Prevalence of amyloid deposition and cardiac amyloidosis in shoulder disease compared to carpal tunnel syndrome.

Background: Cardiac amyloidosis is a fatal disease of severe heart failure caused by the accumulation of amyloid in the myocardium. This disease is often advanced by the time cardiac symptoms appear; therefore, early detection and treatment are critical for a good prognosis. Recently, it has been suggested that cardiac amyloidosis is implicated in several orthopedic diseases, including carpal tunnel syndrome (CTS), which is often reported to precede cardiac dysfunction. Shoulder disease has also been suggested to be associated with cardiac amyloidosis; however, there have been no reports investigating the rate of amyloid deposition in shoulder specimens and the simultaneous prevalence of cardiac amyloidosis. Herein, we investigated the prevalence of intraoperative specimen amyloid deposition and cardiac amyloidosis in shoulder disease and CTS to determine the usefulness of shoulder specimen screening as a predictor of cardiac amyloidosis development. Methods: A total of 41 patients undergoing arthroscopic shoulder surgery and 33 patients undergoing CTS surgery were enrolled in this study. The shoulder group included rotator cuff tears, contracture of the shoulder, synovitis, and calcific tendonitis. In the shoulder group, a small sample of synovium and the long head of the biceps brachii tendon were harvested, while the transverse carpal ligament was harvested from the CTS group. The intraoperative specimens were pathologically examined for amyloid deposition, and patients with amyloid deposition were examined for the presence of cardiac amyloidosis by cardiac evaluation. Results: In the shoulder group, three cases (7.3%) of transthyretin amyloid deposition were found, all of which involved rotator cuff tears. None of these three cases with amyloid deposition were associated with cardiac amyloidosis. When examining the specimens, the amyloid deposition rate in the long head of the biceps brachii tendon was higher than that in the synovium. In the CTS group, 12 cases (36.4%) of transthyretin amyloid deposition were observed. Of these cases, seven underwent cardiac evaluation and two were identified with cardiac amyloidosis. Conclusion: While the prevalence of amyloid deposition and cardiac amyloidosis in the CTS group was consistent with previous reports, the shoulder group showed a lower deposition rate and no concomitant cardiac amyloidosis. Therefore, it remains debatable whether investigating amyloid deposition in samples obtained from shoulder surgery is beneficial for the early detection of cardiac amyloidosis.

Using In-Shoe Inertial Measurement Unit Sensors to Understand Daily-Life Gait Characteristics in Patients With Distal Radius Fractures During 6 Months of Recovery: Cross-Sectional Study.

BACKGROUND: A distal radius fracture (DRF) is a common initial fragility fracture among women in their early postmenopausal period, which is associated with an increased risk of subsequent fractures. Gait assessments are valuable for evaluating fracture risk; inertial measurement units (IMUs) have been widely used to assess gait under free-living conditions. However, little is known about long-term changes in patients with DRF, especially concerning daily-life gait. We hypothesized that, in the long term, the daily-life gait parameters in patients with DRF could enable us to reveal future risk factors for falls and fractures. OBJECTIVE: This study assessed the spatiotemporal characteristics of patients with DRF at 4 weeks and 6 months of recovery. METHODS: We recruited 16 women in their postmenopausal period with DRF as their first fragility fracture (mean age 62.3, SD 7.0 years) and 28 matched healthy controls (mean age 65.6, SD 8.0 years). Daily-life gait assessments and physical assessments, such as hand grip strength (HGS), were performed using an in-shoe IMU sensor. Participants' results were compared with those of the control group, and their recovery was assessed for 6 months after the fracture. RESULTS: In the fracture group, at 4 weeks after DRF, lower foot height in the swing phase (P=.049) and higher variability of stride length (P=.03) were observed, which improved gradually. However, the dorsiflexion angle in the fracture group tended to be lower consistently during 6 months (at 4 weeks: P=.06; during 6 months: P=.07). As for the physical assessments, the fracture group showed lower HGS at all time points (at 4 weeks: P<.001; during 6 months: P=.04), despite significant improvement at 6 months (P<.001). CONCLUSIONS: With an in-shoe IMU sensor, we discovered the recovery of spatiotemporal gait characteristics 6 months after DRF surgery without the participants' awareness. The consistently unchanged dorsiflexion angle in the swing phase and lower HGS could be associated with fracture risk, implying the high clinical importance of appropriate interventions for patients with DRF to prevent future fractures. These results could be applied to a screening tool for evaluating the risk of falls and fractures, which may contribute to constructing a new health care system using wearable devices in the near future.

A screening method for cervical myelopathy using machine learning to analyze a drawing behavior.

Early detection of cervical myelopathy (CM) is important for a favorable outcome, as its prognosis is poor when left untreated. We developed a screening method for CM using machine learning-based analysis of the drawing behavior of 38 patients with CM and 66 healthy volunteers. Using a stylus pen, the participants traced three different shapes displayed on a tablet device. During the tasks, writing behaviors, such as the coordinates, velocity, and pressure of the stylus tip, along with the drawing time, were recorded. From these data, features related to the drawing pressure, and time to trace each shape and combination of shapes were used as training data for the support vector machine, a machine learning algorithm. To evaluate the accuracy, a receiver operating characteristic curve was generated, and the area under the curve (AUC) was calculated. Models with triangular waveforms tended to be the most accurate. The best triangular wave model identified patients with and without CM with 76% sensitivity and 76% specificity, yielding an AUC of 0.80. Our model was able to classify CM with high accuracy and could be applied to the development of disease screening systems useful outside the hospital setting.