Liu Guiling, Li Xunliang, Zhao Wenman, Shi Rui, Zhu Yuyu, Wang Zhijuan, Pan Haifeng, 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.
Heliyon. 2023 Jul 23;9(8):e18551. doi: 10.1016/j.heliyon.2023.e18551. eCollection 2023 Aug.
This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections.
In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram. The concordance index (C-Index) was utilized to assess the precision of the model's predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram.
The final study population included 217 patients with PDAP, and 37 (17.1%) patients had gram-negative bacteria due to dialysate effluent culture. After multivariate logistic regression, age, procalcitonin, and hemoglobin were predictive factors of GNB infections. The C-index and bootstrap-corrected index of the nomogram for estimating GNB infections in patients were 0.821 and 0.814, respectively. The calibration plots showed good agreement between the predictions of the nomogram and the actual observation of GNB infections. The AUC of the receiver operating characteristic curve was 0.821, 95% CI: 0.747-0.896, which indicates that the model has good predictive accuracy. In addition, the DCA curve showed that the nomogram had a high clinical value in the range of 1%-94%, which further demonstrated that the nomogram could accurately predict GNB infection in patients with PDAP.
We have created a new nomogram for predicting GNB infections in patients with PDAP. The nomogram model may improve the identification of GNB infections in patients with PDAP and contribute to timely intervention to improve patient prognosis.
本研究旨在开发一种列线图，用于预测腹膜透析相关性腹膜炎（PDAP）患者的革兰氏阴性菌（GNB）感染，以识别GNB感染的高危患者。
在本调查中，回顾性收集了2016年1月至2021年12月期间PDAP患者的住院信息。通过单因素逻辑回归、LASSO分析和随机森林（RF）算法获得的潜在生物标志物串联纳入多因素逻辑回归，以识别与GNB感染相关的混杂因素，然后将其整合到列线图中。一致性指数（C指数）用于评估模型预测的准确性。曲线下面积（AUC）和决策曲线分析（DCA）用于评估列线图的预测性能和临床实用性。
最终研究人群包括217例PDAP患者，37例（17.1%）患者因透析液流出物培养检出革兰氏阴性菌。多因素逻辑回归后，年龄、降钙素原和血红蛋白是GNB感染的预测因素。用于估计患者GNB感染的列线图的C指数和经自举校正的指数分别为0.821和0.814。校准图显示列线图预测与GNB感染实际观察结果之间具有良好的一致性。受试者工作特征曲线的AUC为0.821，95%CI：0.747-0.896，表明该模型具有良好的预测准确性。此外，DCA曲线显示列线图在1%-94%的范围内具有较高的临床价值，这进一步证明列线图可以准确预测PDAP患者的GNB感染。
我们创建了一种用于预测PDAP患者GNB感染的新列线图。该列线图模型可能会改善对PDAP患者GNB感染的识别，并有助于及时进行干预以改善患者预后。
