Assessing inter-rater reproducibility in MScanFit MUNE in a 6-subject, 12-rater “Round Robin” setup

ObjectiveTo assess the inter-rater reliability of MScanFit MUNE using a “Round Robin” research design.MethodsTwelve raters from different centres examined six healthy study participants over two days. Median, ulnar and common peroneal nerves were stimulated, and compound muscle action potential (CMAP)-scans were recorded from abductor pollicis brevis (APB), abductor digiti minimi (ADM) and anterior tibial (TA) muscles respectively. From this we calculated the Motor Unit Number Estimation (MUNE) and “A50”, a motor unit size parameter. As statistical analysis we used the measures Limits of Agreement (LOA) and Coefficient of Variation (COV). Study participants scored their perception of pain from the examinations on a rating scale from 0 (no pain) to 10 (unbearable pain).ResultsBefore this study, 41.6% of the raters had performed MScanFit less than five times. The mean MUNE-values were: 99.6 (APB), 131.4 (ADM) and 126.2 (TA), with LOA: 19.5 (APB), 29.8 (ADM) and 20.7 (TA), and COV: 13.4 (APB), 6.3 (ADM) and 5.6 (TA). MUNE-values correlated to CMAP max amplitudes (R²-values were: 0.463 (APB) (p<0.001), 0.421 (ADM) (p<0.001) and 0.645 (TA) (p<0.001)). The average perception of pain was 4.DiscussionMScanFit indicates a high level of inter-rater reliability, even with only limited rater experience and is overall reasonably well tolerated by patients. These results may indicate MScanFit as a reliable MUNE method with potential as a biomarker in drug trials.     

Quantifying and leveraging predictive uncertainty for medical image assessment

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. Current machine learning solutions for these problems are typically limited to providing probabilistic predictions, relying on the capacity of underlying models to adapt to limited information and the high degree of label noise. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams including computed radiography, ultrasonography and magnetic resonance imaging. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs. In addition, we show that using uncertainty-driven bootstrapping to filter the training data, one can achieve a significant increase in robustness and accuracy. Finally, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.     

Effectiveness,Patient Satisfaction,and Cost Reduction of Virtual Joint Replacement Clinic Follow-Up of Hip and Knee Arthroplasty

BackgroundTotal hip and knee arthroplasties are increasingly performed operations, and routine follow-up places huge demands on orthopedic services. This study investigates the effectiveness, patients’ satisfaction, and cost reduction of Virtual Joint Replacement Clinic (VJRC) follow-up of total hip arthroplasty and total knee arthroplasty patients in a university hospital. VJRC is especially valuable when in-person appointments are not advised or feasible such as during the COVID-19 pandemic.MethodsA total of 1749 patients who were invited for VJRC follow-up for knee or hip arthroplasty from January 2017 to December 2018 were included in this retrospective study. Patients were referred to VJRC after their 6-week postoperative review. Routine VJRC postoperative review was undertaken at 1 and 7 years and then 3-yearly thereafter. We evaluated the VJRC patient response rate, acceptability, and outcome. Patient satisfaction was measured in a subgroup of patients using a satisfaction survey. VJRC costs were calculated compared to face-to-face follow-up.ResultsThe VJRC had a 92.05% overall response rate. Only 7.22% required further in-person appointments with only 3% being reviewed by an orthopedic consultant. VJRC resulted in an estimated saving of £42,644 per year at our institution. The patients’ satisfaction survey showed that 89.29% of the patients were either satisfied or very satisfied with VJRC follow-up.ConclusionVJRC follow-up for hip and knee arthroplasty patients is an effective alternative to in-person clinic assessment which is accepted by patients, has high patient satisfaction, and can reduce the cost to both health services and patients.     

Bidirectional Association Between Sleep Quality and Low Back Pain in Older Adults: A Longitudinal Observational Study

ObjectiveTo investigate the bidirectional relationship by determining whether baseline sleep quality predicts pain intensity and whether baseline pain intensity predicts sleep quality in older individuals with chronic low back pain (LBP).DesignA prospective longitudinal cohort study with a 6-month follow-up period.SettingCommunity.ParticipantsOlder adults with LBP aged 60 years or older (N=215).InterventionNot applicable.Main Outcome MeasuresData collection occurred at baseline and at 6 months. Pain intensity and sleep quality were measured in both time points of assessment using the numeric pain rating scale (range, 0-10) and the Pittsburg Sleep Quality Index. At baseline, we also collected information on demographic anthropometric variables, cognitive status, depression, and comorbidities. Multivariable linear regression analyses adjusted for potential covariates were performed.ResultsA total of 215 individuals with LBP were recruited. Poor sleep quality at baseline predicted high pain intensity at 6 months (β coefficient, 0.18; 95% confidence interval [CI], 0.07-0.30). High pain intensity at baseline predicted poor sleep quality 6 months later (β coefficient, 0.14; 95% CI, 0.01-0.26).ConclusionOur findings give some support to the bidirectional relationship between pain and sleep quality in older individuals with LBP. This bidirectional relationship may be used as prognostic information by clinicians when managing patients with LBP.