairo, Egypt.
Background:
Most machine learning (ML) models for aortic dissection predict mortality but do not address treatment-effect heterogeneity, fairness, or bedside implementation. We developed a causal-inference-informed framework integrating prediction, heterogeneous treatment-effect estimation, fairness auditing, and individualized risk-benefit profiling.
Methods:
Using 2016-2022 Healthcare Cost and Utilization Project National Inpatient Sample discharge data, we analyzed 45,112 aortic dissection hospitalizations. Five ML models, plus a weighted ensemble, were trained on 2016-2019 data (n = 24,475) and temporally tested on 2020-2022 data (n = 20,624). CausalForestDML estimated surgery-associated heterogeneous effects with specification-based robustness analyses. We audited fairness across race/ethnicity, insurance status, and income, and performed internal-external geographic validation.
Results:
In-hospital mortality was 11.0% (n = 4,940). The ensemble achieved an area under the receiver operating characteristic curve (AUROC) of 0.845, with stable COVID-era performance. Calibration varied substantially across models (Brier 0.076-0.161). Causal forest estimated heterogeneous surgery-associated treatment effects (interpreted as observational associations), with 39.5% of patients aged ≥80 years showing negative conditional average treatment effects versus 18.5% aged 65-80; however, all average treatment effect (ATE) 95% confidence intervals crossed zero. Dissection type-stratified analysis showed distinct patterns for Type A (ATE +0.4 pp) versus Type B (ATE +0.8 pp). Fairness was consistent across the 3 largest racial groups (AUROC 0.820-0.862). Model discrimination was preserved with race excluded (AUROC 0.834 vs. 0.836). Geographic validation preserved discrimination (0.832 ± 0.007).
Conclusion:
This framework supports risk stratification and hypothesis generation regarding treatment-effect heterogeneity, but not causal treatment recommendations. Clinical deployment requires prospective testing and independent external validation.
