Daily Cardiology Research Analysis

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, and accurately predicting individual risk is critical for prevention. Here we aimed to integrate unmodifiable risk factors, such as age and genetics, with modifiable risk factors, such as clinical and biometric measurements, into a meta-prediction framework that produces actionable and personalized risk estimates. In the initial development of the model, ~2,000 predictive features were considered, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: first, a prevalent CAD cohort used to train predictive models for cross-sectional prediction at baseline and prospective estimation of contributing risk factor levels and diagnoses (baseline models) and, second, an incident CAD cohort using, in part, these baseline models as meta-features to train a final CAD incident risk prediction model. The resultant 10-year incident CAD risk model, composed of 15 derived meta-features with multiple embedded polygenic risk scores, achieves an area under the curve of 0.84. In an independent test cohort from the All of Us research program, this model achieved an area under the curve of 0.81 for predicting 10-year incident CAD risk, outperforming standard clinical scores and previously developed integrative models. Moreover, this framework enables the generation of individualized risk reduction profiles by quantifying the potential impact of standard clinical interventions. Notably, genetic risk influences the extent to which these interventions reduce overall CAD risk, allowing for tailored prevention strategies.

BACKGROUND: Combination lipid-lowering therapy (LLT) after myocardial infarction (MI) achieves lower low-density lipoprotein cholesterol (LDL-C) levels and better cardiovascular outcomes vs statin monotherapy. As a result, global guidelines recommend lower LDL-C but, paradoxically, advise treatment through a stepwise approach. Yet the need for combination therapy is inevitable as <20% of patients achieve goals with statins alone. Whether combining ezetimibe with a statin early vs late after MI results in better outcomes is unknown. OBJECTIVES: In this study, the authors sought to assess the impact of delayed treatment escalation on outcomes by comparing early vs late oral combination LLT (statins plus ezetimibe) in patients with MI. METHODS: LLT-naïve patients (SWEDEHEART registry) hospitalized for MI (2015-2022) and discharged on statins were included. Using clone-censor-weight and Cox proportional hazards models, we compared differences in risks of MACE (death, MI, stroke), components of MACE, and cardiovascular death between patients with ezetimibe added to statins ≤12 weeks after discharge as reference (early combination therapy), from 13 weeks to 16 months (late combination therapy), or not at all. RESULTS: Of 35,826 patients (median age 65.1 years, 26.0% women), 6,040 (16.9%) received ezetimibe early, 6,495 (18.1%) ezetimibe late, and 23,291 (65.0%) received no ezetimibe. High-intensity statin use was ≥98% in all groups. Over a median 3.96 years (Q1-Q3: 2.15-5.81 years), 2,570 patients had MACE (440 cardiovascular deaths). One-year MACE incidences were 1.79 (early), 2.58 (late), and 4.03 (none) per 100 patient-years. Compared with early combination therapy, weighted risk differences in MACE for late combination therapy at 1, 2, and 3 years were 0.6% (95% CI: 0.1%-1.1%; P < 0.01), 1.1% (95% CI: 0.3%-2.0%; P < 0.01), and 0.7% (95% CI: -0.2% to 1.3%; P = 0.18), and 3-year HR was 1.14 (95% CI: 0.95-1.41). For those receiving no ezetimibe, risk differences were 0.7% (95% CI: 0.2%-1.3%), 1.6% (95% CI: 0.8%-2.5%), and 1.9% (95% CI: 0.8%-3.1%; P for all <0.01; 3-year HR: 1.29 [95% CI: 1.12-1.55]). Similar differences in risk of cardiovascular death at 3 years were observed (HRs vs early: late: 1.64 [95% CI: 1.15-2.63]; none: 1.83 [95% CI: 1.35-2.69]). CONCLUSIONS: MI care pathways should implement early combination therapy with statins and ezetimibe as standard care, because delaying use of combination LLT or using high-intensity statin monotherapy is associated with avoidable harm.

AIMS: Artificial intelligence (AI) has enabled accurate and fast plaque quantification from coronary computed tomography angiography (CCTA). However, AI detects any coronary plaque in up to 97% of patients. To avoid overdiagnosis, a plaque burden safety cut-off for future coronary events is needed. METHODS AND RESULTS: Percent atheroma volume (PAV) was quantified with AI-guided quantitative computed tomography in a blinded fashion. Safety cut-off derivation was performed in the Turku CCTA registry (Finland), and pre-defined as ≥90% sensitivity for acute coronary syndrome (ACS). External validation was performed in the Amsterdam CCTA registry (the Netherlands). In the derivation cohort, 100/2271 (4.4%) patients experienced ACS (median follow-up 6.9 years). A threshold of PAV ≥ 2.6% was derived with 90.0% sensitivity and negative predictive value (NPV) of 99.0%. In the validation cohort 27/568 (4.8%) experienced ACS (median follow-up 6.7 years) with PAV ≥ 2.6% showing 92.6% sensitivity and 99.0% NPV for ACS. In the derivation cohort, 45.2% of patients had PAV < 2.6 vs. 4.3% with PAV 0% (no plaque) (P < 0.001) (validation cohort: 34.3% PAV < 2.6 vs. 2.6% PAV 0%; P < 0.001). Patients with PAV ≥ 2.6% had higher adjusted ACS rates in the derivation [Hazard ratio (HR) 4.65, 95% confidence interval (CI) 2.33-9.28, P < 0.001] and validation cohort (HR 7.31, 95% CI 1.62-33.08, P = 0.010), respectively. CONCLUSION: This study suggests that PAV up to 2.6% quantified by AI is associated with low-ACS risk in two independent patient cohorts. This cut-off may be helpful for clinical application of AI-guided CCTA analysis, which detects any plaque in up to 96-97% of patients.