Background: A relevant proportion of patients with suspected coronary artery disease undergo invasive coronary angiography showing normal or nonobstructive coronary arteries. However, the prevalence of coronary microvascular disease (CMD) and coronary spasm in patients with nonobstructive coronary artery disease remains to be determined. The objective of this study was to determine the prevalence of coronary CMD and coronary vasospastic angina in patients with no obstructive coronary artery disease. Methods and Results: A systematic review and meta‐analysis of studies assessing the prevalence of CMD and vasospastic angina in patients with no obstructive coronary artery disease was performed. Random‐effects models were used to determine the prevalence of these 2 disease entities. Fifty‐six studies comprising 14 427 patients were included. The pooled prevalence of CMD was 0.41 (95% CI, 0.36–0.47), epicardial vasospasm 0.40 (95% CI, 0.34–0.46) and microvascular spasm 24% (95% CI, 0.21–0.28). The prevalence of combined CMD and vasospastic angina was 0.23 (95% CI, 0.17–0.31). Female patients had a higher risk of presenting with CMD compared with male patients (risk ratio, 1.45 [95% CI, 1.11–1.90]). CMD prevalence was similar when assessed using noninvasive or invasive diagnostic methods. Conclusions: In patients with no obstructive coronary artery disease, approximately half of the cases were reported to have CMD and/or coronary spasm. CMD was more prevalent among female patients. Greater awareness among physicians of ischemia with no obstructive coronary arteries is urgently needed for accurate diagnosis and patient‐tailored management.

Background: Coronary artery disease (CAD) patterns play an essential role in the decision‐making process about revascularization. The pullback pressure gradient (PPG) quantifies CAD patterns as either focal or diffuse based on fractional flow reserve (FFR) pullbacks. The objective of this study was to evaluate the impact of CAD patterns on acute percutaneous coronary intervention (PCI) results considered surrogates of clinical outcomes. Methods and Results: This was a prospective, multicenter study of patients with hemodynamically significant CAD undergoing PCI. Motorized FFR pullbacks and optical coherence tomography (OCT) were performed before and after PCI. Post‐PCI FFR >0.90 was considered an optimal result. Focal disease was defined as PPG >0.73 (highest PPG tertile). Overall, 113 patients (116 vessels) were included. Patients with focal disease were younger than those with diffuse CAD (61.4±9.9 versus 65.1±8.7 years, P =0.042). PCI in vessels with high PPG (focal CAD) resulted in higher post‐PCI FFR (0.91±0.07 in the focal group versus 0.86±0.05 in the diffuse group, P <0.001) and larger minimal stent area (6.3±2.3 mm ² in focal versus 5.3±1.8 mm ² in diffuse CAD, P =0.015) compared withvessels with low PPG (diffuse CAD). The PPG was associated with the change in FFR after PCI ( R ² =0.51, P <0.001). The PPG significantly improved the capacity to predict optimal PCI results compared with an angiographic assessment of CAD patterns (area under the curve _PPG 0.81 [95% CI, 0.73–0.88] versus area under the curve _angio 0.51 [95% CI, 0.42–0.60]; P <0.001). Conclusions: PCI in vessels with focal disease defined by the PPG resulted in greater improvement in epicardial conductance and larger minimal stent area compared with diffuse disease. PPG, but not angiographically defined CAD patterns, distinguished patients attaining superior procedural outcomes. Registration: URL: https://clinicaltrials.gov/ct2/show/NCT03782688

In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.