Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis

Purpose To develop and externally validate a multivariate prediction model for the prediction of acute kidney injury (AKI) in COVID-19, based on baseline renal perfusion from contrast-enhanced CT together with clinical and laboratory parameters. Methods In this retrospective IRB-approved study, we identified COVID-19 patients who had a standard-of-care contrast-enhanced abdominal CT scan within 5 days of their COVID-19 diagnosis at our institution (training set; n = 45, mean age 65 years, M/F 23/22) and at a second institution (validation set; n = 41, mean age 61 years, M/F 22/19). The CT renal perfusion parameter, cortex-to-aorta enhancement index (CAEI), was measured in both sets. A multivariate logistic regression model for predicting AKI was constructed from the training set with stepwise feature selection with CAEI together with demographical and baseline laboratory/clinical data used as input variables. Model performance in the training and validation set was evaluated with ROC analysis. Results AKI developed in 16 patients (35.6%) of the training set and in 6 patients (14.6%) of the validation set. Baseline CAEI was significantly lower in the patients that ultimately developed AKI ( P = 0.003). Logistic regression identified a model combining baseline CAEI, blood urea nitrogen, and gender as most significant predictor of AKI. This model showed excellent diagnostic performance for prediction of AKI in the training set (AUC = 0.89, P