Evaluating NSQIP Outcomes According to the Clavien–Dindo Classification: A Model to Estimate Global Outcome Measures Following Hepatopancreaticobiliary Surgery
Bibliographic record
Abstract
Background: The National Surgical Quality Improvement Program (NSQIP) database provides one of the largest repositories of surgical outcome data—guiding local, national, and international quality improvement and research. We aim to describe a model to estimate Clavien–Dindo complication (CDC) rates from NSQIP data to enable comprehensive outcome measurement, allowing an NSQIP-based surrogate measure for longer-term outcomes. Methods: This is a validation study of a model to estimate CDCs from NSQIP data for pancreaticoduodenectomy (PD) and hepatic resection (HR). The primary objective of this study is to evaluate whether our method to estimate CDCs ≥ 3 outcomes from NSQIP data results in similar serious complication rates to large benchmark studies on outcomes following PD and HR. Secondary outcomes evaluate whether specific NSQIP outcomes provide adequate information to estimate CDC grades I-V following PD and HR. Results: We evaluated 20,575 patients undergoing PD, with 71.3% having pancreatic ductal adenocarcinoma. Comparing CDCs ≥ 3 complications for NSQIP and benchmark PD patients, we estimated a 23.2% rate with our model, which was significantly lower than the reported 27.6% in the benchmark study (p < 0.001). Additionally, the benchmark reported higher complication rates for every CDC grade compared to our estimates using NSQIP PD patients (p < 0.001). Further, we evaluated 29,809 patients within NSQIP undergoing HR, where most patients with a diagnosis listed had colorectal cancer metastases (30.8%). Compared to the benchmark HR study (n = 2159), the NSQIP patients were less likely to have hepatic resection for malignancy (57.7% vs. 84.0%; p < 0.001). Comparing CDCs ≥ 3 complications following HR demonstrated that rates were clinically similar (13.0% vs. 15.8%) but statistically different between the benchmark study and NSQIP data (p < 0.001). Additionally, the NSQIP patients had lower rates of estimated complications for nearly all CDC grades (p < 0.001). Conclusions: This is the first reported method to estimate aggregate morbidity from NSQIP data. Results demonstrate that despite differences in this and comparator cohorts, this model may underestimate CDC grade 1–2 complications but provide similar rates of CDCs ≥ 3 complications compared to benchmark studies. Future studies to validate or modify this estimation method are warranted and may allow extrapolation of short-term NSQIP measures to oncologic, quality of life, and long-term outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".