A Comprehensive Analysis of Class I Medical Device Recalls: Unveiling Patterns, Causes and Global Impacts.

In the landscape of medical device regulation, Class I recalls serve as pivotal indicators of potential risks, necessitating comprehensive analysis to unveil underlying patterns and causal factors. This research offers a detailed examination of Class 1 recalls, focusing on the critical aspects of device classification, review panel involvement, and the geographic distribution of recalling companies. Utilizing a robust dataset spanning multiple jurisdictions and device categories, this study reveals recurring trends in recall occurrences, providing insights into the regulatory mechanisms governing device safety assessments. Furthermore, it investigates the role of review panels in evaluating device safety and effectiveness, shedding light on their significance in the recall process. Moreover, the analysis explores the countries of origin of companies initiating recalls, offering insights into the global impact of regulatory actions on medical device manufacturers. By understanding the drivers behind recall decisions and their implications on a regional scale, regulatory authorities, and healthcare stakeholders can implement targeted measures to enhance patient safety and strengthen post-market surveillance practices.

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals.

Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals.

Automated detection of obstructive sleep apnea in more than 8000 subjects using frequency optimized orthogonal wavelet filter bank with respiratory and oximetry signals.

Obstructive sleep apnea (OSA) is a common respiratory disorder marked by interruption of the respiratory tract and difficulty in breathing. The risk of serious health damage can be reduced if OSA is diagnosed and treated at an early stage. OSA is primarily diagnosed using polysomnography (PSG) monitoring performed for overnight sleep; furthermore, capturing PSG signals during the night is expensive, time-consuming, complex and highly inconvenient to patients. Hence, we are proposing to detect OSA automatically using respiratory and oximetry signals. The aim of this study is to develop a simple and computationally efficient wavelet-based automated system based on these signals to detect OSA in elderly subjects. In this study, we proposed an accurate, reliable, and less complex OSA automated detection system by using pulse oximetry (SpO2) and respiratory signals including thoracic (ThorRes) movement, abdominal (AbdoRes) movement, and airflow (AF). These signals are collected from the Sleep Heart Health Study (SHHS) database from the National Sleep Research Resource (NSRR), which is one of the largest repositories of publicly available sleep databases. The database comprises of two groups SHHS-1 and SHHS-2, which involves 5,793 and 2,651 subjects, respectively with an average age of ≥60 years. The 30-s epochs of the signals are decomposed into sub-bands using frequency optimized orthogonal wavelet filter bank. Tsallis entropies are extracted from the sub-band coefficients of wavelet filter bank. A total 4,415,229 epochs of respiratory and oximetry signals are used to develop the model. The proposed model is developed using GentleBoost and Random under-sampling Boosting (RUSBoosted Tree) algorithms with 10-fold cross-validation technique. Our developed model has obtained the highest classification accuracy of 89.39% and 84.64% for the imbalanced and balanced datasets, respectively using 10-fold cross-validation technique. Using the 20% hold-out validation, the model yielded an accuracy of 88.26% and 84.31% for the imbalanced and balanced datasets, respectively. Hence, the respiratory and SpO2 signals-based model can be used for automated OSA detection. The results obtained from the proposed model are better than the state-of-the-art models and can be used in-home for screening the OSA.

Comparative evaluation of efficacy, pharmacodynamics, and safety of Hetero's adalimumab (Mabura®, Hetero Biopharma Ltd.) and reference adalimumab (Humira®, Abbvie Inc.) in patients with active rheumatoid arthritis on concomitant methotrexate therapy.

BACKGROUND: Our study aimed to compare efficacy and safety of Hetero's adalimumab (Mabura®, Hetero Biopharma Limited) versus reference adalimumab (Humira®, Abbvie Inc.) in Indian patients with active rheumatoid arthritis (RA) concomitant on methotrexate (MTX) therapy. METHODS: Patients (n = 168) were randomized (2:1) to receive either test or reference product for 24 weeks with concomitant MTX. Proportion of patients achieving American College of Rheumatology 20 (ACR20) criteria at week 12 was the primary endpoint. Changes in Disease Activity Score of 28 joints-C-reactive protein (DAS28-CRP), Health Assessment Questionnaire-Disability Index (HAQ-DI), and patients achieving ACR20 at week 24, ACR50/70 at weeks 12 and 24 were secondary endpoints. RESULTS: Patients achieving ACR20 responses with test (96.43%) were similar to reference (96.43%) in intention-to-treat (ITT) analysis at week 12. Proportional difference (PD) between groups (PD [95% CI] 0.0 [- 6.0, 6.0], p = 1.000) for ACR20 at week 12 for ITT analysis showed lower limit of the two-sided 95% CI was above the pre-specified noninferiority margin of - 15%. Similar trend in PP analysis (PD [95% CI] 0.0 [- 0.03, 0.07], p = 1.000), confirmed therapeutic equivalence. No significant difference was noted between arms for patients attaining ACR20 at week 24 and ACR50/70 at weeks 12 and 24 (all p > 0.05). DAS28-CRP and HAQ-DI were similar between groups. Total of 54 patients reported 88 AEs during the study. Out of these, 60 AEs were reported in 34 patients with Hetero-Adalimumab and 28 AEs were reported in 20 patients with Reference-Adalimumab. Total two patients, one in each group reported two serious adverse events (Sinusitis and Viral infection) during the study and resolved completely. No deaths and no life threatening AEs were reported. CONCLUSION: Results demonstrated Hetero's adalimumab is as effective and well tolerated as reference adalimumab in patients with active RA concomitantly on MTX therapy. TRIAL REGISTRATION: CTRI/2016/04/006884, Registered on 28/04/2016.