Acute Kidney Injury in Hospitalized Patients with COVID-19

ABSTRACT Importance Preliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described.Objective To provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients.Design Observational, retrospective study.Setting Admitted to hospital between February 27 and April 15, 2020.Participants Patients aged ≥18 years with laboratory confirmed COVID-19Exposures AKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline).Main Outcomes and Measures Frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation.Results A total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test.Conclusions and Relevance AKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.Key Points Question What is incidence and outcomes of acute kidney injury (AKI) in patients hospitalized with COVID-19?Findings In this observational study of 3,235 hospitalized patients with COVID-19 in New York City, AKI occurred in 46% of patients and 20% of those patients required dialysis. AKI was associated with increased mortality. 44% of patients discharged alive had residual acute kidney disease. A machine learned predictive model using baseline features for dialysis requirement had an AUC Of 0.79.Meaning AKI was common in patients with COVID-19, associated with increased mortality, and nearly half of patients had acute kidney disease on discharge..

Medienart:

Preprint

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

bioRxiv.org - (2021) vom: 03. Feb. Zur Gesamtaufnahme - year:2021

Sprache:

Englisch

Beteiligte Personen:

Chan, Lili [VerfasserIn]
Chaudhary, Kumardeep [VerfasserIn]
Saha, Aparna [VerfasserIn]
Chauhan, Kinsuk [VerfasserIn]
Vaid, Akhil [VerfasserIn]
Baweja, Mukta [VerfasserIn]
Campbell, Kirk [VerfasserIn]
Chun, Nicholas [VerfasserIn]
Chung, Miriam [VerfasserIn]
Deshpande, Priya [VerfasserIn]
Farouk, Samira S. [VerfasserIn]
Kaufman, Lewis [VerfasserIn]
Kim, Tonia [VerfasserIn]
Koncicki, Holly [VerfasserIn]
Lapsia, Vijay [VerfasserIn]
Leisman, Staci [VerfasserIn]
Lu, Emily [VerfasserIn]
Meliambro, Kristin [VerfasserIn]
Menon, Madhav C. [VerfasserIn]
Rein, Joshua L. [VerfasserIn]
Sharma, Shuchita [VerfasserIn]
Tokita, Joji [VerfasserIn]
Uribarri, Jaime [VerfasserIn]
Vassalotti, Joseph A. [VerfasserIn]
Winston, Jonathan [VerfasserIn]
Mathews, Kusum S. [VerfasserIn]
Zhao, Shan [VerfasserIn]
Paranjpe, Ishan [VerfasserIn]
Somani, Sulaiman [VerfasserIn]
Richter, Felix [VerfasserIn]
Do, Ron [VerfasserIn]
Miotto, Riccardo [VerfasserIn]
Lala, Anuradha [VerfasserIn]
Kia, Arash [VerfasserIn]
Timsina, Prem [VerfasserIn]
Li, Li [VerfasserIn]
Danieletto, Matteo [VerfasserIn]
Golden, Eddye [VerfasserIn]
Glowe, Patricia [VerfasserIn]
Zweig, Micol [VerfasserIn]
Singh, Manbir [VerfasserIn]
Freeman, Robert [VerfasserIn]
Chen, Rong [VerfasserIn]
Nestler, Eric [VerfasserIn]
Narula, Jagat [VerfasserIn]
Just, Allan C. [VerfasserIn]
Horowitz, Carol [VerfasserIn]
Aberg, Judith [VerfasserIn]
Loos, Ruth J.F. [VerfasserIn]
Cho, Judy [VerfasserIn]
Fayad, Zahi [VerfasserIn]
Cordon-Cardo, Carlos [VerfasserIn]
Schadt, Eric [VerfasserIn]
Levin, Matthew A. [VerfasserIn]
Reich, David L. [VerfasserIn]
Fuster, Valentin [VerfasserIn]
Murphy, Barbara [VerfasserIn]
He, John Cijiang [VerfasserIn]
Charney, Alexander W. [VerfasserIn]
Böttinger, Erwin P. [VerfasserIn]
Glicksberg, Benjamin S. [VerfasserIn]
Coca, Steven G. [VerfasserIn]
Nadkarni, Girish N. [VerfasserIn]

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doi:

10.1101/2020.05.04.20090944

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XBI017769248