Pre-race screening and stratification predicts adverse events-A 4-year study in 29585 ultra-marathon entrants, SAFER X

Nicola Sewry; Martin Schwellnus; Mats Borjesson; Sonja Swanevelder; Esme Jordaan

doi:10.1111/sms.13659

Pre-race screening and stratification predicts adverse events-A 4-year study in 29585 ultra-marathon entrants, SAFER X

Scand J Med Sci Sports. 2020 Jul;30(7):1205-1211. doi: 10.1111/sms.13659. Epub 2020 Apr 3.

Authors

Nicola Sewry^{1

2}, Martin Schwellnus^{1

2

3}, Mats Borjesson^{4

5

6}, Sonja Swanevelder⁷, Esme Jordaan^{7

8}

Affiliations

¹ Sport, Exercise Medicine and Lifestyle Institute (SEMLI), Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.
² International Olympic Committee (IOC) Research Centre, Cape Town, South Africa.
³ Emeritus Professor of Sport and Exercise Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
⁴ Institute of Neuroscience and Physiology, Sahlgrenska Academy, Göteborg University, Gothenburg, Sweden.
⁵ Center for Health and Performance, Göteborg University, Gothenburg, Sweden.
⁶ Sahlgrenska University Hospital/Östra, Göteborg, Sweden.
⁷ Biostatistics Unit, South African Medical Research Council, Cape Town, South Africa.
⁸ Statistics and Population Studies Department, University of the Western Cape, Cape Town, South Africa.

PMID: 32187395
DOI: 10.1111/sms.13659

Abstract

Background: Pre-race screening and risk stratification in recreational endurance runners may predict adverse events (AEs) during a race.

Aim: To determine if pre-race screening and risk stratification predict AEs during a race.

Methods: A total of 29 585 participants (Male 71.1%, average age = 42.1 years; Female 28.9%, average age = 40.2 years) at the Two Oceans ultra-marathon races (56 km) completed a pre-race medical screening questionnaire and were risk stratified into four pre-specified groups [very high risk (VHR; existing cardiovascular disease-CVD:3.2%), high risk (HR; risk factors for CVD:10.5%), intermediate risk (IR; existing other chronic disease, medication use or injury:53.3%), and low risk (LR:33.0%)]. Race starters, finishers, and medical encounters (ME) were recorded. Did-not-start (DNS) rate (per 1000 entrants that did-not-start), did-not-finish (DNF) rate (per 1000 starters that did-not-finish), AE rate [per 1000 starters that either DNF or had an ME], and ME rate (per 1000 starters with an ME) were compared across risk categories.

Results: Adverse events were significantly higher (per 1000 starters; 95%CI) in the VHR (68.9; 52.4-89.9:P = .0407) compared with the LR (51.3; 46.5-56.7). The DNS rate was significantly different between the IR (190.3; 184.0-196.9) and LR (207.4; 199.2-216.0: P = .0011). DNF rates were not different in the VHR (56.4; 41.9-75.9) compared to LR (44.2; 39.7-49.1: P = .1295), and ME rate was also not different between risk categories, however, VHR (12.9; 7.0-23.9) was approaching significance compared to LR (6.9; 5.2-9.1: P = .0662).

Conclusion: Pre-race medical screening and risk stratification may identify athletes at higher risk of AEs. Further studies should be performed in larger cohorts to clarify the role of pre-race medical screening in reducing AEs in endurance runners.

Keywords: SAFER study; epidemiology; medical screening; pre-race screening; risk stratification; running.

MeSH terms

Adult
Athletes / classification*
Cardiovascular Diseases / diagnosis*
Cross-Sectional Studies
Female
Forecasting
Humans
Male
Mass Screening*
Prospective Studies
Risk Factors
Running*
Surveys and Questionnaires

Grants and funding

IOC Research Center (South Africa) (partial funding), Medical Research Council (partial funding, statistical analysis)