My name is Natalie Lowenstein, and as he just said, I am presenting our paper, Preoperative Predictors of Arthroscopic Partial Meniscectomy Outcomes, the APM Index Score. Thank you to Dr. Matzkin for your mentorship, my co-authors, and to AOSSM for the opportunity to present our research. Meniscal tears are extremely common, and in the U.S., over 700,000 arthroscopic partial meniscectomies are performed annually. In general, there is inadequate consensus on the use of APM for treatment of meniscal tears. For traumatic tears in young patients without OA, the evidence and treatment recommendations for surgical interventions are clear. However, the AAOS guidelines offer inconclusive recommendations about APM in patients with OA of the knee. So the question remains, how do we preoperatively predict who will do well following arthroscopic partial meniscectomy? Preoperative risk factors contributing to poor outcomes following APM have not yet been consolidated and codified into an index scoring system used to predict APM success. The purpose of the current study was to create an index score using available preoperative factors to predict the likelihood of favorable outcomes following APM. We conducted a retrospective review of prospectively collected data from patients who underwent APM at a single academic medical center over eight years. Patients were excluded if they underwent revision procedures, were under 18 years old, had concomitant ligamentous procedures, incomplete PROMs, or did not have a preoperative x-ray and EPIC. This led to our cohort of 468 patients. All patients had strict x-ray grading performed using the Kellgren-Lawrence grading scale. There were four independent reviewers and two reviewers graded each x-ray preoperatively and independently. If there were any discrepancies in overall KL grade, the PI reevaluated these images to determine a final KL grade used for the analysis. Among our 468 subjects, the mean age was 49, 42% of subjects were male, 89% were white, and 79% were either normal or overweight according to BMI classifications. There were no patients with KL grade 4 x-rays as they were not candidates for knee arthroscopy. We carefully collected patient characteristics that we could evaluate as potential predictors included patient age, sex, BMI, symptom duration, and KL grade, as well as pretreatment PROMs. The primary outcome was clinical improvement in KUS pain scale at one year post-operation. The secondary outcomes were improvement in KUS symptoms and activities of daily living subscales. The cutoffs for clinical improvement as defined by minimal clinically important difference, MCID, and substantial clinical benefit, SCB, were chosen from established values reported in prior literature, and any improvement reaching a score of 90 or above was considered to be clinically sufficient. We first conducted a univariate analysis to identify the potential risk factors for each of the three KUS subscales and the two types of improvement, MCID and SCB. We then conducted multivariable logistic regression models to evaluate independent predictors of these outcomes. To then generate a risk score, we assigned points to each variable proportional to its odds ratio rounded to the nearest integer. Here's the APM index score we developed. We focused on KUS pain for our primary model, however, the KUS symptom and ADL subscales performed similarly. Since pretreatment KUS pain may not be readily available, we also developed an abbreviated model using only symptom duration and KL score. The scoring algorithm performed very well with a higher total index score predicting higher likelihood of achieving clinical improvement with MCID as you can see at the bottom of this figure. The abbreviated scoring algorithm performed similarly to the full model. For example, using the full model, if you had a patient with a meniscal tear from over a year ago, KL grade 3, and a pretreatment KUS of 65, they would be assigned zero points for all three domains giving an index score of 0 and a 40% likelihood of achieving clinical improvement after APM. If you had a patient with a meniscal tear of less than three months, a KL grade 1, and a pretreatment KUS of 30, using the full model, they would have an 89.4% likelihood, and using the abbreviated model, a 90.5% likelihood of achieving clinical improvement after APM. In summary, this study found that preop factors, including shorter symptom duration, lower KL grade, decreased preop pain are predictors of clinical improvement after APM. The APM index score is a simple tool that can be used in real-time to inform patients with a symptomatic meniscal tear of their probability for improvement after APM. Further research should be conducted to determine how this scoring algorithm performs in a clinical setting to inform physician and patient discussions regarding the use of APM to treat symptomatic meniscal tears. Thank you.

Preoperative Predictors

Arthroscopic Partial Meniscectomy Outcomes

APM Index Score

Natalie Lowenstein

Clinical Improvement