ArticleJournal of human kinetics2022
An Acute Kidney Injury Prediction Model for 24-hour Ultramarathon Runners.
Article in Journal of human kinetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed, 5 citations in OpenAlex.
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
- 24-Hour Ultra-Marathon Running: A Narrative Review of Performance Factors and Physiological Impacts.Sports medicine - open · 2026Review
- Risk of Rhabdomyolysis and Kidney Injury after Intensive Exercise. Potential of Novel Biomarkers of Kidney Injury: A Narrative Review.Journal of human kinetics · 2026Review
- Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis.British journal of sports medicine · 2025Article
- The fastest 24-hour ultramarathoners are from Eastern Europe.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is frequently seen in ultrarunners, and in this study, an AKI prediction model for 24-hour ultrarunners was built based on the runner's prerace blood, urine, and body composition data. Twenty-two ultrarunners participated in the study. The risk of acquiring AKI was evaluated by a support vector machine (SVM) model, which is a statistical model commonly used for classification tasks. The inputs of the SVM model were the data collected 1 hour before the race, and the output of the SVM model was the decision of acquiring AKI. Our best AKI prediction model achieved accuracy of 96% in training and 90% in cross-validation tests. In addition, the sensitivity and specificity of the model were 90% and 100%, respectively. In accordance with the AKI prediction model components, ultra-runners are suggested to have high muscle mass and undergo regular ultra-endurance sports training to reduce the risk of acquiring AKI after participating in a 24-hour ultramarathon.
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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.