Radiographic evaluation of knee osteoarthritis in predicting outcomes after arthroscopic partial meniscectomy.

BACKGROUND: Knee osteoarthritis (OA) severity is a predictor of outcomes after arthroscopic partial meniscectomy (APM). Magnetic resonance imaging (MRI) grading of OA is predictive of postoperative outcomes; this prospective study assessed whether radiographic grading is also predictive of outcomes. METHODS: Patients who underwent APM between February 2015 and January 2016, underwent radiography and MRI ≤6 months before surgery, and had outcomes from the surgery date and one year later were included. Surgical failure was defined as <10-point improvement in the Knee Osteoarthritis Outcome Score pain subscore. Radiographs were evaluated using Kellgren-Lawrence (KL) grading and continuous and ordinal minimum joint space width (mJSW) measurements; cartilage loss on MRI was evaluated using a modified Outerbridge system. Predictive abilities were estimated using area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). RESULTS: The study cohort included 66 knees from 64 patients (32 women; mean age, 57.1 years; range, 45-77). Radiographic grading was not predictive of outcomes (KL, AUC = 0.541 [95% CI: 0.358, 0.724]; continuous mJSW, AUC = 0.482 [95% CI: 0.305, 0.659]; ordinal mJSW, AUC = 0.534 [95% CI: 0.433, 0.634]). Comparison of radiographs showing no joint space narrowing (KL grade 0-2) with corresponding MR images demonstrated that 48% of radiographs missed a clinically significant lesion (modified Outerbridge grade ≥ 3). MRI grading was predictive of outcomes (AUC = 0.720 [95% CI: 0.581, 0.859]). CONCLUSIONS: Radiographic grading of OA is not predictive of outcomes after APM; radiographs may miss clinically significant lesions. For outcome prediction, MRI should be used.

Predicting the need for surgical intervention prior to first encounter for individuals with shoulder complaints: a unique approach.

BACKGROUND: Increasing demand for musculoskeletal care necessitates efficient scheduling and matching of patients with the appropriate provider. However, up to 47% to 60% of orthopedic visits are made without formal triage. The purpose of this study was to develop a method to identify, prior to the initial office visit, the probability that a patient with shoulder symptoms will need surgery so that he or she can be appropriately matched with an operative or nonoperative provider. We hypothesized that patients who had an injury, previously saw an orthopedic provider, or previously underwent magnetic resonance imaging on the affected shoulder would be more likely to undergo surgery. METHODS: Drawing from expert opinion on potential risk factors (which could be identified prior to the initial office visit) for requiring operative intervention for a chief complaint of shoulder symptoms, we developed a branching-logic questionnaire that required a maximum of 7 responses from the patient during the scheduling process. We administered the questionnaire to patients calling with a chief complaint of shoulder symptoms at the time of initial appointment scheduling in a sports health network. A chart review was later completed to determine the ultimate treatment (operative vs. nonoperative) of each patient's complaint. A multivariate predictive model was then developed to determine the characteristics of patients with a higher surgical risk. RESULTS: We successfully developed a model capable of determining surgical risk from 7% to 90% based on patient sex, previous magnetic resonance imaging status, and injury status. CONCLUSIONS: Our predictive model can aid in patient clinical scheduling and ensure optimal matching of a patient with the best provider for the patient's care. Decreased wait times and appropriate matching may lead to increased patient satisfaction, superior outcomes, and more efficient use of health care resources.