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Acute Kidney Injury in Hospitalized Patients with COVID-19
Lili Chan; Kumardeep Chaudhary; Aparna Saha; Kinsuk Chauhan; Akhil Vaid; Mukta Baweja; Kirk Campbell; Nicholas Chun; Miriam Chung; Priya Deshpande; Samira S Farouk; Lewis Kaufman; Tonia Kim; Holly Koncicki; Vijay Lapsia; Staci Leisman; Emily Lu; Kristin Meliambro; Madhav C Menon; Joshua L Rein; Shuchita Sharma; Joji Tokita; Jaime Uribarri; Joseph A Vassalotti; Jonathan Winston; Kusum S Mathews; Shan Zhao; Ishan Paranjpe; Sulaiman Somani; Felix Richter; Ron Do; Riccardo Miotto; Anuradha Lala; Arash Kia; Prem Timsina; Li Li; Matteo Danieletto; Eddye Golden; Patricia Glowe; Micol Zweig; Manbir Singh; Robert Freeman; Rong Chen; Eric Nestler; Jagat Narula; Allan C Just; Carol Horowitz; Judith Aberg; Ruth J.F. Loos; Judy Cho; Zahi Fayad; Carlos Cordon-Cardo; Eric Schadt; Matthew A Levin; David L Reich; Valentin Fuster; Barbara Murphy; John Cijiang He; Alexander W Charney; Erwin P Bottinger; Benjamin S Glicksberg; Steven G Coca; Girish N Nadkarni.
Afiliação
  • Lili Chan; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Kumardeep Chaudhary; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Aparna Saha; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Kinsuk Chauhan; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Akhil Vaid; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Mukta Baweja; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Kirk Campbell; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Nicholas Chun; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Miriam Chung; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Priya Deshpande; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Samira S Farouk; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Lewis Kaufman; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Tonia Kim; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Holly Koncicki; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Vijay Lapsia; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Staci Leisman; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Emily Lu; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Kristin Meliambro; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Madhav C Menon; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Joshua L Rein; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Shuchita Sharma; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Joji Tokita; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Jaime Uribarri; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Joseph A Vassalotti; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Jonathan Winston; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Kusum S Mathews; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Shan Zhao; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Ishan Paranjpe; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Sulaiman Somani; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Felix Richter; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Ron Do; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Riccardo Miotto; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Anuradha Lala; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Arash Kia; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Prem Timsina; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Li Li; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Matteo Danieletto; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Eddye Golden; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Patricia Glowe; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Micol Zweig; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Manbir Singh; The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
  • Robert Freeman; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Rong Chen; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Eric Nestler; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Jagat Narula; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Allan C Just; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Carol Horowitz; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Judith Aberg; judith.aberg@mssm.edu
  • Ruth J.F. Loos; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Judy Cho; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Zahi Fayad; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Carlos Cordon-Cardo; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Eric Schadt; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Matthew A Levin; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • David L Reich; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Valentin Fuster; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Barbara Murphy; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • John Cijiang He; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Alexander W Charney; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Erwin P Bottinger; Digital Health Center, Hasso Plattner Institute, University of Potsdam, Professor-Dr.-Helmert-Strasse 2-3, Potsdam, Germany
  • Benjamin S Glicksberg; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Steven G Coca; Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Girish N Nadkarni; Icahn School of Medicine at Mount Sinai, New York, NY, USA
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20090944
ABSTRACT
ImportancePreliminary 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. ObjectiveTo provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients. DesignObservational, retrospective study. SettingAdmitted to hospital between February 27 and April 15, 2020. ParticipantsPatients aged [≥]18 years with laboratory confirmed COVID-19 ExposuresAKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and MeasuresFrequency 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. ResultsA 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 RelevanceAKI 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 PointsO_ST_ABSQuestionC_ST_ABSWhat is incidence and outcomes of acute kidney injury (AKI) in patients hospitalized with COVID-19? FindingsIn 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. MeaningAKI was common in patients with COVID-19, associated with increased mortality, and nearly half of patients had acute kidney disease on discharge.
Licença
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Cohort_studies / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Cohort_studies / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
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