Efficiency and Accuracy of Computerized Adaptive Testing for the Oswestry Disability Index and Neck Disability Index.

This study aimed to determine the efficiency and accuracy of computerized adaptive testing (CAT) models of the Oswestry Disability Index (ODI) and Neck Disability Index (NDI). Methods: The study involved simulation using retrospectively collected real-world data. Previously developed CAT models of the ODI and NDI were applied to the responses from 52,551 and 18,196 patients with spinal conditions, respectively. Efficiency was evaluated by the reduction in the number of questions administered. Accuracy was evaluated by comparing means and standard deviations, calculating Pearson r and intraclass correlation coefficient (ICC) values, plotting the frequency distributions of CAT and full questionnaire scores, plotting the frequency distributions of differences between paired scores, and Bland-Altman plotting. Score changes, calculated as the postoperative ODI or NDI scores minus the preoperative scores, were compared between the CAT and full versions in patients for whom both preoperative and postoperative ODI or NDI questionnaires were available. Results: CAT models of the ODI and NDI required an average of 4.47 and 4.03 fewer questions per patient, respectively. The mean CAT ODI score was 0.7 point lower than the full ODI score (35.4 ± 19.0 versus 36.1 ± 19.3), and the mean CAT NDI score was 1.0 point lower than the full NDI score (34.7 ± 19.3 versus 33.8 ± 18.5). The Pearson r was 0.97 for both the ODI and NDI, and the ICC was 0.97 for both. The frequency distributions of the CAT and full scores showed marked overlap for the ODI and NDI. Differences between paired scores were less than the minimum clinically important difference in 98.9% of cases for the ODI and 98.5% for the NDI. Bland-Altman plots showed no proportional bias. The ODI and NDI score changes could be calculated in a subgroup of 6,044 and 4,775 patients, respectively; the distributions of the ODI and NDI score changes were near identical between the CAT and full versions. Conclusions: CAT models were able to reduce the question burden of the ODI and NDI. Scores obtained from the CAT models were faithful to those from the full questionnaires, both on the population level and on the individual patient level. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Computerized adaptive testing for the Oxford Hip, Knee, Shoulder, and Elbow scores : accurate measurement from fewer, and more patient-focused, questions.

AIMS: The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales. METHODS: We developed CAT algorithms for the OHS, OKS, OSS, overall OES, and each of the OES subscales, using responses to the full-length questionnaires and a machine-learning technique called regression tree learning. The algorithms were evaluated through a series of simulation studies, in which they aimed to predict respondents' full-length questionnaire scores from only a selection of their item responses. In each case, the total number of items used by the CAT algorithm was recorded and CAT scores were compared to full-length questionnaire scores by mean, SD, score distribution plots, Pearson's correlation coefficient, intraclass correlation (ICC), and the Bland-Altman method. Differences between CAT scores and full-length questionnaire scores were contextualized through comparison to the instruments' minimal clinically important difference (MCID). RESULTS: The CAT algorithms accurately estimated 12-item questionnaire scores from between four and nine items. Scores followed a very similar distribution between CAT and full-length assessments, with the mean score difference ranging from 0.03 to 0.26 out of 48 points. Pearson's correlation coefficient and ICC were 0.98 for each 12-item scale and 0.95 or higher for the OES subscales. In over 95% of cases, a patient's CAT score was within five points of the full-length questionnaire score for each 12-item questionnaire. CONCLUSION: Oxford Hip Score, Oxford Knee Score, Oxford Shoulder Score, and Oxford Elbow Score (including separate subscale scores) CATs all markedly reduce the burden of items to be completed without sacrificing score accuracy.Cite this article: Bone Jt Open 2022;3(10):786-794.

Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty.

The ability to accurately predict postoperative outcomes is of considerable interest in the field of orthopaedic surgery. Machine learning has been used as a form of predictive modeling in multiple health-care settings. The purpose of the current study was to determine whether machine learning algorithms using preoperative data can predict improvement in American Shoulder and Elbow Surgeons (ASES) scores for patients with glenohumeral osteoarthritis (OA) at a minimum of 2 years after shoulder arthroplasty. METHODS: This was a retrospective cohort study that included 472 patients (472 shoulders) diagnosed with primary glenohumeral OA (mean age, 68 years; 56% male) treated with shoulder arthroplasty (431 anatomic total shoulder arthroplasty and 41 reverse total shoulder arthroplasty). Preoperative computed tomography (CT) scans were used to classify patients on the basis of glenoid and rotator cuff morphology. Preoperative and final postoperative ASES scores were used to assess the level of improvement. Patients were separated into 3 improvement ranges of approximately equal size. Machine learning methods that related patterns of these variables to outcome ranges were employed. Three modeling approaches were compared: a model with the use of all baseline variables (Model 1), a model omitting morphological variables (Model 2), and a model omitting ASES variables (Model 3). RESULTS: Improvement ranges of ≤28 points (class A), 29 to 55 points (class B), and >55 points (class C) were established. Using all follow-up time intervals, Model 1 gave the most accurate predictions, with probability values of 0.94, 0.95, and 0.94 for classes A, B, and C, respectively. This was followed by Model 2 (0.93, 0.80, and 0.73) and Model 3 (0.77, 0.72, and 0.71). CONCLUSIONS: Machine learning can accurately predict the level of improvement after shoulder arthroplasty for glenohumeral OA. This may allow physicians to improve patient satisfaction by better managing expectations. These predictions were most accurate when latent variables were combined with morphological variables, suggesting that both patients' perceptions and structural pathology are critical to optimizing outcomes in shoulder arthroplasty. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.