Lin Hairong, Lin Mei, Xu Zhiying, Li Hong, Sun Dingce
Department of Gastroenterology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Department of Nursing, Tianjin Medical University General Hospital, Tianjin, China.
Front Public Health. 2024 Nov 27;12:1434244. doi: 10.3389/fpubh.2024.1434244. eCollection 2024.
Frailty is common in atrial fibrillation (AF) patients, but the specific risk factors contributing to frailty need further investigation. There is an urgent need for a risk prediction model to identify individuals at high risk of frailty.
This cross-sectional study aims to explore the multiple risk factors of frailty in older adult patients with AF and then construct a nomogram model to predict frailty risk.
We recruited 337 hospitalized patients over the age of 60 (average age: 69, 53.1% male) with AF between November 2021 and August 2022. Data collected included patient demographics, disease characteristics, sleep patterns, mental health status, and frailty measures. We used LASSO and ordinal regression to identify independent risk factors. These factors were then incorporated into a nomogram model to predict frailty risk. The model's performance was assessed using the concordance index (C-index) and calibration curves.
Among the AF patients, 23.1% were classified as frail and 52.2% as pre-frail. Six risk factors were identified: age, gender, history of coronary heart disease, number of chronic conditions, sleep disruption, and mental health status. The internal validation C-index was 0.821 (95% CI: 0.778-0.864; bias-corrected C-index: 0.795), and the external validation C-index was 0.819 (95% CI: 0.762-0.876; bias-corrected C-index: 0.819), demonstrating strong discriminative ability. Calibration charts for both internal and external validations closely matched the ideal curve, indicating robust predictive performance.
The nomogram developed in this study is a promising and practical tool for assessing frailty risk in AF patients, aiding clinicians in identifying those at high risk.
This study demonstrates the utility of a comprehensive predictive model based on frailty risk factors in AF patients, offering clinicians a practical tool for personalized risk assessment and management strategies.
衰弱在心房颤动（AF）患者中很常见，但导致衰弱的具体风险因素仍需进一步研究。迫切需要一种风险预测模型来识别衰弱高危个体。
本横断面研究旨在探讨老年AF患者衰弱的多种风险因素，然后构建列线图模型来预测衰弱风险。
我们招募了2021年11月至2022年8月期间337名年龄在60岁以上（平均年龄：69岁，男性占53.1%）的住院AF患者。收集的数据包括患者人口统计学特征、疾病特征、睡眠模式、心理健康状况和衰弱指标。我们使用LASSO和有序回归来确定独立风险因素。然后将这些因素纳入列线图模型以预测衰弱风险。使用一致性指数（C指数）和校准曲线评估模型性能。
在AF患者中，23.1%被归类为衰弱，52.2%为衰弱前期。确定了六个风险因素：年龄、性别、冠心病史、慢性病数量、睡眠中断和心理健康状况。内部验证C指数为0.821（95%CI：0.778-0.864；偏差校正C指数：0.795），外部验证C指数为0.819（95%CI：0.762-0.876；偏差校正C指数：0.819），表明具有很强的判别能力。内部和外部验证的校准图与理想曲线密切匹配，表明预测性能良好。
本研究开发的列线图是评估AF患者衰弱风险的一种有前景且实用的工具，有助于临床医生识别高危患者。
本研究证明了基于AF患者衰弱风险因素的综合预测模型的实用性，为临床医生提供了个性化风险评估和管理策略的实用工具。
