ArticleHepatobiliary surgery and nutrition2021
Machine learning predicts unpredicted deaths with high accuracy following hepatopancreatic surgery.
Article in Hepatobiliary surgery and nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of artificial intelligence in pancreatic surgery: a systematic review.Updates in surgery · 2022Pooled it
- Navigating the artificial intelligence (AI) era: a paradigm shift in clinical surgical education.Hepatobiliary surgery and nutrition · 2025Article
- The Obesity Paradox Revisited: Is Obesity Still a Protective Factor for Patients With High Comorbidity Burden or High-Complexity Procedures?Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Article
- Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study.BMC medical informatics and decision making · 2024Article
- Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC.Interdisciplinary sciences, computational life sciences · 2023Article
- Trends and Variations in Drain Use Following Pancreatoduodenectomy: Is Early Drain Removal Becoming More Common?World journal of surgery · 2023Article
- Highlights of constructing liver-relevantHepatobiliary surgery and nutrition · 2022Article
- Hidden-mortality risk among patients deemed "low-risk" following high-risk operations.Hepatobiliary surgery and nutrition · 2022Article
- Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.Cancers · 2022Review
- Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis.Frontiers in oncology · 2022Article
- Predicting Outcomes in Patients Undergoing Pancreatectomy Using Wearable Technology and Machine Learning: Prospective Cohort Study.Journal of medical Internet research · 2021Article
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11 authors.
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Abstract
backgroundMachine learning to predict morbidity and mortality-especially in a population traditionally considered low risk-has not been previously examined. We sought to characterize the incidence of death among patients with a low estimated morbidity and mortality risk based on the National Surgical Quality Improvement Program (NSQIP) estimated probability (EP), as well as develop a machine learning model to identify individuals at risk for "unpredicted death" (UD) among patients undergoing hepatopancreatic (HP) procedures.
methodsThe NSQIP database was used to identify patients who underwent elective HP surgery between 2012-2017. The risk of morbidity and mortality was stratified into three tiers (low, intermediate, or high estimated) using a k-means clustering method with bin sorting. A machine learning classification tree and multivariable regression analyses were used to predict 30-day mortality with a 10-fold cross validation. C statistics were used to compare model performance.
resultsAmong 63,507 patients who underwent an HP procedure, median patient age was 63 (IQR: 54-71) years. Patients underwent either pancreatectomy (n=38,209, 60.2%) or hepatic resection (n=25,298, 39.8%). Patients were stratified into three tiers of predicted morbidity and mortality risk based on the NSQIP EP: low (n=36,923, 58.1%), intermediate (n=23,609, 37.2%) and high risk (n=2,975, 4.7%). Among 36,923 patients with low estimated risk of morbidity and mortality, 237 patients (0.6%) experienced a UD. According to the classification tree analysis, age was the most important factor to predict UD (importance 16.9) followed by preoperative albumin level (importance: 10.8), disseminated cancer (importance: 6.5), preoperative platelet count (importance: 6.5), and sex (importance 5.9). Among patients deemed to be low risk, the c-statistic for the machine learning derived prediction model was 0.807 compared with an AUC of only 0.662 for the NSQIP EP.
conclusionsA prognostic model derived using machine learning methodology performed better than the NSQIP EP in predicting 30-day UD among low risk patients undergoing HP surgery.
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