Defining Clinically Meaningful Subgroups in Patients Undergoing Arthroscopic Rotator Cuff Repair Using Unsupervised Machine Learning

Abstract

Background: Outcomes after arthroscopic rotator cuff repair (RCR) are frequently measured through clinically significant outcomes (CSOs) such as the minimal clinically important difference, the substantial clinical benefit, and the Patient Acceptable Symptom State. Global achievement of CSOs is challenging to predict.

Purpose: To determine if unsupervised machine learning can identify distinct patient subgroups based on CSO achievement after elective arthroscopic RCR.

Study design: Case-control study; Level of evidence, 3.

Methods: A prospectively collected database was analyzed to identify patients who underwent elective arthroscopic RCR from 2015 to 2017. Tear dimensions were measured on magnetic resonance imaging utilizing a validated technique. CSO achievements on the American Shoulder and Elbow Surgeons, the Single Assessment Numeric Evaluation, and the Constant-Murley subjective score at 2-year follow-up were calculated. An unsupervised random forest algorithm was utilized to develop and internally validate patient subgroups with significantly different rates of CSO achievement. Patient subgroup membership, along with a total of 30 demographic and clinical variables, as well as preoperative patient-reported outcomes, were incorporated into a stepwise multivariable logistic regression to identify factors predictive of optimal CSO achievement.

Results: A total of 346 patients (192 male; mean ± SD age, 57.2 ± 9.1 years; body mass index, 30.1 ± 5.4 kg/m²) were eligible for inclusion and followed for a mean of 3.8 years (range, 2.0-6.2 years) Of these, a total of 333 patients were partitioned by the random forest algorithm into 2 subgroups (stability, 0.16; connectivity: 180.8; Dunn: 0.16; silhouette: 0.05), with 176 patients in the optimal achievement subgroup and 157 patients in the suboptimal achievement subgroup. The 2 subgroups differed significantly (all P≤ .004) in the likelihood of achievement of all CSOs. Stepwise multivariable logistic regression identified an increase of 1 mm in tear size in the sagittal dimension beyond 1.9 cm to predict a 10% increase in the probability of suboptimal achievement. Additional risk factors for suboptimal CSO achievement included increasing number of tendons involved (odds ratio [OR], 14.07; 95% CI, 4.50-44.02; P < .001), subscapularis involvement (OR, 8.67; 95% CI, 2.45-30.71; P = .01), and increased preoperative CMS score (OR, 1.11; 95% CI, 1.04-1.18; P = .001). Protective factors included performance of a subpectoral biceps tenodesis compared with biceps tenotomy (OR, 0.22; 95% CI, 0.05-0.92; P = .03).

Conclusion: Clinically meaningful subgroups were uncovered using an unsupervised machine learning algorithm in patients undergoing arthroscopic RCR. Tear size, number of tendons involved, and subscapularis involvement were significant and additive predictors of suboptimal CSO achievement at 2-year minimum follow-up. Treatment of concurrent biceps pathology with tenodesis conferred 78% increased likelihood of CSO achievement compared with tenotomy.

Keywords: clinically significant outcome; machine learning; patient-reported outcome measurement; rotator cuff repair; rotator cuff tear; shoulder arthroscopy.

Figure 1.

(A) Visualization of the 2 random forest partitioned patient subgroups in 3-dimensional feature space; the axis is generated following principal component (PC) analysis to reduce the dimensionality of the data set for ease of visualization. (B) Global variable importance of feature contributions to the performance of the random forest clustering algorithm. BMI, body mass index; CMS, Constant-Murley score; MCID, minimal clinically important difference; PASS, Patient Acceptable Symptom State; SANE, Single Assessment Numeric Evaluation; SCB, substantial clinical benefit.