Zunyi Medical University, Guizhou, 563003, China.
Department of Nephrology, Guizhou Provincial People's Hospital, Guizhou, 550002, China.
Sci Rep. 2024 Mar 21;14(1):6760. doi: 10.1038/s41598-024-55161-y.
The mortality rates for patients undergoing hemodialysis (HD) remain unacceptably high compared to the general population, and more specific information about the causes of death is not known. The study aimed to develop and validate a risk prediction model that uses common clinical factors to predict the probability of cardiovascular events in maintenance hemodialysis (MHD) patients. The study involved 3488 adult patients who received regular scheduled hemodialysis treatment at 20 hemodialysis centers in southwest China between June 2015 and August 2020, with follow-up until August 2021. The optimal parameter set was identified by multivariable Cox regression analyses and Cross-LASSO regression analyses and was used to establish a nomogram for predicting the risk of cardiovascular events in maintenance hemodialysis patients at 3 and 5 years. The performance of the model was evaluated using the consistency index (Harrell's C-index), the area under the receiver operating characteristic (ROC) curve, and calibration plots. The model was validated by tenfold cross-validation and bootstrapping with 1000 resamples. In the derivation cohort, the model yields an AUC of 0.764 [95% confidence interval (CI), 0.737-0.790] and 0.793 [CI, 0.757-0.829] for predicting the risk of cardiovascular events of MHD patients at 3 and 5 years. In the internal validation cohort AUC of 0.803 [95% CI, 0.756-0.849], AUC of 0.766 [95% CI, 0.686-0.846], and the external validation cohort AUC of 0.826 [95% CI, 0.765-0.888], AUC of 0.817 [95% CI, 0.745-0.889] at 3 and 5 years. The model's calibration curve is close to the ideal diagonal. By tenfold cross-validation analyses, the 3- and 5-year risk of cardiovascular events (AUC 0.732 and 0.771, respectively). By the bootstrap resampling method, the derivation cohort and validation cohort (Harrell's C-index 0.695 and 0.667, respectively) showed good uniformity with the model. The constructed model accurately predicted cardiovascular events of MHD patients in the 3rd and 5th years after dialysis. And the further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.
血液透析(HD)患者的死亡率与普通人群相比仍然高得令人无法接受,并且对于死亡原因的更具体信息尚不清楚。本研究旨在开发和验证一种风险预测模型,该模型使用常见的临床因素来预测维持性血液透析(MHD)患者发生心血管事件的概率。该研究纳入了 2015 年 6 月至 2020 年 8 月期间在中国西南部的 20 个血液透析中心接受定期计划血液透析治疗的 3488 名成年患者,随访至 2021 年 8 月。通过多变量 Cox 回归分析和 Cross-LASSO 回归分析确定最佳参数集,并用于建立预测 MHD 患者 3 年和 5 年心血管事件风险的列线图。通过一致性指数(Harrell 的 C 指数)、接受者操作特征(ROC)曲线下面积和校准图评估模型的性能。通过 10 倍交叉验证和 1000 次重采样的 bootstrap 验证对模型进行验证。在推导队列中,该模型对 MHD 患者 3 年和 5 年心血管事件风险的预测 AUC 分别为 0.764[95%置信区间(CI),0.737-0.790]和 0.793[CI,0.757-0.829]。内部验证队列的 AUC 为 0.803[95%CI,0.756-0.849],AUC 为 0.766[95%CI,0.686-0.846],外部验证队列的 AUC 为 0.826[95%CI,0.765-0.888],AUC 为 0.817[95%CI,0.745-0.889],分别为 3 年和 5 年。模型的校准曲线接近理想的对角线。通过 10 倍交叉验证分析,预测心血管事件的 3 年和 5 年风险(AUC 分别为 0.732 和 0.771)。通过 bootstrap 重采样方法,推导队列和验证队列(Harrell 的 C 指数分别为 0.695 和 0.667)显示出与模型的良好一致性。所构建的模型准确预测了透析后第 3 年和第 5 年 MHD 患者的心血管事件。需要进一步研究确定使用风险预测工具是否能改善临床结局。