Patient-Reported Outcomes and Function after Surgical Repair of the Ulnar Collateral Ligament of the Thumb

Purpose: The purpose of this study was to report prospectively collected patient-reported outcomes of patients who underwent open thumb ulnar collateral ligament (UCL) repair and to find risk factors associated with poor patient-reported outcomes. Methods: Patients undergoing open surgical repair for a complete thumb UCL rupture were included between December 2011 and February 2021. Michigan Hand Outcomes Questionnaire (MHQ) total scores at baseline were compared to MHQ total scores at three and 12 months after surgery. Associations between the 12-month MHQ total score and several variables (i.e., sex, injury to surgery time, K-wire immobilization) were analyzed. Results: Seventy-six patients were included. From baseline to three and 12 months after surgery, patients improved significantly with a mean MHQ total score of 65 (standard deviation [SD] 15) to 78 (SD 14) and 87 (SD 12), respectively. We did not find any differences in outcomes between patients who underwent surgery in the acute (&lt;3 weeks) setting compared to a delayed setting (&lt;6 months). Conclusions: We found that patient-reported outcomes improve significantly at three and 12 months after open surgical repair of the thumb UCL compared to baseline. We did not find an association between injury to surgery time and lower MHQ total scores. This suggests that acute repair for full-thickness UCL tears might not always be necessary. Type of study/level of evidence: Therapeutic II.</p

Measurement of Ulnar Variance on Uncalibrated Digital Radiographic Images

Uncalibrated digital radiographs used in multicenter trials hinder quantitative measures such as articular step and ulnar variance. This investigation tested the reliability of alternative measures of ulnar variance that are scaled to the length of the capitate. A sample of 30 sets of radiographs from patients enrolled in a prospective study of operative treatment of fractures of the distal radius were blinded and randomized. Five observers measured the ulnar variance (UV) and longitudinal length of the capitate (CH) on two separate occasions with greater than 2 weeks between measurements. During each measurement session, the observers made the measurements on both a calibrated and a noncalibrated workstation. The ratio of the ulnar variance to the length of capitate was calculated (UV/CH ratio). Paired t tests were used to compare two rounds of measurements for both methods. Intra- and interobserver reliability was assessed by the Pearson product-moment correlation coefficients. The ratios were compared using analysis of variance with a Bonferroni correction. The intraobserver reliability was excellent for each of the three variables (UV, CH, UV/CH ratio) for each workstation. The interobserver reliability of the UV/CH ratios obtained for each workstation was moderate to excellent as judged by the Pearson correlations between observers. The Bland–Altman method indicated a mean difference in UV/CH between calibrated and uncalibrated measurement techniques of 0.002 with limits of agreement of −0.11 to 0.11. Measurements of ulnar variance that are scaled to the length of the capitate may be useful measures of deformity in studies that utilize uncalibrated digital radiographs

The predictive value of the extensor grip test for the effectiveness of bracing for tennis elbow

Background: Tennis elbow is a common complaint. Several treatment strategies, such as corticosteroid injections and physical therapy and braces, have been described. Hypothesis: The extensor grip test has predictive value in assessing the effectiveness of bracing in tennis elbow. Study Design: Cohort study (prognosis); Level of evidence, 1. Methods: Patients with tennis elbow complaints were randomized into 3 groups: brace only, physical therapy, and combination brace and physical therapy. The extensor grip test was performed before randomization on all patients. Outcome measures at 6-week follow-up were success rate, severity of complaints, pain, disability, inconvenience during daily life, and satisfaction. Results: In the brace-only group, significant differences were identified between patients with a positive test result and patients with a negative test result for 3 outcome measures. The success rate in the test-negative group was 23% (5/22) compared to 47% (21/45) in the test-positive group. Mean decrease in pain was 23 (95% confidence interval, -3 to 49) in the test-positive group compared to 11 (95% confidence interval, -6 to 28) in the test-negative group, and mean satisfaction in the test-positive group was 71 (95% confidence interval, 48 to 94) compared to 51 (95% confidence interval, 24 to 78) in the test-negative group. In the physical therapy and combination groups, no differences were identified between test-positive and test-negative patients. Conclusion: The extensor grip test seems valuable as a predictive factor for the effectiveness of bracing as treatment for tennis elbow over the short ter

Predicting Clinically Relevant Patient-Reported Symptom Improvement After Carpal Tunnel Release: A Machine Learning Approach

BACKGROUND: Symptom improvement is an important goal when considering surgery for carpal tunnel syndrome. There is currently no prediction model available to predict symptom improvement for patients considering a carpal tunnel release (CTR).  OBJECTIVE: To predict using a model the probability of clinically relevant symptom improvement at 6 mo after CTR.  METHODS: We split a cohort of 2119 patients who underwent a mini-open CTR and completed the Boston Carpal Tunnel Questionnaire preoperatively and 6 mo postoperatively into training (75%) and validation (25%) data sets. Patients who improved more than the minimal clinically important difference of 0.8 at the Boston Carpal Tunnel Questionnaire-symptom severity scale were classified as "improved." Logistic regression, random forests, and gradient boosting machines were considered to train prediction models. The best model was selected based on discriminative ability (area under the curve) and calibration in the validation data set. This model was further assessed in a holdout data set (N = 397).  RESULTS: A gradient boosting machine with 5 predictors was chosen as optimal trade-off between discriminative ability and the number of predictors. In the holdout data set, this model had an area under the curve of 0.723, good calibration, sensitivity of 0.77, and specificity of 0.55. The positive predictive value was 0.50, and the negative predictive value was 0.81.  CONCLUSION: We developed a prediction model for clinically relevant symptom improvement 6 mo after a CTR, which required 5 patient-reported predictors (18 questions) and has reasonable discriminative ability and good calibration. The model is available online and might help shared decision making when patients are considering a CTR

Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis

BACKGROUND: Surgery for thumb carpometacarpal osteoarthritis is offered to patients who do not benefit from nonoperative treatment. Although surgery is generally successful in reducing symptoms, not all patients benefit. Predicting clinical improvement after surgery could provide decision support and enhance preoperative patient selection. QUESTIONS/PURPOSES: This study aimed to develop and validate prediction models for clinically important improvement in (1) pain and (2) hand function 12 months after surgery for thumb carpometacarpal osteoarthritis. METHODS: Between November 2011 and June 2020, 2653 patients were surgically treated for thumb carpometacarpal osteoarthritis. Patient-reported outcome measures were used to preoperatively assess pain, hand function, and satisfaction with hand function, as well as the general mental health of patients and mindset toward their condition. Patient characteristics, medical history, patient-reported symptom severity, and patient-reported mindset were considered as possible predictors. Patients who had incomplete Michigan Hand outcomes Questionnaires at baseline or 12 months postsurgery were excluded, as these scores were used to determine clinical improvement. The Michigan Hand outcomes Questionnaire provides subscores for pain and hand function. Scores range from 0 to 100, with higher scores indicating less pain and better hand function. An improvement of at least the minimum clinically important difference (MCID) of 14.4 for the pain score and 11.7 for the function score were considered "clinically relevant." These values were derived from previous reports that provided triangulated estimates of two anchor-based and one distribution-based MCID. Data collection resulted in a dataset of 1489 patients for the pain model and 1469 patients for the hand function model. The data were split into training (60%), validation (20%), and test (20%) dataset. The training dataset was used to select the predictive variables and to train our models. The performance of all models was evaluated in the validation dataset, after which one model was selected for further evaluation. Performance of this final model was evaluated on the test dataset. We trained the models using logistic regression, random forest, and gradient boosting machines and compared their performance. We chose these algorithms because of their relative simplicity, which makes them easier to implement and interpret. Model performance was assessed using discriminative ability and qualitative visual inspection of calibration curves. Discrimination was measured using area under the curve (AUC) and is a measure of how well the model can differentiate between the outcomes (improvement or no improvement), with an AUC of 0.5 being equal to chance. Calibration is a measure of the agreement between the predicted probabilities and the observed frequencies and was assessed by visual inspection of calibration curves. We selected the model with the most promising performance for clinical implementation (that is, good model performance and a low number of predictors) for further evaluation in the test dataset. RESULTS: For pain, the random forest model showed the most promising results based on discrimination, calibration, and number of predictors in the validation dataset. In the test dataset, this pain model had a poor AUC (0.59) and poor calibration. For function, the gradient boosting machine showed the most promising results in the validation dataset. This model had a good AUC (0.74) and good calibration in the test dataset. The baseline Michigan Hand outcomes Questionnaire hand function score was the only predictor in the model. For the hand function model, we made a web application that can be accessed via https://analyse.equipezorgbedrijven.nl/shiny/cmc1-prediction-model-Eng/. CONCLUSION: We developed a promising model that may allow clinicians to predict the chance of functional improvement in an individual patient undergoing surgery for thumb carpometacarpal osteoarthritis, which would thereby help in the decision-making process. However, caution is warranted because our model has not been externally validated. Unfortunately, the performance of the prediction model for pain is insufficient for application in clinical practice. LEVEL OF EVIDENCE: Level III, therapeutic study

Agreement between Initial Classification and Subsequent Reclassification of Fractures of the Distal Radius in a Prospective Cohort Study

We tested the hypothesis that the original surgeon-investigator classification of a fracture of the distal radius in a prospective cohort study would have moderate agreement with the final classification by the team performing final analysis of the data. The initial post-injury radiographs of 621 patients with distal radius fractures from a multicenter international prospective cohort study were classified according to the Comprehensive Classification of Fractures, first by the treating surgeon-investigator and then by a research team analyzing the data. Correspondence between original and revised classification was evaluated using the Kappa statistic at the type, group and subgroup levels. The agreement between initial and revised classifications decreased from Type (moderate; Κtype = 0.60), to Group (moderate; Κgroup = 0.41), to Subgroup (fair; Κsubgroup = 0.33) classifications (all p < 0.05). There was only moderate agreement in the classification of fractures of the distal radius between surgeon-investigators and final evaluators in a prospective multicenter cohort study. Such variations might influence interpretation and comparability of the data. The lack of a reference standard for classification complicates efforts to lessen variability and improve consensus

Reply to the Letter to the Editor: Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis

To the Editor,We would like to thank Kabuli et al. for their interest in our study and for sharing their perspectives on the importance of anxiety on the outcomes of surgery. [...] We fully agree with the letter authors on the importance of considering the psychological profiles of patients when predicting outcomes after surgical treatment of the first carpometacarpal with osteoarthritis (CMC-1 OA).[...] We therefore included several psychological variables as possible predictors for our models, including the PHQ-4 for depression and anxiety. [...