ArticleScientific reports2024
Development of an individualized model for predicting postoperative delirium in elderly patients with hepatocellular carcinoma.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prediction Models for Postoperative Delirium Among Cancer Patients: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- 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
- Efficacy and safety of combined targeted therapy and immunotherapy versus targeted monotherapy in older patients with uHCC.Frontiers in oncology · 2025Article
- Development and validation of a machine learning model to predict postoperative complications following radical gastrectomy for gastric cancer.Frontiers in oncology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Postoperative delirium (POD) is a common complication in older patients with hepatocellular carcinoma (HCC) that adversely impacts clinical outcomes. We aimed to evaluate the risk factors for POD and to construct a predictive nomogram. Data for a total of 1481 older patients (training set: n=1109; validation set: n=372) who received liver resection for HCC were retrospectively retrieved from two prospective databases. The receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance. The rate of POD was 13.3% (148/1109) in the training set and 16.4% (61/372) in the validation set. Multivariate analysis of the training set revealed that factors including age, history of cerebrovascular disease, American Society of Anesthesiologists (ASA) classification, albumin level, and surgical approach had significant effects on POD. The area under the ROC curves (AUC) for the nomogram, incorporating the aforementioned predictors, was 0.798 (95% CI 0.752-0.843) and 0.808 (95% CI 0.754-0.861) for the training and validation sets, respectively. The calibration curves of both sets showed a degree of agreement between the nomogram and the actual probability. DCA demonstrated that the newly established nomogram was highly effective for clinical decision-making. We developed and validated a nomogram with high sensitivity to assist clinicians in estimating the individual risk of POD in older patients with HCC.
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