ArticleTranslational psychiatry2024
Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations.
Article in Translational psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning application in the prediction of postoperative delirium among elderly patients: a systematic review and meta-analysis.Langenbeck's archives of surgery · 2026Pooled it
- Machine learning models for predicting postoperative delirium in non-cardiac surgery patients - systematic review and meta-analysis.GeroScience · 2026Pooled it
- Association between the geriatric nutritional risk index and postoperative delirium: a meta-analysis.Frontiers in psychiatry · 2026Pooled it
- Machine Learning-Based prediction models for postoperative delirium: a systematic review and Meta-Analysis.BMC psychiatry · 2025Pooled it
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- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Development and multicenter validation of a dynamic nomogram for early postoperative neurocognitive disorder in older adults with hip fracture.BMC geriatrics · 2026Article
- Understanding psychiatrist readiness for AI: a study of access, self-efficacy, trust, and design expectations.BMC health services research · 2026Article
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- A predictive study of glycaemic reversal in Chinese individuals with prediabetes based on machine learning: a 5-year cohort study.Frontiers in endocrinology · 2026Article
- Prediction model for postoperative delirium risk in elderly hypertensive patients: machine learning-based development and validation.Frontiers in psychiatry · 2026Article
- Development and internal validation of a perioperative nomogram for predicting post-operative delirium in geriatric patients with intertrochanteric fractures undergoing internal fixation with pre-operative low-dose dexamethasone.Frontiers in medicine · 2026Article
- Determinants of effectiveness in orthopedic ERAS: a cross-setting perspective based on the integration of nutritional status and clinical outcomes.Frontiers in nutrition · 2026Article
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11 authors.
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Abstract
Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (≥65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanations (SHAP). About 797 patients were enrolled in the study, with the incidence of POD at 9.28% (74/797). The age, renal insufficiency, chronic obstructive pulmonary disease (COPD), use of antipsychotics, lactate dehydrogenase (LDH), and C-reactive protein are used to build a nomogram for POD with an AUC of 0.71. The AUCs of five machine-learning models are 0.81 (Random Forest), 0.80 (GBM), 0.68 (AdaBoost), 0.77 (XGBoost), and 0.70 (SVM). The sensitivities of the six models range from 68.8% (logistic regression and SVM) to 91.9% (Random Forest). The precisions of the six machine-learning models range from 18.3% (logistic regression) to 67.8% (SVM). Six prediction models of POD in patients with hip fractures were constructed using logistic regression and five machine-learning algorithms. The application of machine-learning algorithms could provide convenient POD risk stratification to benefit elderly hip fracture patients.
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