Department of Nephrology, Second Clinical College of Nanchong North Sichuan Medical College, Nanchong Central Hospital, Nanchong, P.R. China.
Department of Nephrology, Suining Central Hospital, Suining, P.R. China.
Ren Fail. 2023;45(2):2255686. doi: 10.1080/0886022X.2023.2255686. Epub 2023 Sep 21.
Heart failure (HF) in patients undergoing maintenance hemodialysis (MHD) increases their hospitalization rates, mortality, and economic burden significantly. We aimed to develop and validate a predictive model utilizing contemporary deep phenotyping for individual risk assessment of all-cause mortality or HF hospitalization in patients on MHD.
A retrospective review was conducted from January 2017 to October 2022, including 348 patients receiving MHD from four centers. The variables were adjusted by Cox regression analysis, and the clinical prediction model was constructed and verified.
The median follow-up durations were 14 months (interquartile range [IQR] 9-21) for the modeling set and 14 months (9-20) for the validation set. The composite outcome occurred in 72 (29.63%) of 243 patients in the modeling set and 39 (37.14%) of 105 patients in the validation set. The model predictors included age, albumin, history of cerebral hemorrhage, use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers/"sacubitril/valsartan", left ventricular ejection fraction, urea reduction ratio, N-terminal prohormone of brain natriuretic peptide, and right atrial size. The C-index was 0.834 (95% CI 0.784-0.883) for the modeling set and 0.853 (0.798, 0.908) for the validation set. The model exhibited excellent calibration across the complete risk profile, and the decision curve analysis (DCA) suggested its ability to maximize patient benefits.
The developed prediction model offered an accurate and personalized assessment of HF hospitalization risk and all-cause mortality in patients with MHD. It can be employed to identify high-risk patients and guide treatment and follow-up.
接受维持性血液透析（MHD）的心力衰竭（HF）患者的住院率、死亡率和经济负担显著增加。我们旨在开发和验证一种利用当代深度表型的预测模型，对 MHD 患者的全因死亡率或 HF 住院的个体风险进行评估。
回顾性分析了 2017 年 1 月至 2022 年 10 月期间来自四个中心的 348 名接受 MHD 的患者。采用 Cox 回归分析调整变量，构建并验证临床预测模型。
建模组中位随访时间为 14 个月（IQR 9-21），验证组中位随访时间为 14 个月（9-20）。建模组 243 例患者中有 72 例（29.63%）发生复合结局，验证组 105 例患者中有 39 例（37.14%）发生复合结局。模型预测因子包括年龄、白蛋白、脑出血史、血管紧张素转换酶抑制剂/血管紧张素 II 受体阻滞剂/“沙库巴曲缬沙坦”、左心室射血分数、尿素清除率、脑钠肽 N 末端前体和右心房大小。建模组的 C 指数为 0.834（95%CI 0.784-0.883），验证组为 0.853（0.798，0.908）。该模型在整个风险谱上表现出良好的校准度，决策曲线分析（DCA）表明其具有最大化患者获益的能力。
该预测模型为 MHD 患者 HF 住院风险和全因死亡率提供了准确的个体化评估。它可以用于识别高危患者并指导治疗和随访。
