School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China.
School of Computer Science, Peking University, Beijing 100871, China.
Int J Med Inform. 2023 Sep;177:105138. doi: 10.1016/j.ijmedinf.2023.105138. Epub 2023 Jun 27.
To develop the wed-based system for predicting risk of (pre)frailty among community-dwelling older adults.
(Pre)frailty was determined by physical frailty phenotype scale. A total of 2802 robust older adults aged ≥60 years from the China Health and Retirement Longitudinal Study (CHARLS) 2013-2015 survey were randomly assigned to derivation or internal validation cohort at a ratio of 8:2. Logistic regression, Random Forest, Support Vector Machine and eXtreme Gradient Boosting (XGBoost) were used to construct (pre)frailty prediction models. The Grid search and 5-fold cross validation were combined to find the optimal parameters. All models were evaluated externally using the temporal validation method via the CHARLS 2011-2013 survey. The (pre)frailty predictive system was web-based and built upon representational state transfer application program interfaces.
The incidence of (pre)frailty was 34.2 % in derivation cohort, 34.8 % in internal validation cohort, and 32.4 % in external validation cohort. The XGBoost model achieved better prediction performance in derivation and internal validation cohorts, and all models had similar performance in external validation cohort. For internal validation cohort, XGBoost model showed acceptable discrimination (AUC: 0.701, 95 % CI: [0.655-0.746]), calibration (p-value of Hosmer-Lemeshow test > 0.05; good agreement on calibration plot), overall performance (Brier score: 0.200), and clinical usefulness (decision curve analysis: more net benefit than default strategies within the threshold of 0.15-0.80). The top 3 of 14 important predictors generally available in community were age, waist circumference and cognitive function. We embedded XGBoost model into the server and this (pre)frailty predictive system is accessible at http://www.frailtyprediction.com.cn. A nomogram was also conducted to enhance the practical use.
A user-friendly web-based system was developed with good performance to assist healthcare providers to measure the probability of being (pre)frail among community-dwelling older adults in the next two years, facilitating the early identification of high-risk population of (pre)frailty. Further research is needed to validate this preliminary system across more controlled cohorts.
开发基于 wed 的系统，以预测社区居住的老年人发生（预）虚弱的风险。
采用身体虚弱表型量表确定（预）虚弱。共有 2802 名来自中国健康与退休纵向研究（CHARLS）2013-2015 年调查的≥60 岁的健壮老年人被随机分配到推导或内部验证队列，比例为 8:2。使用逻辑回归、随机森林、支持向量机和极端梯度提升（XGBoost）构建（预）虚弱预测模型。通过网格搜索和 5 折交叉验证相结合来寻找最优参数。所有模型均通过使用 CHARLS 2011-2013 年调查的时间验证方法进行外部评估。基于表示状态转移应用程序接口的（预）虚弱预测系统是基于网络的。
推导队列中（预）虚弱的发生率为 34.2%，内部验证队列为 34.8%，外部验证队列为 32.4%。在推导和内部验证队列中，XGBoost 模型表现出更好的预测性能，而所有模型在外部验证队列中的表现相似。对于内部验证队列，XGBoost 模型显示出可接受的区分度（AUC：0.701，95%CI：[0.655-0.746]）、校准（Hosmer-Lemeshow 检验的 p 值>0.05；校准图上的良好一致性）、总体性能（Brier 评分：0.200）和临床有用性（决策曲线分析：在 0.15-0.80 的阈值内比默认策略有更多的净收益）。社区中通常可用的 14 个重要预测因素的前 3 位是年龄、腰围和认知功能。我们将 XGBoost 模型嵌入到服务器中，该（预）虚弱预测系统可在 http://www.frailtyprediction.com.cn 上访问。还制作了一个列线图，以增强实际应用。
开发了一个用户友好的基于网络的系统，具有良好的性能，以帮助医疗保健提供者在未来两年内测量社区居住的老年人发生（预）虚弱的概率，有助于早期识别（预）虚弱的高危人群。需要进一步的研究来验证这个初步系统在更多受控队列中的有效性。
