Zhang Danfeng, Zhao Tian, Gao Liting, Zhu Huan, Jin Haowei, Liu Guiling, Wang Deguang
Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Institute of Kidney Disease, Inflammation & Immunity-mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
BMC Nephrol. 2025 May 2;26(1):223. doi: 10.1186/s12882-025-04124-0.
This study aimed to develop a nomogram to predict peritoneal dialysis (PD) adequacy in incident PD patients and identify those at high risk for low Kt/Vurea PD function.
We retrospectively analyzed 141 incident PD patients from January 2021 to January 2024. Baseline characteristics, including BMI, hemoglobin levels, and high transport PD membrane, were compared between patients with and without adequate PD function. Univariate logistic regression, LASSO analysis, and Random Forest (RF) algorithms were employed to identify potential biomarkers. Significant predictors were integrated into a multivariable logistic regression model to construct a predictive nomogram.
The study found that 32.1% of patients had low total Kt/Vurea. Significant predictors of low Kt/Vurea included smoking (OR 2.23, CI 1.47-5.85), BMI (OR 1.35, CI 1.17-1.59), hemoglobin levels (OR 0.98, CI 0.95-0.99), and High transport (OR 0.2., CI 0.04-0.72). These factors were incorporated into a nomogram, which demonstrated strong predictive accuracy, with a C-Index of 0.802 in the main study group. The model's AUC was 0.778 (95% CI: 0.686-0.870), and Decision Curve Analysis (DCA) confirmed its clinical utility across a wide range of threshold probabilities.
We developed a nomogram that accurately predicts PD total Kt/Vurea in incident PD patients. This model can be a valuable tool for identifying patients at risk of low PD total Kt/Vurea, facilitating timely interventions to improve patient outcomes.
本研究旨在开发一种列线图，以预测新发腹膜透析（PD）患者的腹膜透析充分性，并识别那些腹膜尿素清除率（Kt/Vurea）低的高风险患者。
我们回顾性分析了2021年1月至2024年1月期间的141例新发腹膜透析患者。比较了腹膜透析功能充分和不充分患者的基线特征，包括体重指数（BMI）、血红蛋白水平和高转运腹膜透析膜。采用单因素逻辑回归、LASSO分析和随机森林（RF）算法来识别潜在的生物标志物。将显著的预测因素纳入多变量逻辑回归模型，以构建预测列线图。
研究发现，32.1%的患者总Kt/Vurea较低。Kt/Vurea低的显著预测因素包括吸烟（比值比[OR]2.23，可信区间[CI]1.47 - 5.85）、BMI（OR 1.35，CI 1.17 - 1.59）、血红蛋白水平（OR 0.98，CI 0.95 - 0.99）和高转运（OR 0.2，CI 0.04 - 0.72）。这些因素被纳入列线图，该列线图显示出强大的预测准确性，在主要研究组中的C指数为0.802。该模型的曲线下面积（AUC）为0.778（95%CI：0.686 - 0.870），决策曲线分析（DCA）证实了其在广泛阈值概率范围内的临床实用性。
我们开发了一种列线图，可准确预测新发腹膜透析患者的腹膜透析总Kt/Vurea。该模型可作为识别腹膜透析总Kt/Vurea低风险患者的有价值工具，有助于及时进行干预以改善患者预后。
