Effect of Sodium-Glucose Cotransporter 2 Inhibitors on the 24-Hour Ambulatory Blood Pressure in Patients With Type 2 Diabetes Mellitus and Hypertension: An Updated Meta-Analysis.

OBJECTIVE: Sodium-glucose cotransporter 2 inhibitors (SGLT2i), initially developed for type 2 diabetes mellitus (DM) treatment, have shown potential benefits beyond glycemic control, including a positive impact on the blood pressure (BP). This meta-analysis aimed to evaluate their effects on patients with type 2 DM and hypertension. METHODS: We searched the PubMed, Google Scholar, and Cochrane databases for relevant randomized controlled trials published until May 31, 2023. Ten randomized controlled trials involving participants with confirmed type 2 DM were selected. The intervention group received SGLT2i, whereas the control group received a placebo or standard care. The primary outcomes were the 24-hour ambulatory systolic BP (SBP) and diastolic BP (DBP). RESULTS: The results showed a significant reduction in the 24-hour ambulatory SBP (weighted mean difference, -5.08 mm Hg; 95% confidence interval, -7.02 to -3.14; P <.00001) and DBP (weighted mean difference, -2.73 mm Hg; 95% confidence interval, -4.25 to -1.20; P =.0005) with the use of SGLT2i compared with that using the placebo. However, a high-heterogeneity level was observed in both analyses (SBP, I2 = 83%; DBP, I2 = 91%). Sensitivity analysis excluding specific studies reduced heterogeneity while maintaining statistically significant and clinically relevant reductions in the BP. CONCLUSION: In conclusion, this meta-analysis proves that SGLT2i significantly reduce the 24-hour ambulatory BP. SGLT2i may be considered an effective treatment option for lowering the BP in addition to standard care in patients with hypertension and type 2 DM.

Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases.

Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.

Assessing the efficacy of celecoxib after tonsillectomy and/or adenoidectomy: A systematic review and meta-analysis of randomised control trials.

OBJECTIVES: Tonsillectomy and adenoidectomy are common surgical procedures that cause persistent pain, bleeding, and functional limitations. We aimed to investigate the efficacy of celecoxib compared with a placebo for managing post-tonsillectomy or adenoidectomy pain and other adverse events. DESIGN: Systematic review and meta-analysis. METHODS: We conducted a systematic literature search in the PubMed, Cochrane, and Google Scholar databases from inception until July 2023. Dichotomous outcomes have been reported as risk ratios (RR) while continuous outcomes were reported using mean differences (MD). A funnel plot was drawn to investigate publication bias. RESULTS: From 1394 records identified, 6 randomised double-blind trials comprising 591 participants undergoing tonsillectomy and/or adenoidectomy were eligible for inclusion. A high dose (400 mg) of celecoxib was effective in decreasing the pain score for 'worst pain' after the procedure (MD: -10.98, [95% CI: -11.53, -10.42], p < .01, I2 = 0%) while a low dose (200 mg) was not significantly effective (p = 0.31). For managing other outcomes such as vomiting (RR: 1.37 [95% CI: 0.69, 2.68], p = 0.37, I2 = 67%), diarrhoea (RR: 1.41, [95% CI: 0.75, 2.64], p = .29, I2 = 42%), dizziness/drowsiness (RR: 0.90, [95% CI: 0.71, 1.15], p = .48, I2 = 0%), functional recovery time (p = .74), and headache (p = .91), there was no significant difference between the group on celecoxib and the placebo group regardless of dosage. Finally, there was no significant difference (RR: 1.02, [95% CI: 0.91, 1.15], p = .69, I2 = 0%) in the effect of the intervention on minimum bleeding, moderate bleeding, and profuse bleeding. CONCLUSION: This meta-analysis provides robust evidence pooled from high-quality trials and raises questions about the efficacy of celecoxib for tonsillectomy and/or adenoidectomy, challenging existing perceptions.