Antiarrhythmic drug therapy and catheter ablation in patients with paroxysmal or persistent atrial fibrillation: a systematic review and meta-analysis.

BACKGROUND: Catheter ablation and antiarrhythmic drug therapy are utilized for rhythm control in atrial fibrillation (AF), but their comparative effectiveness, especially with contemporary treatment modalities, remains undefined. We conducted a systematic review and meta-analysis contrasting current ablation techniques against antiarrhythmic medications for AF. METHODS: We searched PubMed, SCOPUS, Cochrane CENTRAL, and Web of Science until November 2023 for randomized trials comparing AF catheter ablation with antiarrhythmics, against antiarrhythmic drug therapy alone, reporting outcomes for > 6 months. Four investigators extracted data and appraised risk of bias (ROB) with ROB 2 tool. Meta-analyses estimated pooled efficacy and safety outcomes using R software. RESULTS: Twelve trials (n = 3977) met the inclusion criteria. Catheter ablation was associated with lower AF recurrence (relative risk (RR) = 0.44, 95%CI (0.33, 0.59), P ˂ 0.0001) and hospitalizations (RR = 0.44, 95%CI (0.23, 0.82), P = 0.009) than antiarrhythmic medications. Catheter ablation also improved the physical quality of life component score (assessed by a 36-item Short Form survey) by 7.61 points (95%CI -0.70-15.92, P = 0.07); but, due to high heterogeneity, it was not statistically significant. Ablation was significantly associated with higher procedural-related complications [RR = 15.70, 95%CI (4.53, 54.38), P < 0.0001] and cardiac tamponade [RR = 9.22, 95%CI (2.16, 39.40), P = 0.0027]. All-cause mortality was similar between the two groups. CONCLUSIONS: For symptomatic AF, upfront catheter ablation reduces arrhythmia and hospitalizations better than continued medical therapy alone, albeit with moderately more adverse events. Careful patient selection and risk-benefit assessment are warranted regarding the timing of ablation.

Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool.

Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer's ability to make decisions more accurately. Due to AI's ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI's ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.