Development and validation of a nomogram for the early prediction of acute kidney injury in hospitalized COVID-19 patients

作者：Congjie Wang, Huiyuan Sun, Xinna Li, Daoxu Wu, Xiaoqing Chen, Shenchun Zou, Tingshu Jiang, Changjun Lv · 发表于：Frontiers in Public Health · 年份：2022 · DOI：10.3389/fpubh.2022.1047073 · 被引用次数：6 · 研究领域：COVID-19 Clinical Research Studies、Acute Kidney Injury Research、COVID-19 and healthcare impacts

Introduction Acute kidney injury (AKI) is a prevalent complication of coronavirus disease 2019 (COVID-19) and is closely linked with a poorer prognosis. The aim of this study was to develop and validate an easy-to-use and accurate early prediction model for AKI in hospitalized COVID-19 patients. Methods Data from 480 COVID-19-positive patients (336 in the training set and 144 in the validation set) were obtained from the public database of the Cancer Imaging Archive (TCIA). The least absolute shrinkage and selection operator (LASSO) regression method and multivariate logistic regression were used to screen potential predictive factors to construct the prediction nomogram. Receiver operating curves (ROC), calibration curves, as well as decision curve analysis (DCA) were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined. Results A predictive nomogram for AKI was developed based on arterial oxygen saturation, procalcitonin, C-reactive protein, glomerular filtration rate, and the history of coronary artery disease. In the training set, the nomogram produced an AUC of 0.831 (95% confidence interval [CI]: 0.774–0.889) with a sensitivity of 85.2% and a specificity of 69.9%. In the validation set, the nomogram produced an AUC of 0.810 (95% CI: 0.737–0.871) with a sensitivity of 77.4% and a specificity of 78.8%. The calibration curve shows that the nomogram exhibited excellent calibration and fit in both the training and valida...