Abnormal Indexes of Liver and Kidney Injury Markers Predict Severity in COVID-19 Patients

Infect Drug Resist. 2021 Aug 10:14:3029-3040. doi: 10.2147/IDR.S321915. eCollection 2021.

Abstract

Background: SARS-CoV-2 can damage not only the lungs but also the liver and kidney. Most critically ill patients with coronavirus disease 2019 (COVID-19) have liver and kidney dysfunction. We aim to investigate the levels of liver and kidney function indexes in mild and severe COVID-19 patients and their capability to predict the severity of the disease.

Methods: The characteristics and laboratory indexes were compared between patients with different conditions. We applied binary logistic regression to find the independent risk factors of severe patients. Receiver operating characteristic (ROC) analysis was used to predict the severity of COVID-19 using the liver and kidney function indexes.

Results: This study enrolled 266 COVID-19 patients, including 235 mild patients and 31 severe patients. Compared with mild patients, severe patients had lower albumin (ALB) and higher alanine aminotransferase (ALT), aspartate aminotransferase (AST), and urea nitrogen (BUN) (all p<0.001). Binary logistic regression analysis also identified ALB [OR=0.273 (0.079-0.947), p=0.041] and ALT [OR=2.680 (1.036-6.934), p=0.042] as independent factors of severe COVID-19 patients. Combining ALB, ALT, BUN, and LDH exhibited the area under ROC at 0.914, with a sensitivity of 86.7% and specificity of 83.0%.

Conclusion: COVID-19 patients, especially severe patients, have damage to liver and kidney function. ALT, AST, LDH, and BUN could be independent factors for predicting the severity of COVID-19. Combining the ALB, ALT, BUN, and LDH could predict the transition from mild to severe in COVID-19 patients.

Keywords: COVID-19; kidney damage; liver damage; predictor of disease severity.

Grants and funding

This study was supported by the grants of the novel coronavirus pneumonia major project of Hunan (2020SK3014), novel coronavirus pneumonia major project of Changsha Science and technology project (kq2001017) and the Planned Science and Technology Innovation Project of Hunan Province, China (2018SK50503).