Lipase-to-Amylase Ratio for the Prediction of Clinically Relevant Postoperative Pancreatic Fistula Following Pancreaticoduodenectomy
Bibliographic record
Abstract
OBJECTIVE: Postoperative pancreatic fistula (POPF) represents a leading cause of morbidity and mortality following major pancreatic resections. This study aimed to evaluate the use of postoperative drain fluid lipase-to-amylase ratio (LAR) for the prediction of clinically relevant fistulae (CR-POPF). METHODS: Consecutive patients undergoing pancreaticoduodenectomy between 2017 and 2021 at a tertiary centre were retrospectively reviewed. Univariable and multivariable analyses were performed to identify predictors for CR-POPF (ISGPS grade B/C). Receiver operating characteristic (ROC) curve analyses were conducted to evaluate the performance of LAR and determine optimum prediction thresholds. RESULTS: Among 130 patients, 28 (21.5%) developed CR-POPF. Variables positively associated with CR-POPF included soft gland texture, acinar cell density, diagnosis other than PDAC or chronic pancreatitis, resection without neoadjuvant therapy, and postoperative drain fluid lipase, amylase, and LAR (all P <0.05). Multivariable regression analysis identified LAR as an independent predictor of CR-POPF ( P <0.05). ROC curve analysis showed that LAR had moderate ability to predict CR-POPF on POD1 (AUC,0.64; 95%CI,0.54-0.74) and excellent ability on POD3 (AUC,0.85; 95%CI,0.78-0.92) and POD 5 (AUC,0.86; 95%CI,0.79-0.92). Optimum thresholds were consistent over PODs 1 to 5 (ratio>2.6) and associated with 92% sensitivity and 46% to 71% specificity. CONCLUSIONS: Postoperative drain fluid LAR represents a reliable predictor for the development of CR-POPF. With early prognostication, the postoperative care of patients at risk of developing high-grade fistulas may be optimized.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".