中国血液净化

中国血液净化 ›› 2026, Vol. 25 ›› Issue (02): 120-124.doi: 10.3969/j.issn.1671-4091.2026.02.007

• 临床研究 • 上一篇    下一篇

中老年腹膜透析患者认知障碍风险预测模型构建

蔡佳杰   史亚男   刘聪慧   李晶晶   白婷婷   张 义   

  1. 101149 北京，1 首都医科大学附属北京潞河医院肾病中心
  • 收稿日期:2025-06-05 修回日期:2025-12-04 出版日期:2026-02-12 发布日期:2026-02-02
  • 通讯作者: 史亚男 E-mail: 319916616@qq.com

Construction of a risk model for cognitive impairment in middle-aged and elderly patients undergoing peritoneal dialysis

CAI Jia- jie, SHI Ya- nan, LIU Cong- hui, LI Jing- jing, BAI Ting- ting, ZHANG Yi   

  1. 1 Department of Nephrology, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China
  • Received:2025-06-05 Revised:2025-12-04 Online:2026-02-12 Published:2026-02-02
  • Contact: 101149 北京，1首都医科大学附属北京潞河医院肾病中心 E-mail: 319916616@qq.com

摘要： 目的 分析中老年腹膜透析(peritoneal dialysis，PD)患者认知功能障碍(cognitive impairment，CI)的危险因素并构建风险预测模型。 方法 纳入2024年10月1日─12月31日首都医科大 学附属北京潞河医院腹膜透析中心的中老年PD患者，进行分组分析及预测模型构建。 结果 共纳入 138例患者，其中训练集96例，单因素分析显示CI组患者较非CI组血钾更低(t=2.150，P=0.034)；白蛋白 水平较差(t=2.310，P=0.023)；ADL评分较低(Z=-4.170，P＜0.010)、健康素养更缺乏(88.14%比 54.05%， χ²=4.930，P=0.026)，糖尿病患病率则偏高(χ²=4.930，P=0.0260)、饮酒者更多(χ²=9.810，P=0.002)；收入 低者更多(67.80%比32.43%，χ²=11.460，P＜0.001)，进一步行二元Logistics回归分析，显示，ADL评分下 降(OR=0.960,95% CI:0.930～0.990，P=0.003)、饮 酒 史(OR=16.310,95% CI:3.740～71.080，P＜0.001)、 家 庭 年 收 入 ＜10 万(OR=9.620,95%CI:2.450～37.750，P=0.001)及 服 药 依 从 性 差(OR=0.050,95% CI: 0.010～0.430，P=0.005)是中老年PD患者发生CI的独立危险因素。基于以上因素构建预测模型并绘制 列线图，结果显示训练集曲线下面积(area under curve,AUC)达到了0.884(95%CI:0.819～0.948)，准 确 率 0.781(95%CI:0.685～0.859)，敏 感 性 0.973(95%CI:0.921～1.000)，而 验 证 集 对 应 指 标 分 别 为 0.839(95%CI:0.716～0.961)、0.762(95%CI:0.605～0.879)和 0.842(95%CI：0.678～1.000)，表 明 模 型 预测性、准确性和稳定性较高，具有较好的泛化能力。 结论 研究构建的风险预测模型在对中老年PD 患者发生CI的早期预判中效能优异，具有一定的临床应用和推广价值。

关键词: 腹膜透析, 认知, 危险因素, 模型构建

Abstract: Objective To analyze the risk factors for cognitive impairment (CI) in middle-aged and elderly peritoneal dialysis (PD) patients and to develop a risk prediction model. Methods Middle-aged and elderly PD patients from the Peritoneal Dialysis Center of Beijing Luhe Hospital, Capital Medical University between October 1, 2024 and December 31, 2024 were enrolled for group analysis and prediction model construction. Results A total of 138 PD patients were included in this study, with 96 in the training set. Univariate analysis revealed that the CI group had significantly lower serum potassium levels (t=2.150, P=0.034), lower albumin levels (t=2.310, P=0.023), lower Activities of Daily Living (ADL) scores [90.00 (60.00, 100.00) vs. 100.00 (100.00, 100.00), Z=-4.170, P＜0.010), higher prevalence of diabetes (71.19% vs. 48.65%, χ²=4.930, P=0.026), greater proportion of alcohol drinkers (67.80% vs. 35.14%, χ²=9.810, P=0.002), poorer health literacy (88.14% vs. 54.05%, χ² =4.930, P=0.026), and higher proportion of low income (67.80% vs. 32.43%, χ²=11.460, P＜0.001), as compared those with the non-CI group. Binary logistic regression identified decreased ADL score (OR=0.960, 95% CI: 0.930～0.990, P=0.003), history of alcohol consumption (OR= 16.310, 95% CI: 3.740～71.080, P＜0.001), annual family income ＜100,000 yuan (OR=9.620, 95% CI: 2.450～37.750, P=0.001), and poor medication adherence (OR=0.050, 95% CI:0.010～0.430, P=0.005) as the independent risk factors for CI in middle-aged and elderly PD patients. A prediction model was constructed based on these factors, and a nomogram was drawn. The model demonstrated an area under the curve (AUC) of 0.884 (95% CI: 0.819～0.948) in the training set, with an accuracy of 0.781 (95% CI:0.685～0.859) and a sensitivity of 0.973 (95%CI: 0.921～1.000). The validation set showed a predictive accuracy of 0.839 (95% CI: 0.716～0.961), a precision of 0.762 (95% CI: 0.605～0.879), and a F1 score of 84.2% (95% CI: 67.8～ 100.0%), which indicated that the model had higher predictability, accuracy and stability, and a better generalization ability. Conclusion The risk prediction model developed in this study demonstrates excellent performance for early identification of CI in middle-aged and elderly PD patients, having potential value for clinical application and promotion.

Key words: Peritoneal dialysis, Cognitive, Risk factor, Model construction

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