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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".