Predicting the Need for Surgical Intervention Prior to First Encounter for Individuals With Knee Complaints: A Novel...

Orthopaedic complaints, particularly those relating to the knee, are some of the most common conditions that bring patients to the hospital. Many patients bypass their primary care physician to seek the care of an orthopaedic surgeon without referral, leaving the surgeon to manage an increasingly large number of patients, many of whom will never require surgery.

To develop a brief questionnaire that can be administered via phone/web at the time of appointment request to predict an individual patient's probability of requiring surgical intervention.

Case-control study; Level of evidence, 3.

All patients (N = 1307) seeking an appointment for a new knee-related complaint completed a branching-logic questionnaire. A retrospective chart review was conducted following the conclusion of each patient's episode of care to determine whether surgery was recommended. Logistic regression models were used to predict the risk of surgery based on triage question responses, basic demographics (age, sex), and laterality (unilateral vs bilateral). The ability of the models to discriminate between those who did and did not receive a surgical recommendation was measured with a concordance index.

The model provided a high level of discrimination between surgical and nonsurgical cases (concordance index, 0.69). Recent injury with inability to walk and no recent injury with no pain were both associated with an increased probability of receiving a recommendation of surgical intervention as compared with patients who reported pain without recent injury (odds ratio [OR]: 3.51 [P < .001] and 2.78 [P = .008], respectively). A unilateral complaint was associated with needing surgical intervention (OR, 4.52 [P < .001]). Age had a significant nonlinear relationship with odds of needing of surgery, with middle-aged patients (range, 20-50 years) having the greatest odds.

The current model, which utilizes demographic questions and portions of a routine history alone, was able to accurately identify individuals who are most likely (up to 65% probability) and least likely (<5% probability) to need knee surgery. This model can quickly and easily conduct triage at the time of appointment request to ensure that patients with the highest likelihood of receiving a recommendation for surgical intervention are seen by surgical providers, while those who are unlikely to receive such a recommendation can be seen by nonsurgical providers.