ArticleThe Journal of thoracic and cardiovascular surgery2025
Development of American Association for Thoracic Surgery Quality Gateway outcome models, analytics, and visualizations for quality assurance.
Article in The Journal of thoracic and cardiovascular surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- The 90-day blind spot: A surgical imperative after Kuehn and colleagues.JTCVS techniques · 2026Article
- Multi-Output Machine Learning for Prediction of Postoperative Outcomes After Cardiac Surgery Using Patient Blood Management Biomarkers.Journal of clinical medicine · 2026Article
- Validation of current mortality risk prediction models in patients with cirrhosis undergoing cardiac surgery.JTCVS open · 2026Article
- American Association for Thoracic Surgery Quality Gateway: A surgeon case study of its application in adult cardiac surgery for quality assurance.The Journal of thoracic and cardiovascular surgery · 2025Article
- Harnessing the power of virtual (digital) twins: Graphical causal tools for understanding patient and hospital differences.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectiveThe study objective was to develop comprehensive quality assurance models for procedural outcomes after adult cardiac surgery.
methodsBased on 52,792 cardiac operations in adults performed in 19 hospitals of 3 high-performing hospital systems, models were developed for operative mortality (n = 1271), stroke (n = 895), deep sternal wound infection (n = 122), prolonged intubation (6182), renal failure (1265), prolonged postoperative stay (n = 5418), and reoperations (n = 1693). Random forest quantile classification, a method tailored for challenges of rare events, and model-free variable priority screening were used to identify predictors of events.
resultsA small set of preoperative variables was sufficient to model procedural outcomes for virtually all cardiac operations, including older age; advanced symptoms; left ventricular, pulmonary, renal, and hepatic dysfunction; lower albumin; higher acuity; and greater complexity of the planned operation. Geometric mean performance ranged from .63 to .76. Calibration covered large areas of probability. Continuous risk factors provided high information content, and their association with outcomes was visualized with partial plots. These risk factors differed in strength and configuration among hospitals, as did their risk-adjusted outcomes according to patient risk as determined by counterfactual causal inference within a framework of virtual (digital) twins.
conclusionsBy using a small set of variables and contemporary machine-learning methods, comprehensive models for procedural operative mortality and major morbidity after adult cardiac surgery were developed based on data from 3 exemplary hospital systems. They provide surgeons, their patients, and hospital systems with 21st century tools for assessing their risks compared with these advanced hospital systems and improving cardiac surgery quality.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.