维持性血液透析患者发生心力衰竭的风险预测模型构建

Developing a Risk Prediction Model for the Occurrence of Heart Failure in Patients on Maintenance Hemodialysis

摘要: 目的：构建维持性血液透析(maintenance hemodialysis, MHD)患者发生心力衰竭(heart failure, HF)的风险预测模型，分析MHD患者发生心力衰竭(HF)的因素，以早期识别并减少不良的预后。方法：回顾性收集2021~2023年青海大学附属医院住院MHD患者的资料，以MHD患者是否发生HF为结局事件分为HF组(n = 59)和非HF组(n = 297)，比较两组研究对象的基线特征，按照7:3的比例将MHD患者随机分为建模集(n = 250)和验证集(n = 106)。通过LASSO回归确定预测变量，通过二分类逻辑回归建立MHD患者发生HF的风险预测模型并开发列线图；采用受试者工作特征(ROC)曲线下面积评估模型的区分度。结果：建模集42例(16.8%)MHD患者发生HF，验证集17例(16.04%)患者发生HF。LASSO回归结合Logistic回归分析结果显示，高血压(OR = 4.05, 95% CI = 1.86~9.30)、C反应蛋白异常(OR = 3.04, 95% CI = 1.45~6.71)、透析频次3次/周(OR = 4.08, 95% CI = 1.80~9.97)、透析龄(OR = 1.18, 95% CI = 1.02~1.36)是MHD患者发生SF的独立影响因素(P<0.05)。构建包含上述4个影响因素的风险预测模型并绘制列线图。该预测模型在建模集和验证集的ROC曲线下面积分别为0.785 (95% CI = 0.7124~0.8575)和0.746 (95% CI = 0.6103~0.8821)，区分度良好。结论：高血压、C反应蛋白异常、透析频次3次/周、透析龄是MHD患者发生HF的独立危险因素；预测模型的区分度良好。

Abstract: Objective: To construct a risk prediction model for heart failure (HF) in patients on maintenance hemodialysis (MHD) and to explore the risk factors for heart failure (HF) in MHD patients so as to identify and reduce adverse prognosis at an early stage. Methods: Data of MHD patients hospitalized in the Affiliated Hospital of Qinghai University from 2021 to 2023 were collected. These patients were divided into the HF group (n = 59) and the non-HF group (n = 297) based on whether they developed HF as the outcome event. The baseline characteristics of the two groups were compared, and the patients were randomly divided into a modeling set (n = 250) and a validation set (n = 106) in a ratio of 7:3. The predictor variables were determined by LASSO regression, and a prediction model for HF in MHD patients was constructed by binary logistic regression and a nomogram was drawn. The area under the receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model. Results: HF occurred in 42 (16.8%) MHD patients in the modeling set and 17 (16.04%) patients in the validation set. LASSO regression combined with Logistic regression analysis showed that hypertension (OR = 4.05, 95% CI = 1.86~9.30), abnormal C-reactive protein (OR = 3.04, 95% CI = 1.45~6.71), dialysis frequency 3 times/week (OR = 4.08, 95% CI = 1.80~9.97), and dialysis age (OR = 1.18, 95% CI = 1.02~1.36) were independent influencing factors for SF in MHD patients (P < 0.05). A risk prediction model including the above four influencing factors was constructed and a nomogram was drawn. The areas under the ROC curve of the prediction model in the modeling set and validation set were 0.785 (95% CI = 0.7124~0.8575) and 0.746 (95% CI = 0.6103~0.8821), respectively, with good discrimination. Conclusion: Hypertension, abnormal C-reactive protein, dialysis frequency 3 times/week, and dialysis age are independent risk factors for HF in MHD patients. The prediction model has good discrimination.