维持性血液透析并发不宁腿综合征危险因素和风险预测模型的构建-现代医学

维持性血液透析并发不宁腿综合征危险因素和风险预测模型的构建

单位：1. 天津中医药大学研究生院, 天津 301617;
2. 天津市第一中心医院肾科, 天津 300192

分类号：R459.5

出版年·卷·期（页码）：2023·51·第八期（1078-1084）

摘要：

目的：研究维持性血液透析(MHD)患者并发不宁腿综合征(RLS)的危险因素，并在此基础上构建风险预测模型。方法：选取202例行MHD的患者为建模组，使用多因素Logistic回归分析来探讨MHD患者并发RLS的危险因素，并基于危险因素构建风险预测模型；另收集103例行MHD的患者作为验证组，对模型进行内、外部验证。两组均根据是否发生不宁腿综合征分为阳性组和阴性组。结果：多因素Logistic回归分析显示，Hb＜110 g·L^-1、PTH≥350 pg·ml^-1、原发糖尿病肾病是MHD患者并发RLS的危险因素。内部验证显示Calibration校准曲线拟合良好(H-L检验显示 χ²=8.620,P=0.375)，受试者工作特征(ROC)曲线下面积(AUC)及一致性指数(C-index)均为0.763(95%CI 0.665~0.861)，灵敏度为66.7%，特异度为76.9%。验证组显示Calibration校准曲线具有良好的一致性(H-L检验显示χ²=4.028，P=0.673)，AUC与C-index均为0.852(95%CI 0.769~0.935)。两组决策曲线分析(DCA)显示临床效益良好。结论：糖尿病肾病、贫血、高PTH水平是MHD 并发 RLS的独立危险因素，构建RLS在MHD患者中的风险预测模型具有较好的校准性、区分性和临床效益，可用于辅助临床决策。

Objective: To investigate the risk factors of restless legs syndrome(RLS) in maintenance hemodialysis(MHD) patients and to construct a risk nomogram prediction model based on this. Methods: 202 cases of MHD patients were selected as the modeling group, and multifactorial Logistic regression analysis was used to explore the risk factors for the complication of RLS in patients with MHD, and a risk prediction model was constructed based on the risk factors, and 103 cases of MHD patients were collected as the validation group, and the model was internally and externally validated.The 2 groups were divided into a positive group and a negative group according to the occurrence or non-occurrence of RLS. Results: The multifactorial Logistic regression analysis showed that Hb<110 g·L^-1, PTH ≥350 pg·ml^-1, and primary diabetic nephropathy were independent risk factors for RLS in MHD patients. The Bootstrap resampling procedure was validated internally(the H-L test showed χ²=8.620,P=0.375) and the Calibration curve fitted well. The area under the ROC curve and C-index were 0.763(95% CI 0.665-0.861),the sensitivity was 66.7% and the specificity was 76.9%.The Calibration curves showed a good consistency with the external validation results of the validation group(H-L test showed χ²=4.028,P=0.673) and the AUC and C-index were 0.852(95%CI 0.769-0.935).Decision curve analysis also showed good net benefits in both groups. Conclusion: Diabetic nephropathy, anemia, and high PTH levels are independent risk factors for RLS in MHD patients, and the risk nomogram prediction model for RLS in MHD patients has good calibration, discrimination and clinical benefit which can be used to assist clinical decision making.

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