Evidence map›Paper›PMID 32511564›Full record

ArticlemedRxiv : the preprint server for health sciences2020

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 and 53 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

63 authors.

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

Funding

Optimizing ED to ICU triage decisions for critically ill patients in respiratory distressK23HL130648 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MATHEWS, KUSUM SARA · 2016 to 2021
$744k
6 · The paper itself

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 Exposures: 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.

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 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.

Identifiers

PMID32511564
PMCPMC7274245

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.