Dai Rong, Peng Chuyi, Sang Tian, Cheng Meng, Wang Yiping, Zhang Lei
Department of Chinese Medicine, Anhui University of Chinese Medicine, Hefei, China.
Graduate School, Anhui University of Chinese Medicine, Hefei, China.
Front Med (Lausanne). 2023 Sep 15;10:1193754. doi: 10.3389/fmed.2023.1193754. eCollection 2023.
To construct and validate a risk prediction model for the development of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritoneal dialysis (PD).
This retrospective analysis included patients undergoing PD at the Department of Nephrology, the First Affiliated Hospital of Anhui University of Chinese Medicine, between January 2016 and January 2021. Baseline data were collected. The primary study endpoint was PDAP occurrence. Patients were divided into a training cohort ( = 264) and a validation cohort ( = 112) for model building and validation. Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to optimize the screening variables. Predictive models were developed using multifactorial logistic regression analysis with column line plots. Receiver operating characteristic (ROC) curves, calibration curves, and Hosmer-Lemeshow goodness-of-fit tests were used to verify and evaluate the discrimination and calibration of the prediction models. Decision curve analysis (DCA) was used to assess the clinical validity of the prediction models.
Five potential predictors of PDAP after PD catheterization were screened using LASSO regression analysis, including neutrophil-to-lymphocyte ratio (NLR), serum ALBumin (ALB), uric acid (UA), high sensitivity C-reactive protein (hsCRP), and diabetes mellitus (DM). Predictive models were developed by multi-factor logistic regression analysis and plotted in columns. The area under the ROC curve (AUC) values were 0.891 (95% confidence interval [CI]: 0.829-0.844) and 0.882 (95% CI: 0.722-0.957) for the training and validation cohorts, respectively. The Hosmer-Lemeshow test showed a good fit ( = 0.829 for the training cohort; = 0.602 for the validation cohort). The DCA curves indicated that the threshold probabilities for the training and validation cohorts were 4-64% and 3-90%, respectively, predicting a good net gain for the clinical model.
NLR, ALB, UA, hsCRP, and DM are independent predictors of PDAP after PD catheterization. The column line graph model constructed based on the abovementioned factors has good discriminatory and calibrating ability and helps to predict the risk of PDAP after PD catheterization.
构建并验证腹膜透析（PD）患者发生腹膜透析相关性腹膜炎（PDAP）的风险预测模型。
本回顾性分析纳入了2016年1月至2021年1月期间在安徽中医药大学第一附属医院肾内科接受PD治疗的患者。收集基线数据。主要研究终点为PDAP的发生。将患者分为训练队列（n = 264）和验证队列（n = 112）用于模型构建和验证。应用最小绝对收缩和选择算子（LASSO）回归优化筛选变量。使用多因素逻辑回归分析和柱状线图建立预测模型。采用受试者工作特征（ROC）曲线、校准曲线和Hosmer-Lemeshow拟合优度检验来验证和评估预测模型的区分度和校准度。采用决策曲线分析（DCA）评估预测模型的临床有效性。
通过LASSO回归分析筛选出PD置管后PDAP的5个潜在预测因素，包括中性粒细胞与淋巴细胞比值（NLR）、血清白蛋白（ALB）、尿酸（UA）、高敏C反应蛋白（hsCRP）和糖尿病（DM）。通过多因素逻辑回归分析建立预测模型并绘制柱状图。训练队列和验证队列的ROC曲线下面积（AUC）值分别为0.891（95%置信区间[CI]：0.829 - 0.844）和0.882（95% CI：0.722 - 0.957）。Hosmer-Lemeshow检验显示拟合良好（训练队列为0.829；验证队列为0.602）。DCA曲线表明，训练队列和验证队列的阈值概率分别为4% - 64%和3% - 90%，预测临床模型有良好的净收益。
NLR、ALB、UA、hsCRP和DM是PD置管后PDAP的独立预测因素。基于上述因素构建的柱状线图模型具有良好的区分度和校准能力，有助于预测PD置管后PDAP的风险。
