ArticleScientific reports2021
Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Machine learning-based risk prediction of outcomes in patients hospitalized with COVID-19 in Australia: the AUS-COVID Score.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Explainable machine learning-based mortality risk stratification for older adults with COVID-19: pinpointing core immunological biomarkers and revealing dose-threshold effects.Frontiers in immunology · 2026Article
- Calibration and prediction of results after failed injection in SPECT renal dynamic imaging.American journal of nuclear medicine and molecular imaging · 2026Article
- Acute kidney injury and in-hospital mortality in COVID-19: a causal analysis using directed acyclic graphs.Journal of nephrology · 2025Article
- Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice.BMC nephrology · 2024Review
- Using artificial intelligence to predict mortality in AKI patients: a systematic review/meta-analysis.Clinical kidney journal · 2024Article
- Development and validation of a simple machine learning tool to predict mortality in leptospirosis.Scientific reports · 2023Article
- Development of a Risk Score for AKI onset in COVID-19 Patients: COV-AKI Score.BMC nephrology · 2023Observational
- Artificial Intelligence in Acute Kidney Injury Prediction.Advances in chronic kidney disease · 2022Review
- Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation.Diagnostics (Basel, Switzerland) · 2022Article
- Predictors of Acute Kidney Disease Severity in Hospitalized Patients with Acute Kidney Injury.Biomedicines · 2022Article
- Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.Computational and structural biotechnology journal · 2022Article
- Artificial Intelligence in Nephrology: Pioneering Precision with Multimodal Intelligence.Indian journal of nephrologyReview
- Prediction of acute kidney injury in intensive care unit patients based on interpretable machine learning.Digital healthArticle
- Viral Glomerulopathy.Glomerular diseasesReview
Corrections and comments
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is frequently associated with COVID-19 and it is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting in-hospital mortality in COVID-19 patients with AKI (AKI-COV score). This was a cross-sectional multicentre prospective cohort study in the Latin America AKI COVID-19 Registry. A total of 870 COVID-19 patients with AKI defined according to the KDIGO were included between 1 May 2020 and 31 December 2020. We evaluated four categories of predictor variables that were available at the time of the diagnosis of AKI: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) characteristics and causes of AKI. We used a machine learning approach to fit models in the training set using tenfold cross-validation and validated the accuracy using the area under the receiver operating characteristic curve (AUC-ROC). The coefficients of the best model (Elastic Net) were used to build the predictive AKI-COV score. The AKI-COV score had an AUC-ROC of 0.823 (95% CI 0.761-0.885) in the validation cohort. The use of the AKI-COV score may assist healthcare workers in identifying hospitalized COVID-19 patients with AKI that may require more intensive monitoring and can be used for resource allocation.
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