摘要
Background: Ultramarathon is a high endurance exercise associated with a wide range of exerciserelated problems, such as acute kidney injury (AKI). Early recognition of individuals at risk of AKI during ultramarathon event is critical for implementing preventative strategies. Objectives: To investigate the impact of speed variability to identify the exercise-related acute kidney injury anticipatively in ultramarathon event. Methods: This is a prospective, observational study using data from a 100 km ultramarathon in Taipei, Taiwan. The distance of entire ultramarathon race was divided into 10 splits. The mean and variability of speed, which was determined by the coefficient of variation (CV) in each 10 km-split (25 laps of 400 m oval track) were calculated for enrolled runners. Baseline characteristics and biochemical data were collected completely 1 week before, immediately postrace, and one day after race. The main outcome was the development of AKI, defined as Stage II or III according to the Acute Kidney Injury Network (AKIN) criteria. Multivariate analysis was performed to determine the independent association between variables and AKI development. Results: 26 ultramarathon runners were analyzed in the study. The overall incidence of AKI (in all Stages) was 84.6%(22 in 26 runners). Among these 22 runners, 18 runners were determined as Stage I, 4 runners (15.4%) were determined as Stage II, and none was in Stage III. The covariates of BMI (25.22 ± 2.02 vs. 22.55 ± 1.96, p = 0.02), uric acid (6.88 ± 1.47 vs. 5.62 ± 0.86, p = 0.024), and CV of speed in specific 10-km splits (from secondary 10 km-split (10th - 20th km-split) to 60th - 70th km-split) were significantly different between runners with or without AKI (Stage II) in univariate analysis and showed discrimination ability in ROC curve. In the following multivariate analysis, only CV of speed in 40th - 50th km-split continued to show a significant association to the development of AKI (Stage II) (p = 0.032). Conclusions: The development of exercise-related AKI was not infrequent in the ultramarathon runners. Because not all runners can routinely receive laboratory studies after race, variability of running speed (CV of speed) may offer a timely and efficient tool to identify AKI early during the competition, and used as a surrogate screening tool, at-risk runners can be identified and enrolled into prevention trials, such as adequate fluid management and avoidance of further NSAID use.
| 原文 | 英語 |
|---|---|
| 文章編號 | e0133146 |
| 期刊 | PLoS One |
| 卷 | 10 |
| 發行號 | 7 |
| DOIs | |
| 出版狀態 | 已發佈 - 七月 15 2015 |
指紋
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Medicine(all)
引用此文
Clinical impact of speed variability to identify ultramarathon runners at risk for acute kidney injury. / Hou, Sen Kuang; Chiu, Yu Hui; Tsai, Yi Fang; Tai, Ling Chen; Hou, Peter C.; How, Chorng Kuang; Yang, Chen Chang; Kao, Wei Fong.
於: PLoS One, 卷 10, 編號 7, e0133146, 15.07.2015.研究成果: 雜誌貢獻 › 文章
}
TY - JOUR
T1 - Clinical impact of speed variability to identify ultramarathon runners at risk for acute kidney injury
AU - Hou, Sen Kuang
AU - Chiu, Yu Hui
AU - Tsai, Yi Fang
AU - Tai, Ling Chen
AU - Hou, Peter C.
AU - How, Chorng Kuang
AU - Yang, Chen Chang
AU - Kao, Wei Fong
PY - 2015/7/15
Y1 - 2015/7/15
N2 - Background: Ultramarathon is a high endurance exercise associated with a wide range of exerciserelated problems, such as acute kidney injury (AKI). Early recognition of individuals at risk of AKI during ultramarathon event is critical for implementing preventative strategies. Objectives: To investigate the impact of speed variability to identify the exercise-related acute kidney injury anticipatively in ultramarathon event. Methods: This is a prospective, observational study using data from a 100 km ultramarathon in Taipei, Taiwan. The distance of entire ultramarathon race was divided into 10 splits. The mean and variability of speed, which was determined by the coefficient of variation (CV) in each 10 km-split (25 laps of 400 m oval track) were calculated for enrolled runners. Baseline characteristics and biochemical data were collected completely 1 week before, immediately postrace, and one day after race. The main outcome was the development of AKI, defined as Stage II or III according to the Acute Kidney Injury Network (AKIN) criteria. Multivariate analysis was performed to determine the independent association between variables and AKI development. Results: 26 ultramarathon runners were analyzed in the study. The overall incidence of AKI (in all Stages) was 84.6%(22 in 26 runners). Among these 22 runners, 18 runners were determined as Stage I, 4 runners (15.4%) were determined as Stage II, and none was in Stage III. The covariates of BMI (25.22 ± 2.02 vs. 22.55 ± 1.96, p = 0.02), uric acid (6.88 ± 1.47 vs. 5.62 ± 0.86, p = 0.024), and CV of speed in specific 10-km splits (from secondary 10 km-split (10th - 20th km-split) to 60th - 70th km-split) were significantly different between runners with or without AKI (Stage II) in univariate analysis and showed discrimination ability in ROC curve. In the following multivariate analysis, only CV of speed in 40th - 50th km-split continued to show a significant association to the development of AKI (Stage II) (p = 0.032). Conclusions: The development of exercise-related AKI was not infrequent in the ultramarathon runners. Because not all runners can routinely receive laboratory studies after race, variability of running speed (CV of speed) may offer a timely and efficient tool to identify AKI early during the competition, and used as a surrogate screening tool, at-risk runners can be identified and enrolled into prevention trials, such as adequate fluid management and avoidance of further NSAID use.
AB - Background: Ultramarathon is a high endurance exercise associated with a wide range of exerciserelated problems, such as acute kidney injury (AKI). Early recognition of individuals at risk of AKI during ultramarathon event is critical for implementing preventative strategies. Objectives: To investigate the impact of speed variability to identify the exercise-related acute kidney injury anticipatively in ultramarathon event. Methods: This is a prospective, observational study using data from a 100 km ultramarathon in Taipei, Taiwan. The distance of entire ultramarathon race was divided into 10 splits. The mean and variability of speed, which was determined by the coefficient of variation (CV) in each 10 km-split (25 laps of 400 m oval track) were calculated for enrolled runners. Baseline characteristics and biochemical data were collected completely 1 week before, immediately postrace, and one day after race. The main outcome was the development of AKI, defined as Stage II or III according to the Acute Kidney Injury Network (AKIN) criteria. Multivariate analysis was performed to determine the independent association between variables and AKI development. Results: 26 ultramarathon runners were analyzed in the study. The overall incidence of AKI (in all Stages) was 84.6%(22 in 26 runners). Among these 22 runners, 18 runners were determined as Stage I, 4 runners (15.4%) were determined as Stage II, and none was in Stage III. The covariates of BMI (25.22 ± 2.02 vs. 22.55 ± 1.96, p = 0.02), uric acid (6.88 ± 1.47 vs. 5.62 ± 0.86, p = 0.024), and CV of speed in specific 10-km splits (from secondary 10 km-split (10th - 20th km-split) to 60th - 70th km-split) were significantly different between runners with or without AKI (Stage II) in univariate analysis and showed discrimination ability in ROC curve. In the following multivariate analysis, only CV of speed in 40th - 50th km-split continued to show a significant association to the development of AKI (Stage II) (p = 0.032). Conclusions: The development of exercise-related AKI was not infrequent in the ultramarathon runners. Because not all runners can routinely receive laboratory studies after race, variability of running speed (CV of speed) may offer a timely and efficient tool to identify AKI early during the competition, and used as a surrogate screening tool, at-risk runners can be identified and enrolled into prevention trials, such as adequate fluid management and avoidance of further NSAID use.
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U2 - 10.1371/journal.pone.0133146
DO - 10.1371/journal.pone.0133146
M3 - Article
VL - 10
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 7
M1 - e0133146
ER -