Risk Prediction Models for Preoperative Deep Vein Thrombosis in Older Patients with Hip Fracture: A Systematic Review and Meta-Analysis

医学 荟萃分析 置信区间 髋部骨折 深静脉 接收机工作特性 系统回顾 人口 风险评估 队列研究 血栓形成 物理疗法 外科 梅德林 内科学 骨质疏松症 环境卫生 计算机科学 法学 计算机安全 政治学
作者
Huali Guo,Kuankuan Xu,Fangfang Deng,Qingqing Chen,Jie Liang,Kun Zhang
出处
期刊:Clinical and Applied Thrombosis-Hemostasis [SAGE Publishing]
卷期号:30: 10760296241285565-10760296241285565 被引量:4
标识
DOI:10.1177/10760296241285565
摘要

Objective To systematically assess the risk prediction models for preoperative deep vein thrombosis in older patients with hip fractures. Method We searched four databases for literature through November 17, 2023. We included patients aged ≥60 with hip fractures and considered English-language case-control or cohort studies that focused on developing and/or validating risk prediction models for DVT in this population. Excluded were studies that solely analyzed risk factors without constructing a prediction model, had fewer than 2 predictive variables, or were not available in full-text or were duplicate publications. The Predictive Model Bias Risk Assessment tool was utilized to evaluate risk of bias. The area under the curve (AUC) values were meta-analyzed using R Studio software. The I 2 index and Cochrane q test were employed to assess heterogeneity. Additionally, sensitivity analysis was performed by systematically removing individual studies to explore the sources of heterogeneity. Results A total of 1880 studies were gathered. Out of these, seven studies were included, encompassing 8 models. The most commonly utilized factors in the models were D-dimer and the time from injury to admission. The pooled AUC value for the validation of 8 models was 0.84 (95% confidence interval: 0.80-0.87), indicating robust model performance. Conclusion Current risk prediction models for preoperative DVT in elderly hip fracture patients are still in the developmental phase. Future research should focus on developing new models with larger sample sizes, robust study designs, and multicenter external validation.
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