The Surgical Assessment and Healthcare (SAH) Index: A Risk-Adjusted Framework for Surgeon-Level Quality Audit in Gastric Cancer
Sah, B. K.; Li, J.; Zhang, M.; Jin, R.; Li, X.; Dong, C.; Chen, E.
Show abstract
Background Gastric cancer management is heterogeneous, and although the treating surgeon leads decisions across the pathway, surgeon level outcome variation remains poorly quantified. This study assessed surgeon identity as an independent predictor of survival after risk adjustment, introducing the Surgical Assessment and Healthcare (SAH) Index. Methods This single institution retrospective study (Ruijin Hospital, Shanghai Jiao Tong University; NCT07180966) included 692 patients undergoing curative-intent resection for gastric adenocarcinoma (pStage I ,II, III) in 2019 by eight consultant surgeons. Overall survival was modelled by multivariable Cox regression (primary model, 199 events, EPV 16.6; complete-case sensitivity model, N = 647). The SAH Index expressed surgeon * stage observed-to-expected ratios for five-year mortality and major morbidity (Clavien Dindo [≥] IIIa). Median follow up was 74.3 months. Results Independent predictors of survival were tumour stage (HR 2.979/step), age (HR 1.030/year), and non-distal gastrectomy (HR 1.498; all p [≤] .006). After full adjustment, surgeon identity remained significant (Wald = 14.58, df = 7, p = .042): two surgeons carried roughly double the reference hazard S6 (HR 2.219, p = .003) and S8 (HR 2.034, p = .031) both with the cohort's lowest neoadjuvant chemotherapy rates (3.0% and 7.0% versus 17.6%), implicating pre-operative pathway decisions. The effect persisted in the sensitivity model (MSI also prognostic, HR 3.162, p = .007). Morbidity benchmarking flagged no surgeon for excess complications (no Tier 2 flags) and one survival-outlier cell (S6, Stage II; Tier 3). Conclusion Surgeon identity is independently associated with survival in gastric cancer beyond measurable case mix. The SAH Index offers a reproducible tool for institutional and inter-hospital benchmarking, with tier assignments stable across all four prespecified weighting scenarios confirming tier classification is independent of weight specification.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon Cancer 92%
- Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates 91%
- Integrative Molecular Characterization of Sarcomatoid and Rhabdoid Renal Cell Carcinoma Reveals Determinants of Poor Prognosis and Response to Immune Checkpoint Inhibitors 91%
Similar papers in this journal
- Prognostic pan-cancer and single-cancer models: A large-scale analysis using a real-world clinico-genomic database 90%
- Survival benefits of cytoreductive nephrectomy in patients with metastatic renal cell carcinoma: evidence from a SEER-based retrospective cohort study 89%
- COVID-19 Mortality Risk Assessment: An International Multi-Center Study 89%
Similar papers in this journal
- Quantifying and mitigating the impact of the COVID-19 pandemic on outcomes in colorectal cancer 90%
- Barrett's esophagus is the precursor of all esophageal adenocarcinomas 89%
- GATA4 and GATA6 loss-of-expression is associated with extinction of the classical programme and poor outcome in pancreatic ductal adenocarcinoma 89%
Similar papers in this journal
- Association of COVID-19 vaccines ChAdOx1 and BNT162b2 with major venous, arterial, or thrombocytopenic events: whole population cohort study in 46 million adults in England 87%
- Outcome of COVID-19 in hospitalised immunocompromised patients: an analysis of the WHO ISARIC CCP-UK prospective cohort study 86%
- Assessing a causal relationship between circulating lipids and breast cancer risk: Mendelian randomization study 86%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.