Int J Sports Med 2018; 39(01): 58-66
DOI: 10.1055/s-0043-119225
Orthopedics & Biomechanics
© Georg Thieme Verlag KG Stuttgart · New York

Factors Predicting Lower Leg Chronic Exertional Compartment Syndrome in a Large Population

Johan A. de Bruijn1, *, Aniek P. M. van Zantvoort1, *, David van Klaveren2, Michiel B. Winkes1, Marike van der Cruijsen-Raaijmakers3, Adwin R. Hoogeveen3, Joep A. W. Teijink4, 5, Marc R. Scheltinga1
  • 1Máxima Medical Center, Surgery, Veldhoven, the Netherlands
  • 2Erasmus MC, Public health, Rotterdam, the Netherlands
  • 3Máxima Medical Center, Sports Medicine, Veldhoven, the Netherlands
  • 4Catharina Hospital, Surgery, Eindhoven, the Netherlands
  • 5Maastricht University, CAPHRI research school, department of epidemiology, Maastricht, the Netherlands
Further Information

Publication History

Accepted after revision 24 August 2017

Publication Date:
10 November 2017 (eFirst)


Knowledge about lower leg chronic exertional compartment syndrome (CECS) is largely obtained from highly selected populations. Patient characteristics may therefore not be appropriate for the general population. Our purpose was to describe a heterogeneous population of individuals suspected of lower leg CECS and to identify predictors of CECS. Charts of individuals who were analyzed for exercise-induced lower leg pain in a referral center between 2001 and 2013 were retrospectively studied. Patients were included if history and physical examination were suggestive of CECS and if they had undergone a dynamic intracompartmental pressure measurement. Six hundred ninety-eight of 1411 individuals were diagnosed with CECS in one or more of three lower leg muscle compartments (anterior tibial, deep flexor, lateral). Prevalence of CECS peaked around the age of 20–25 years and decreased thereafter, although a plateau around 50 years was found. Age, gender, bilateral symptoms, previous lower leg pathology, sports (running and skating) and tender muscle compartments were identified as independent predictors of lower leg CECS. The proposed predictive model has moderate discriminative ability (AUC 0.66) and good calibration over the complete range of predicted probabilities. The predictive model, displayed as a nomogram, may aid in selecting individuals requiring an invasive dynamic intracompartmental muscle pressure measurement.

* Both authors contributed equally