Prediction of acute kidney injury during the prerace stage of a 48-hour ultramarathon

Po Ya Hsu, Kuan Yu Lin, Po Han Hsu, Wei Fong Kao, Yi Chung Hsu, Hsin Li Liu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Acute kidney injury (AKI) is commonly seen in ultrarunners, and we hypothesized that an AKI prediction model for a 48-hour ultramarathon runner could be constructed with the runner's prerace blood, urine, and body composition data. Fifteen male and three female ultrarunners were recruited from a 48-hour Ultramarathon Festival. AKI prediction models were built based on the support vector machine algorithm. The models’ performance was evaluated by the accuracy of cross-validation tests. Moreover, we used the Friedman test to determine physiological changes from prerace to post-race in blood, urine, and body composition data. The best AKI prediction model reached an accuracy of 85% with the sensitivity and specificity being 78% and 93%, respectively. The major components of the best model were potassium, triglyceride, troponin, cholesterol, low-density lipoproteins, and creatine kinase MB of the blood; blood urea nitrogen of the urine; and muscle and creatinine clearance rate of the body composition. Furthermore, the biochemical and physiological responses of ultrarunners showed consistencies with related studies in traditional marathons and ultramarathons. In conclusion, a promising AKI prediction model was proposed, and ultrarunners are suggested to maintain healthy kidneys, heart, muscle mass, and decrease fat mass to reduce the risk of acquiring AKI.

Original languageEnglish
Pages (from-to)599-606
Number of pages8
JournalTranslational Sports Medicine
Issue number6
Publication statusPublished - Nov 1 2020


  • acute kidney injury
  • extreme sports
  • injury prevention
  • machine learning
  • statistics

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine


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