lancet-header

Preprints with The Lancet is part of SSRN´s First Look, a place where journals identify content of interest prior to publication. Authors have opted in at submission to The Lancet family of journals to post their preprints on Preprints with The Lancet. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early stage research papers that have not been peer-reviewed. The findings should not be used for clinical or public health decision making and should not be presented to a lay audience without highlighting that they are preliminary and have not been peer-reviewed. For more information on this collaboration, see the comments published in The Lancet about the trial period, and our decision to make this a permanent offering, or visit The Lancet´s FAQ page, and for any feedback please contact preprints@lancet.com.

Two Phenotypes of Acute Kidney Injury Among Patients with COVID-19: A Multicenter Study from Wuhan, China

38 Pages Posted: 24 Sep 2020

See all articles by Suyuan Peng

Suyuan Peng

National Institute of Health Data Science, Peking University

Huai-yu Wang

National Institute of Health Data Science, Peking University

Xiaoyu Sun

Peking University - National Institute of Health Data Science

Pengfei Li

Advanced Institute of Information Technology, Peking University

Zhanghui Ye

Advanced Institute of Information Technology, Peking University

Qing Li

Advanced Institute of Information Technology, Peking University

Jinwei Wang

Peking University - Renal Division

Xuanyu Shi

National Institute of Health Data Science, Peking University

Rui Zeng

Huazhong University of Science and Technology (Formerly Tongi Medical University) - State Key Laboratory of Material Processing and Die & Mould Technology

Ying Yao

Department of Clinical Nutrition, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Shuwang Ge

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Nephrology

Fan He

Department of Nephrology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Junhua Li

Department of Nephrology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Liu Liu

4Department of Nephrology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Xianjun Ke

Taikang Tongji (Wuhan) Hospital

Zhibin Zhou

Taikang Tongji (Wuhan) Hospital

Erdan Dong

Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital

Haibo Wang

Sun Yat-Sen University (SYSU), First Affiliated Hospital, Clinical Trial Unit

Gang Xu

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Nephrology

Luxia Zhang

Peking University - National Institute of Health Data Science

Ming-Hui Zhao

Peking University - Renal Division; Peking University - Institute of Nephrology

More...

Abstract

Background: Acute kidney injury (AKI) is an important complication of Coronavirus disease 2019 (COVID-19), which could be caused by both systematic responses for multi-organ dysfunction and direct virus infection. While advanced evidence is needed regarding its clinical features and mechanisms. We aimed to describe two phenotypes of AKI, as well as their risk factors and the association with mortality.

Methods: Consecutive hospitalized patients with COVID-19 of three hospitals in Wuhan, China, from January 2020 to March 2020 were included. Patients with AKI were classified as AKI-early and AKI-late according to the sequence of organ dysfunction (kidney as the first dysfunctional organ or not). Demographic and clinical features were compared between two AKI groups. Their risk factors and the associations with in-hospital mortality were analyzed.  

Findings: A total of 4020 cases with laboratory-confirmed COVID-19 were included, and 285(7·09%) of them were identified as AKI. Compared with patients with AKI-early, patients with AKI-late had significantly higher levels of systemic inflammatory markers. Both AKIs were associated with increased risks of in-hospital mortality, with similar fully adjusted HRs of 2·46 (95% confidence interval [CI] 1·35 - 4·49) for AKI-early and 3·09(95% CI 2·17 - 4·40) for AKI-late. Only hypertension was independently associated with the risk of AKI-early, while age, history of chronic kidney disease, and the levels of inflammatory biomarkers were associated with the risk of AKI-late.

Interpretation: AKI among patients with COVID-19 has two clinical phenotypes, which could be due to different mechanisms. Considering the increased risk for mortality for both phenotypes, monitoring for AKI should be emphasized during the COVID-19. 

Funding: Special Research Fund of PKU for Prevention and Control of COVID-19; the Fundamental Research Funds for the Central Universities; the National Natural Science Foundation of China; Major Research Plan of the National Natural Science Foundation of China

Declaration of Interests: The authors declare no competing interests.

Ethics Approval Statement: This study was approved by the National Health Commission of China and the institutional review board (IRB) of Peking University Health Science Centre(IRB00001052-20032).

Keywords: Coronavirus disease 2019; COVID-19; Severe acute respiratory syndrome coronavirus 2; acute kidney injury

Suggested Citation

Peng, Suyuan and Wang, Huai-yu and Sun, Xiaoyu and Li, Pengfei and Ye, Zhanghui and Li, Qing and Wang, Jinwei and Shi, Xuanyu and Zeng, Rui and Yao, Ying and Ge, Shuwang and He, Fan and Li, Junhua and Liu, Liu and Ke, Xianjun and Zhou, Zhibin and Dong, Erdan and Wang, Haibo and Xu, Gang and Zhang, Luxia and Zhao, Ming-Hui, Two Phenotypes of Acute Kidney Injury Among Patients with COVID-19: A Multicenter Study from Wuhan, China (6/23/2020). Available at SSRN: https://ssrn.com/abstract=3634852 or http://dx.doi.org/10.2139/ssrn.3634852

Suyuan Peng

National Institute of Health Data Science, Peking University ( email )

Huai-yu Wang

National Institute of Health Data Science, Peking University ( email )

Xiaoyu Sun

Peking University - National Institute of Health Data Science

Beijing
China

Pengfei Li

Advanced Institute of Information Technology, Peking University

Zhanghui Ye

Advanced Institute of Information Technology, Peking University

Qing Li

Advanced Institute of Information Technology, Peking University

Jinwei Wang

Peking University - Renal Division

Beijing
China

Xuanyu Shi

National Institute of Health Data Science, Peking University

Rui Zeng

Huazhong University of Science and Technology (Formerly Tongi Medical University) - State Key Laboratory of Material Processing and Die & Mould Technology

Wuhan, Hubei, 430074
China

Ying Yao

Department of Clinical Nutrition, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Shuwang Ge

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Nephrology ( email )

China

Fan He

Department of Nephrology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Junhua Li

Department of Nephrology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Liu Liu

4Department of Nephrology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Xianjun Ke

Taikang Tongji (Wuhan) Hospital

Zhibin Zhou

Taikang Tongji (Wuhan) Hospital

Erdan Dong

Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital

Haibo Wang

Sun Yat-Sen University (SYSU), First Affiliated Hospital, Clinical Trial Unit

China

Gang Xu

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Nephrology ( email )

China

Luxia Zhang (Contact Author)

Peking University - National Institute of Health Data Science ( email )

China

Ming-Hui Zhao

Peking University - Renal Division ( email )

Beijing
China

Peking University - Institute of Nephrology ( email )

Beijing
China

Click here to go to TheLancet.com

Paper statistics

Abstract Views
987
Downloads
36
PlumX Metrics