2017 ISAKOS Biennial Congress ePoster #1082


Acute ACL Injuries Can Be Diagnosed Online with 84% Accuracy

Nick Mohtadi, MD, MSc, FRCSC, Dip. Sport Med; Clinical Professor, Calgary, AB CANADA
Swathi Damaraju, PhD, Calgary, Alberta CANADA
Victor Lun, MD, Calgary, Alberta CANADA
Hugh Tyreman, BSc. MSc., Calgary, Alberta CANADA

University of Calgary Sport Medicine Centre, Calgary, Alberta, CANADA

FDA Status Not Applicable


The great majority of ACL injuries (84%)can be diagnosed online without performing a physical exam or MRI. The best predictors are immediate swelling, instability and inability to straighten the knee.



The purposes of this study were to: 1. Identify the incidence of ACL injury from the Acute Knee Injury Clinic (AKIC); 2. To perform logistic regression analysis on the on-line diagnostic questionnaire to determine the best predictors of an ACL injury


All patients who were screened to be eligible and who were seen in the Acute Knee Injury Clinic (AKIC) from 2010-2015 were included. Each patient prospectively and independently filled in, an online screening and diagnostic questionnaire. Every patient’s information was reviewed by one, of three Non-Physician Experts (NPE) prior to being seen in the AKIC. NPEs are Athletic Therapists who have been explicitly trained in the evaluation of patients with acute knee injuries. Following the information review, the NPE determines a primary and secondary diagnosis and this is documented and dated in the electronic medical record. The patient is seen in the AKIC by an NPE and a Sport Medicine Physician where the online history is confirmed, a standardized physical examination is performed, x-rays reviewed and a diagnosis is established. The diagnosis of an acute ACL tear is confirmed clinically by a positive Lachman and pivot shift test, and/or by MRI or at the time of subsequent surgery.
A multiple logistic regression analysis was used to determine which of the diagnostic questions were predictive of an ACL tear. Separate analyses were performed for males and females and those patients above and below the age of 30.


15,368 new patients with an acute knee injury were identified. There were 3,202 confirmed ACL tears with an incidence of 21%. The great majority of ACL injuries (84.3%) were correctly diagnosed online by the NPEs using only the diagnostic questionnaire, prior to performing a physical exam or any investigations. Statistically significant independent predictors of an ACL tear were a history of immediate swelling, a chief complaint of instability and inability to straighten the knee.
The odds of having a torn ACL under the age of 30 were 21.21 times (95% CI 21.04, 21.38) if the patient documented immediate swelling, stiffness, and absence of a popping sound, had initial pain on the outside of the knee (i.e. lateral), current pain behind the knee and a mechanism of injury (MOI) that involved jumping and landing. Males had an odds ratio of 9.00 (95%CI 8.56, 9.44) if they documented that they had a planting/pivoting MOI and current pain behind the knee. In females the odds were 5.66 (95% CI 5.34, 5.98) with a MOI of jumping/landing and current pain behind the knee.


The online patient-documented questionnaire is highly predictive of an acute ACL tear at 84.3%. Independent predictors for all patients were instability, inability to initially straighten the leg and immediate swelling. Age and sex-specific predictors were also identified.