Information about project titled 'Predictive value of patient history, clinical examinations and MRI findings on the time to RTS after acute hamstring injuries'
Predictive value of patient history, clinical examinations and MRI findings on the time to RTS after acute hamstring injuries
|Details about the project - category||Details about the project - value|
|Project manager:||Arnlaug Wangensteen|
|Supervisor(s):||Johannes Tol, Roald Bahr|
|Coworker(s):||Rodney Whitely, Erik Witvrouw, Bruce Hamilton, Mohammed Farooq, Sirine Boukarroum, Emad Almusa, Robbart van Linschoten|
Acute hamstring muscle injuries represent the most prevalent non-contact muscle injury reported in sports with high speed running and power involved or in dancers due to the extreme requirements on range of motion. Despite the high prevalence and a rapidly expanding body of literature investigating acute hamstring muscle injuries, the incidence and re-injury rates have not improved over the last three decades, causing a significant loss of time from competition. The diagnosis and prognosis of time to return to sports (RTS) after acute hamstring injuries are mainly based on clinical findings from patient history and clinical examinations, supplemented by radiological examinations, such as magnetic resonance imaging (MRI) or ultrasound. However, the most accurate diagnostic and prognostic algorithm of clinical evaluation and radiological imaging is still unknown.
To prospectively evaluate the predictive value of patient history, clinical examinations and MRI findings on the time to RTS after acute hamstring muscle injuries in male athletes.
Male athletes, 18-50 years of age, sustaining a posterior thigh injury (potential hamstring strain) are recruited at the walk-in clinic at Aspetar where they undergo a standardized assessment procedure including patient history, clinical examinations and MRI.
In study I, athletes with complete clinical- and MRI examinations within 5 days after injury will be included and time to RTS will be registered prospectively. Using multiple regression analysis, possible predictors for time to RTS will be examined.