ArticleKidney medicine2022
Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19.
Article in Kidney medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Mortality and morbidity associated with new onset acute kidney injury in critically ill COVID-19 infection patients.International journal of emergency medicine · 2024Article
- Article
- Validation of a predictive model for hospital-acquired acute kidney injury with emergence of SARS-CoV-2 variants.Journal of investigative medicine : the official publication of the American Federation for Clinical Research · 2023Article
- Development of a Risk Score for AKI onset in COVID-19 Patients: COV-AKI Score.BMC nephrology · 2023Observational
- The Texas Health Resources Clinical Scholars Program: Learning healthcare system workforce development through embedded translational research.Learning health systems · 2022Article
- Dynamic and Individualized Prediction of Acute Kidney Injury via Longitudinal Peripheral Blood Cell Trajectories among COVID-19 Inpatients: A Multicenter Prospective Cohort Study.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Rationale & Objective: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant. Study Design: Longitudinal cohort study. Setting & Participants: Hospitalized patients with a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result between March 1, 2020, and August 20, 2021 at 19 hospitals in Texas. Exposures: Comorbid conditions, baseline laboratory data, inflammatory biomarkers. Outcomes: AKI defined by KDIGO (Kidney Disease: Improving Global Outcomes) creatinine criteria. Analytical Approach: Three nested models for AKI were built in a development cohort and validated in 2 out-of-time cohorts. Model discrimination and calibration measures were compared among cohorts to assess performance over time. Results: Of 10,034 patients, 5,676, 2,917, and 1,441 were in the development, validation 1, and validation 2 cohorts, respectively, of whom 776 (13.7%), 368 (12.6%), and 179 (12.4%) developed AKI, respectively ( Limitations: Potential billing and coding bias. Conclusions: We developed and externally validated a model to accurately predict AKI in patients with coronavirus disease 2019. The performance of the model withstood changes in practice patterns and virus variants.
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Registered trials
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.