Machine learning model for postpancreaticoduodenectomy haemorrhage prediction: an international multicentre cohort study
Objectives To develop and validate a machine learning model for precise risk stratification of postpancreaticoduodenectomy haemorrhage (PPH), enabling early identification of high-risk patients to guide clinical intervention.Design Retrospective international multicentre cohort study with model deve...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2025-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/7/e096147.full |
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| Summary: | Objectives To develop and validate a machine learning model for precise risk stratification of postpancreaticoduodenectomy haemorrhage (PPH), enabling early identification of high-risk patients to guide clinical intervention.Design Retrospective international multicentre cohort study with model development and external validation.Setting Training data from the American College of Surgeons-National Surgical Quality Improvement Program database (USA, 2014–2017) and external validation data from the National Cancer Center (China, 2014–2019).Participants 3609 patients in the training cohort and 1347 in the validation cohort undergoing pancreaticoduodenectomy. Patients with missing data or non-relevant variables were excluded.Primary and secondary outcome measures Primary outcome: clinically relevant PPH (International Study Group of Pancreatic Surgery grades B/C). Secondary outcomes: model discrimination (area under the curve (AUC)), calibration (Hosmer-Lemeshow test), clinical utility (decision curve analysis) and risk stratification performance.Results The least absolute shrinkage and selection operator (Lasso)-gradient boosting machine model identified eight predictors: albumin, haematocrit (HCT), American Society of Anesthesiologists (ASA) class, operative time, vascular resection, sepsis, reoperation and pancreatic fistula. It achieved AUCs of 0.84 (95% CI 0.82 to 0.86) in training and 0.82 (95% CI 0.78 to 0.85) in validation, outperforming logistic regression and other machine learning models. Risk stratification into low-risk, medium-risk and high-risk groups showed strong discriminatory power (AUCs: 0.72–0.75). Decision curve analysis confirmed net clinical benefit, and SHapley Additive exPlanations values highlighted HCT and operative time as top contributors. The model was deployed as an interactive application for real-time risk assessment.Conclusions This novel machine learning model for PPH prediction integrates interpretable risk stratification and demonstrates robust performance across international cohorts. Its deployment as a clinical tool may facilitate proactive management of high-risk patients. Prospective validation is warranted prior to broad implementation. |
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| ISSN: | 2044-6055 |