Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-2118
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-21T18:14:16.454614Z
status: published
lens: g_space
sector: health
headline: American Association for Thoracic Surgery Quality Gateway Procedural Outcomes Models for General Thoracic Surgery Quality Assurance
dek: To develop quality assurance models for adverse procedural events after lung resection and esophagectomy for cancer. Using procedural outcomes after 16,430 lung resections and 3,068 esophagectomies for cancer at 9 hospitals in 7 hospital systems, we developed 5 individual and 2 composite outcome models from preoperative information. Random forest quantile classification for imbalanced data with model-free variable priority screening identified predictors. Out-of-bag prediction provided regularized estimates and…
gain_title: Random forest models using preoperative variables can estimate adverse procedural event risk after lung and esophageal cancer surgery, giving surgeons and hospitals tools for risk assessment and quality improvement.
problem_title: (none)
trace_subject: (none)
gain_reading: Random forest models using preoperative variables can estimate adverse procedural event risk after lung and esophageal cancer surgery, giving surgeons and hospitals tools for risk assessment and quality improvement.
gain_evidence: They provide surgeons, their patients, and hospital and hospital systems tools for patient risk assessment and for informing quality improvement initiatives in general thoracic surgery.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers analyzed procedural outcomes from 16,430 lung resections and 3,068 esophagectomies for cancer across 9 hospitals in 7 hospital systems. They trained random forest models on preoperative information to produce five individual and two composite models of adverse procedural events.

The models could help surgeons and hospital systems judge patient risk and compare institutions fairly for quality assurance. They are described as initial models, and their predictive performance was moderate, so their reliability in routine clinical use is not yet established.
limitation: Calibration covered only a modest range of probabilities and geometric mean performance was moderate, and the models are described as initial, with validation internal (out-of-bag and hospital-holdout).
tag: Evidence-backed gain
key_points: Of 71 lung resection and 63 esophagectomy preoperative variables, 35 and 29 were identified as important predictors across the seven outcome models. | Important predictors included demographics, advanced cancer, organ dysfunction, higher acuity, and planned surgical approach and resection extent. | Continuous risk factors, particularly pulmonary function, carried high-importance information. | For lung resection operative mortality, hospital-specific contrasts stayed stable when each focal hospital was excluded from the benchmark.
rundown: The method used random forest quantile classification for imbalanced data with model-free variable priority screening to select predictors. Out-of-bag prediction supplied regularized estimates and internal validation, and a hospital-holdout analysis tested whether institutional benchmarks were stable.

Out-of-bag geometric mean ranged from 0.588 to 0.713 for lung resection and 0.517 to 0.649 for esophagectomy. Risk-factor magnitude and risk-adjusted outcomes differed among hospitals, assessed through counterfactual inference within a framework of virtual twins.
sources:
- peer_reviewed | The Journal of Thoracic and Cardiovascular Surgery | https://doi.org/10.1016/j.jtcvs.2026.10.009 | 2026-10-20
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

8ccb33bb55e4555dd644a6b0d3ba6749b93c3ecff78c295c591b2b6ba9838083

previous

0000000000000000000000000000000000000000000000000000000000000000