METHOD: In this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms.
RESULTS: The random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect reliability based on Kappa statistics and a high correlation between the predicted output and actual class of the EDIC patients when all six MNSI variables were considered as inputs.
CONCLUSIONS: This study suggests that the RF algorithm-based classifier using all MNSI variables can help to predict the DSPN severity which will help to enhance the medical facilities for diabetic patients.
Methods: A keyword search was performed across Google Scholar and PubMed. Articles describing trigger wrist conditions were analysed. Based on the information obtain from the articles, the clinical manifestations and approach to diagnosing the cause of trigger wrist is discussed.
Results: A detailed history alone may lead to a reasonably accurate diagnosis. Patients can present with trigger wrist occurring during movement of the fingers or with wrist movements. Presence of tenderness around A1 pulley suggest trigger finger. Absence of tenderness over the A1 pulley may suggest trigger wrist. The wrist should be examined for any swelling or malunion around the wrist joint. Palpate for any bony prominence, clicking, or crepitus with the movement of the wrist. Examination for the presence of carpal tunnel syndrome should be performed. A simple radiograph of the wrist joint is needed to see any possible bony pathology such as malunion, instability or arthritis of the carpal bone. For soft tissue assessment ultrasound would be a good choice and can be done during finger or wrist movement. MRI is useful for further assessment of space occupying lesion within the carpal tunnel and is useful for surgical planning. Nerve conduction study is indicated for patients with median nerve compression symptoms. During the initial stage, the patient should be advised for activity modification to reduce the wrist and finger movements. Surgical treatment will depend on the causative factor. Surgery done under local anaesthesia has the advantage of reconfirming with the patient, resolution of triggering during surgery by asking the patient to actively move the fingers or wrist.
Conclusions: Trigger wrist is a relatively rare condition compared with trigger finger, which is the most common disorder of the hand. To avoid inadequate and ineffective treatment of patients with trigger wrist, careful examination and proper diagnosis are vital.
METHODS: Of the 75 patients enrolled in the MARVEL 2 study, 73 had a rhythm assessment and were included in the analysis. The enhanced MARVEL 2 algorithm included a mode-switching algorithm that automatically switches between VDD and ventricular only antibradycardia pacing (VVI)-40 depending upon AVC status.
RESULTS: Forty-two patients (58%) had persistent third degree AV block (AVB), 18 (25%) had 1:1 AVC, 5 (7%) had variable AVC status, and 8 (11%) had atrial arrhythmias. Among the 42 patients with persistent third degree AVB, the median ventricular pacing (VP) percentage was 99.9% compared to 0.2% among those with 1:1 AVC. As AVC status changed, the algorithm switched to VDD when the ventricular rate dropped less than 40 bpm. During atrial fibrillation (AF) with ventricular response greater than 40 bpm, VVI-40 mode was maintained. No pauses longer than 1500 ms were observed. Frequent ventricular premature beats reduced the percentage of AV synchrony. During AF, the atrial signal was of low amplitude and there was infrequent sensing.
CONCLUSION: The mode switching algorithm reduced VP in patients with 1:1 AVC and appropriately switched to VDD during AV block. No pacing safety issues were observed during arrhythmias.