AUTHOR=Wang Congjie , Sun Huiyuan , Li Xinna , Wu Daoxu , Chen Xiaoqing , Zou Shenchun , Jiang Tingshu , Lv Changjun 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.1047073

DOI=10.3389/fpubh.2022.1047073

ISSN=2296-2565

ABSTRACT=Introduction: Acute kidney injury (AKI) is a common complication in patients with coronavirus disease 2019 (COVID-19) and is associated with a worse prognosis. The aim of this study was to develop and validate a simple-to-use and effective early prediction model for AKI in hospitalized COVID-19 patients.
Methods: Data from 480 COVID-19-positive patients (366 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, and decision curve analysis (DCA) were adopted to evaluate the performance 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, and glomerular filtration rate, and the history of coronary artery disease. The nomogram yielded an AUC of 0.831 (95% confidence interval (CI): 0.774-0.889) in the training set, with a sensitivity of 85.2% and a specificity of 69.9%, and yielded an AUC of 0.810 (95% CI:0.737-0.871) in the validation set, with a sensitivity of 77.4% and specificity of 78.8%. The calibration curve shows that the nomogram had good calibration and fit in both the training and validation sets. DCA also suggested that the nomogram has promising clinical effectiveness. In addition, the median length of stay (m-LS) for patients in the high-risk group for AKI (risk score ≥ 0.122) was 14.0 days (95% CI: 11.3-16.7 days), which was significantly longer than 8.0 days (95% CI: 7.1-8.9 days) for patients in the low-risk group (risk score < 0.122) (hazard ratio (HR): 1.98, 95% CI: 1.55-2.53, p<0.001). Moreover, the mortality rate was also significantly higher in the high-risk group than that in the low-risk group (20.6% versus 2.9%, odd ratio (OR):8.61, 95%CI: 3.45-21.52).
Conclusions: The newly constructed nomogram model could accurately identify potential COVID-19 patients who may experience AKI during hospitalization at the very beginning of their admission and may be useful for informing clinical prognosis.