Polypill-based strategy vs. usual care for secondary prevention of cardiovascular disease: a meta-analysis of randomized controlled trials.

AIMS: We aimed to perform a systematic review and meta-analysis of randomized controlled trials (RCTs) to determine the impact of a polypill-based strategy (PBS) on therapeutic adherence and cardiovascular outcomes compared with usual care for secondary prevention of cardiovascular diseases (CVDs). METHODS AND RESULTS: We systematically searched PubMed, Cochrane, and Scopus databases from inception to January 2023, including RCTs comparing PBS with usual care in patients with prior CVD. We assessed efficacy outcomes of therapeutic adherence, systolic blood pressure (SBP), and LDL-cholesterol (LDL-C) and safety outcomes of all-cause and cardiovascular mortality. Statistical analysis was performed with Review Manager 5.4.1 and R Version 4.2.1. A total of 8 RCTs with a population of 6541 individuals were included, of whom 3318 (50.7%) were treated with the PBS. Follow-up ranged from 6 to 60 months. The polypill-based strategy was associated with a significantly increased therapeutic adherence [risk ratio (RR) 1.22; 95% confidence interval (CI) 1.10-1.34; P < 0.001]. Cardiovascular mortality (RR 0.61; 95% CI 0.44-0.85; P = 0.004), SBP [mean difference (MD) -1.47 mmHg; 95% CI -2.86 to -0.09; P = 0.04], and LDL-C (MD -3.83 mg/dL; 95% CI -6.99 to -0.67; P = 0.02) were significantly lower in the PBS group. The incidence of all-cause mortality was similar between groups (RR 0.83; 95% CI 0.54-1.29; P = 0.41). CONCLUSION: In patients with pre-existing CVD, a PBS is associated with lower cardiovascular mortality and improved therapeutic adherence, along with a modest decrease in SBP and LDL-C compared with usual care. Thus, a PBS may be considered a preferred option for this patient population.

Adherence to medical therapy plays a critical role in the prevention of atherosclerotic events. Previous studies have shown that a polypill-based strategy (PBS) increases treatment adherence in the context of primary prevention of cardiovascular diseases. However, the effectiveness of this strategy in secondary prevention is yet to be determined. Herein, we demonstrate the following: Polypill-based strategy improved therapeutic adherence and reduced LDL-cholesterol and systolic blood pressure levels.There was a reduction in cardiovascular mortality with the use of the PBS; however, no significant difference was found in all-cause mortality between groups.

Artificial intelligence for triaging of breast cancer screening mammograms and workload reduction: A meta-analysis of a deep learning software.

OBJECTIVE: Deep learning (DL) has shown promising results for improving mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) on the breast cancer screening process has not yet been fully elucidated in terms of potential workload reduction. We aim to assess if AI-based triaging of breast cancer screening mammograms could reduce the radiologist's workload with non-inferior sensitivity. METHODS: PubMed, EMBASE, Cochrane Central, and Web of Science databases were systematically searched for studies that evaluated AI algorithms on computer-aided triage of breast cancer screening mammograms. We extracted data from homogenous studies and performed a proportion meta-analysis with a random-effects model to examine the radiologist's workload reduction (proportion of low-risk mammograms that could be theoretically ruled out from human's assessment) and the software's sensitivity to breast cancer detection. RESULTS: Thirteen studies were selected for full review, and three studies that used the same commercially available DL algorithm were included in the meta-analysis. In the 156,852 examinations included, the threshold of 7 was identified as optimal. With these parameters, radiologist workload decreased by 68.3% (95%CI 0.655-0.711, I² = 98.76%, p < 0.001), while achieving a sensitivity of 93.1% (95%CI 0.882-0.979, I² = 83.86%, p = 0.002) and a specificity of 68.7% (95% CI 0.684-0.723, I² = 97.5%, p < 0.01). CONCLUSIONS: The deployment of DL computer-aided triage of breast cancer screening mammograms reduces the radiology workload while maintaining high sensitivity. Although the implementation of AI remains complex and heterogeneous, it is a promising tool to optimize healthcare resources.