The role of aspirin, statins, colchicine, and IL-1 inhibitors in prevention of cardiovascular events: a systematic integrative review.

CONTEXT: Cardiovascular disease (CVD) is the leading cause of death in the United States. As such, an unmet need exists in the primary and secondary prevention of adverse cardiovascular events (CVEs). Specifically, identifying drugs that can reduce the progression of CVD and serious adverse events is much needed. Drugs that work by reducing platelet aggregation, blocking cholesterol formation (3-hydroxy-3-methyl-glutaryl-coenzyme A [HMG-CoA] reductase inhibitors), and/or blocking inflammation pathways (mainly interleukin-1b [IL-1b]) have been linked to preventing adverse CVEs, including acetylsalicylic acid (ASA, aspirin), statins, colchicine, and IL-1 inhibitors (interleukin-1 receptor antagonists). This systematic review aims to provide insight into utilizing these four agents for the primary and/or secondary prevention of CVD. OBJECTIVES: In this systematic review, we opted to review the efficacy of aspirin, statins, colchicine, and IL-1 inhibitors in the primary and secondary prevention of CVE to provide clinical practitioners with evidence-based practice approaches and determine any unmet needs in their utilization. METHODS: Between October 1 and 12, 2021, a search was conducted and completed on five databases: PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and Biomedical Reference Collection: Comprehensive. A total of 13 researchers (V.A., A.H., S.B., V.G., D.C., C.C., C.B., C.A., S.K., J.H., A.K., S.F., and S.E.) were involved in the search and screening of the articles. Search terms included "aspirin, statins, colchicine, IL-1 inhibitors, and primary, secondary, myocardial infarction (MI)." Inclusion criteria included clinical study design, English language articles, all genders older than 50 years old, and established patient history of CVD, including MI. In addition, articles were excluded if they were animal models, in vitro studies, pharmacokinetic studies, systematic reviews, literature reviews, and studies exploring therapies other than those listed in the inclusion criteria. First, five individuals independently sorted through abstracts or articles based on the inclusion and exclusion criteria. Then, a team of 13 individuals sorted through full-text articles of selected abstracts based on the same criteria. A separate researcher resolved conflicts between the team. RESULTS: A total of 725 articles were identified from all databases, from which 256 duplicated articles were removed. Thus, a total of 469 articles abstracts were screened, of which 425 articles either did not meet the inclusion criteria or met the exclusion criteria. A total of 42 articles were retrieved and assessed for full-text review, from which 15 articles were retrieved for analysis. CONCLUSIONS: Statins may prevent primary CVEs based on their role in preventing cholesterol formation. Aspirin, canakinumab, and colchicine may be helpful in the secondary prevention of CVEs due to their blocking of various steps in the inflammation pathway leading to CVD. Future research should primarily focus on the use of canakinumab and colchicine in preventing CVD due to the limited number of studies on these drugs.

A cross-sectional analysis of Yelp and Google reviews of hospitals in the United States.

Objective: Patient satisfaction is now an important metric in emergency medicine, but the means by which satisfaction is assessed is evolving. We sought to examine hospital ratings on Google and Yelp as compared to those on Medicare's Care Compare (CC) and to determine if certain hospital characteristics are associated with crowdsourced ratings. Methods: We performed a cross-sectional analysis of hospital ratings on Google and Yelp as compared to those on CC using data collected between July 8 and August 2, 2021. For each hospital, we recorded the CC ratings, Yelp ratings, Google ratings, and each hospital's characteristics. Using multivariable linear regression, we assessed for associations between hospital characteristics and crowdsourced ratings. We calculated Spearman's correlation coefficients for CC ratings versus crowdsourced ratings. Results: Among 3000 analyzed hospitals, the median hospital ratings on Yelp and Google were 2.5 stars (interquartile ratio [IQR], 2-3) and 3 stars (IQR, 2.7-3.5), respectively. The median number of Yelp and Google reviews per hospital was 13 and 150, respectively. The correlation coefficients for Yelp and Google ratings with CC's overall star ratings were 0.19 and 0.20, respectively. For Yelp and Google ratings with CC's patient survey ratings, correlation coefficients were 0.26 and 0.22, respectively. On multivariable analysis, critical access hospitals had 0.22 (95% confidence interval [CI], 0.14-0.30) more Google stars and hospitals in the West had 0.12 (95% CI, 0.05-0.18) more Google stars than references standard hospitals. Conclusion: Patients use Google more frequently than Yelp to review hospitals. Median UnS hospital ratings on Yelp and Google are 2.5 and 3 stars, respectively. Crowdsourced reviews weakly correlate with CC ratings. Critical access hospitals and hospitals in the West have higher crowdsourced ratings.