ArticleAnnals of medicine2024
Development and validation of machine learning models to predict perioperative transfusion risk for hip fractures in the elderly.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for postoperative infections in patients with hip fractures: a systematic review and critical appraisal.Frontiers in medicine · 2026Pooled it
- Machine learning-based prediction of intraoperative blood transfusion in major surgery: exploiting clinical variables and systemic inflammatory indices.Annals of medicine · 2026Article
- Development and validation of a machine learning model for predicting preoperative deep vein thrombosis in elderly hip fracture patients.Frontiers in medicine · 2026Article
- Perioperative Anemia and Transfusion Management in Older Adults with Osteoporotic Hip Fractures: A Narrative Review of Current Evidence and Emerging Strategies.Clinical interventions in aging · 2026Review
- Development of an online prediction tool for immunotherapy-related adverse events in patients with advanced NSCLC based on machine learning and temporal validation.Frontiers in oncology · 2026Article
- On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality.BJS open · 2025Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Machine Learning Approaches for Predicting Intraoperative Blood Transfusion in Partial Hip Arthroplasty.Journal of clinical medicine · 2025Article
- Development and validation of machine learning models to predict vertebral artery injury by C2 pedicle screws.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Blood transfusion in percutaneous left atrial appendage occlusion: a nationwide analysis of incidence, predictors, and outcomes.BMC cardiovascular disorders · 2025Article
- A nomogram for one-year risk of death after hip fracture.Frontiers in medicine · 2025Article
- All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning.Clinical interventions in aging · 2025Article
Corrections and comments
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with hip fractures frequently need to receive perioperative transfusions of concentrated red blood cells due to preoperative anemia or surgical blood loss. However, the use of perioperative blood products increases the risk of adverse events, and the shortage of blood products is prompting us to minimize blood transfusion. Our study aimed to construct a machine learning algorithm predictive model to identify patients at high risk for perioperative transfusion early in hospital admission and to manage their patient blood to reduce transfusion requirements.
methodsThis study collected patients hospitalized for hip fractures at a university hospital from May 2016 to November 2022. All patients included in the analysis were randomly divided into a training set and validation set according to 70:30. Eight machine learning algorithms, CART, GBM, KNN, LR, NNet, RF, SVM, and XGBoost, were used to construct the prediction models. The models were evaluated for discrimination, calibration, and clinical utility, and the best prediction model was selected.
resultsA total of 805 patients were included in the study, of whom 306 received transfusions during the perioperative period. We screened eight features used to construct the prediction model: age, fracture time, fracture type, hemoglobin, albumin, creatinine, calcium ion, and activated partial thromboplastin time. After evaluating and comparing the performance of each of the eight models, the model constructed by the XGBoost algorithm had the best performance, with MCC values of 0.828 and 0.939 in the training and validation sets, respectively. In addition, it had good calibration and clinical utility in both the training and validation sets.
conclusionThe model constructed by the XGBoost algorithm has the best performance, using this model to identify patients at high risk for transfusion early in their admission and promptly incorporating them into a patient blood management plan can help reduce the risk of transfusion.
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