ArticleBMC surgery2025
Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients.
Article in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning-Based prediction models for postoperative delirium: a systematic review and Meta-Analysis.BMC psychiatry · 2025Pooled it
- The National Clinical Database Risk Calculator and the 5-Item Modified Frailty Index Predict the Development of Postoperative Delirium After Surgery for Hepatocellular Carcinoma.Annals of gastroenterological surgery · 2026Article
- Development and validation of a nomogram prediction model for perioperative delirium in older patients with osteoporotic fractures based on LASSO regression and multiple parameters.Frontiers in medicine · 2026Article
- A machine learning model for predicting obesity risk in patients with diabetes mellitus: analysis of NHANES 2007-2018.Frontiers in public health · 2025Article
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Authors and funding
6 authors.
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Abstract
objectiveThe objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy.
methodsWe applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized R software to develop and validate a nomogram model capable of accurately predicting the incidence of POD.
resultsThe final variables selected by the LASSO method were: Ramelteon, Age, Sex, Alcohol, Viral status, Cardiovascular disease, ASA class, Total bilirubin, Prothrombin time, Laparoscopic approach, and Blood transfusion. The performance of the nomogram was measured using ROC curve analysis, with an AUC of 0.854 (95% CI: 0.794-0.914) for the model. At the optimal cutoff value, the model demonstrated a sensitivity of 91.9% and a specificity of 68.8%. Model validation was performed using internal bootstrap validation to further verify the regression analysis. The ROC curve was generated by repeating the bootstrapping process 500 times, resulting in an AUC of 0.848 (95% CI: 0.786-0.904) for the model. The DCA curve representing the net benefit demonstrated the strong clinical validity of the model in predicting postoperative delirium.
conclusionOur results demonstrated that LASSO-based regression effectively constructed a nomogram model for predicting post-hepatectomy delirium.
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