Search

Background: This study aimed to evaluate the performance of a modified U.S. (MUS) model for risk prediction of cardiovascular (CV) events in Asian patients and compare it to European and Japanese models. Methods: The MUS model, based on the US ACC/AHA 2018 lipid treatment guideline, was employed to stratify patients under primary or secondary prevention. Two multi-center prospective observational registry cohorts, T-SPARCLE and T-PPARCLE, were used to validate the scoring system, and the primary outcome was the time to first occurrence/recurrence of major adverse cardiac events (MACEs). The MUS model's performance was compared to other models from Europe and Japan. Results: A total of 10,733 patients with the mean age of 64.2 (SD: 11.9) and 36.5% female were followed up for a median of 5.4 years. The MUS model was validated, with an AUC score of 0.73 (95% CI 0.68–0.78). The European and Japanese models had AUC scores ranging from 0.6 to 0.7. The MUS model categorized patients into four distinct CV risk groups, with hazard ratios (HRs) as follows: very high- vs. high-risk group (HR = 1.91, 95% CI 1.53–2.39), high- vs. moderate-risk group (HR = 2.08, 95% CI 1.60–2.69), and moderate- vs. low-risk group (HR = 3.14, 95% CI 1.63–6.03). After adjusting for the MUS model, a history of atherosclerotic vascular disease (ASCVD) was not a significant predictor of adverse cardiovascular outcomes within each risk group. Conclusion: The MUS model is an effective tool for risk stratification in Asian patients with and without ASCVD, accurately predicting MACEs and performing comparably or better than other established risk models. Our findings suggest that patient management should focus on background risk factors instead of solely on primary or secondary prevention.

Potent P2Y12 receptor antagonists have been used widely for patients undergoing percutaneous coronary intervention with different results. Benefits from different regimens various between trials. Randomized controlled trials (RCTs) have restrictive inclusion and exclusion criteria; thus, they may limit the generalizability of the findings to a broader population. This study was aimed to comprehensively investigate the outcomes of potent P2Y12 inhibitors in patients undergoing PCI, including RCTs and real-world evidence (RWE) studies.Multiple electronic databases were systemically reviewed and searched on compared potent P2Y12 inhibitors with clopidogrel. The primary efficacy end point was composite ischemic cardiovascular event and primary safety endpoint was major bleeding. Overall estimates of proportions and incidence rates with 95 % confidence intervals (CI) were calculated using fixed-effects models. Total 24 studies (140,986 patients) underwent coronary intervention were included in this meta-analysis, including 18 RCTs and 6 large cohort studies with propensity score matching. The potent P2Y12 inhibitors including cangrelor, prasugrel, and ticagrelor, significantly decreased the risk of composite adverse cardiovascular ischemic events (95 % CI 0.89–0.96, p

With the rapid development of information technology, digital health technologies have become increasingly prevalent in the field of healthcare. In this study, business intelligence (BI) techniques were combined with research-based prediction models to increase the efficiency and quality of healthcare practices. A data scenario involving 200 older adults with various measurements, including health beliefs, social support, self-efficacy, and disease duration, was used to establish a medication adherence prediction model in a BI system. A regression model, logistic regression model, tree model, and score-based prediction model were used to predict medication adherence among older adults. The developed BI-based prediction model has visualization, real-time feedback, and data updating functionality. These features enhanced the effectiveness of prediction models in clinical practice. Healthcare professionals can incorporate the proposed system into their care practice for health assessments and management, and patients can use the system to manage themselves. The developed BI-based care system can also be used to achieve effective communication and shared decision-making between care managers and patients. Further empirical studies integrating prediction models into the proposed BI system for assessment, management, and decision-making in healthcare practice are warranted.