Real-World Study on Effectiveness of Insulin Glargine U300 After Oral Antidiabetic Drug Failure in Patients with Type 2 Diabetes in the Gulf Region.

INTRODUCTION: The effectiveness and safety of long-acting insulin glargine U300 (Gla-300), in patients with type 2 diabetes mellitus (T2DM) requiring insulin, has not been reported in the Gulf region. METHODS: Insulin-naïve patients with T2DM, uncontrolled on OADs, and prescribed Gla-300 were followed up in a 12-month prospective observational study. Gla-300 was titrated to glycemic targets. The primary endpoint (achieving glycemic targets) was evaluated at month 6 of treatment. The need for treatment intensification, safety, and patient-reported outcomes (PRO) were also reported. RESULTS: The study included 412 patients (61.7% men; age 52.2 ± 11.1 years and T2DM duration 10.7 ± 6.8 years). Almost 50% were on more than 3 OADs, mostly biguanides, sulfonylureas, and dipeptidyl-peptidase-4 inhibitors. Baseline HbA1c level was 9.2% ± 1.1% and targets were set at 6.9% ± 0.4%. Baseline fasting plasma glucose was 11.5 ± 3.8 mmol/l. Fifty-seven patients (13.8%) achieved glycemic targets at month 6, hindered by baseline HbA1c ≥ 10%, frequent co-morbidities, older age, suburban/rural residence, and full-time employment. Levels of HbA1c dropped progressively by 0.96% ± 0.07% (month 3), 1.29% ± 0.08% (month 6), and 1.76% ± 0.06% (month 12). Gla-300 dose was 17.0 ± 9.0 IU/day at baseline, 24.6 ± 9.6 IU/day at month 3, 28.5 ± 9.9 IU/day at month 6, and 30.7 ± 10.7 IU/day at month 12. Three patients experienced non-severe hypoglycemia and a slight decrease in body weight and PROs improved. CONCLUSIONS: In the Gulf, Gla-300 in patients with T2DM uncontrolled on OADs improved glycemic control, with low rates of hypoglycemia and improved PROs. Gla-300 dose up-titration from baseline to month 6 did not, however, result in a vast proportion of patients achieving their pre-determined HbA1c targets. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03703869.

Real-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing System.

Cardiac arrhythmia is known to be one of the most common causes of death worldwide. Therefore, development of efficient arrhythmia detection techniques is essential to save patients' lives. In this paper, we introduce a new real-time cardiac arrhythmia classification using memristor neuromorphic computing system for classification of 5 different beat types. Neuromorphic computing systems utilize new emerging devices, such as memristors, as a basic building block. Hence, these systems provide excellent trade-off between real-time processing, power consumption, and overall accuracy. Experimental results showed that the proposed system outperforms most of the methods in comparison in terms of accuracy and testing time, since it achieved 96.17% average accuracy and 34 ms average testing time per beat.