Predicting the Need for Surgical Intervention Prior to First Encounter for Individuals with Knee Complaints: A Novel Approach. Issue 7 (29th July 2019) - Vega, Jose F.; Student, Medical; Bena, James; Strnad, Greg; Spindler, Kurt P.
Objectives: Orthopedic 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 orthopedic surgeon without a referral, leaving the surgeon to manage an increasingly large number of patients, many of which will never require surgery. We set out 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 a surgical intervention. Methods: 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 or not surgery was recommended. Logistic regression models were used to predict the risk of surgery based on triage question responses, basic demographics (age, gender), 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 using a concordance index (C-index). Results: The model developed provided a high level of discrimination between surgical and nonsurgical patients (C-index = 0.69). Recent injury with inability to walk, and no recent injury with no pain were both associated with increased probability of receiving a recommendation of surgical intervention compared to patients that reported pain without recent injury (OR=3.51, p<0.001; OR=2.78, p=0.008, respectively). A unilateral complaint was associated with needing surgical intervention (OR=4.52, p<0.001). Age has a significant, non-linear relationship with odds of needing of surgery, with middle age patients (20-50) having the greatest odds. Conclusion: Our model was able to accurately predict the probability of receiving a recommendation for surgical intervention as high as 65% and as low as 5%. Our model can quickly and easily triage patients at the time of appointment request to ensure that those with the highest likelihood of receiving a recommendation for surgical intervention are seen by surgical providers while those that are unlikely to receive such a recommendation can be seen by nonsurgical providers. To our knowledge, this represents the first attempt at developing an evidence-based scheduling algorithm.