目的 系统评价维持性血液透析患者衰弱风险预测模型。 方法 系统检索PubMed、Embase、CINAHL、Web of Science、Cochrane Library、中国知网、万方数据知识服务平台、维普网和SinoMed等数据库，检索时间从建库至2024年8月29日。由2名研究者筛选文献和提取数据，并评价纳入研究的偏倚风险和适用性。 结果 共纳入12篇文献，涉及16个风险预测模型，11个模型进行了验证，9个模型进行了校准，候选变量为7～24个，13个模型报告了建模时AUC(0.66～0.998)，4个模型报告了内部验证时AUC(0.828～0.939)，2个模型报告了外部验证时AUC(0.865～0.904)；预测因子4～10个，年龄、合并症指数、性别和抑郁是出现频率最多的共同预测因子。模型多以列线图的形式呈现。12项研究均为高偏倚风险，11项研究在研究对象、预测因子、结局和总体显示出较好的适用性。 结论 维持性血液透析患者衰弱风险预测模型具有较好的预测性能和临床价值，但其偏倚风险较高，模型的外推性存在一定限制。
Objective To systematically evaluate the frailty risk prediction model for maintenance hemodialysis patient. Methods PubMed, Embase, CINAHL, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed were systematically searched with a timeframe from establishment of the database to August 29, 2024. Two investigators screened the literature, extracted data, and evaluated bias risk and suitability of the included studies. Results Twelve literatures were included, including 16 risk prediction models. Eleven models were validated, and 9 models were calibrated. There were 7～24 candidate variables. Thirteen models reported the area under the curve (AUC) of 0.66～0.998 at the establishment of the models; 4 models reported the AUC of 0.828～0.939 by internal validation, and 2 models reported the AUC of 0.865～0.904 by external validation. There were 4～10 predictors; age, charlson comorbidity index (CCI), gender applicability and depression were the common predictors with the highest frequency. Twelve studies had high risks of bias. Eleven studies showed better applicability to study population, predictors, outcomes and overall subjects. Conclusions The frailty risk prediction models for maintenance hemodialysis patients have better predictive performance and clinical value. However, these predictive models have higher bias, limiting their extrapolation. A variety of machine learning algorithms can be used for modeling in the future. We should pay attention to the predictors with higher frequencies in the model and carry out external validation of multiple regions, multiple centers, and large samples for these predictors, to develop predictive models with better prediction performance, more clinical utility, and higher generalized suitability.
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