Daily Cardiology Research Analysis

BACKGROUND: Venous thromboembolism (VTE) is a leading cardiovascular disease, yet its etiology is incompletely understood. This study used large-scale, high-throughput aptamer-based proteomics to identify new circulating protein biomarkers and biological pathways for incident VTE. METHODS: We included 4 longitudinal cohorts (the ARIC study [Atherosclerosis Risk in Communities], CHS [Cardiovascular Health Study], MESA [Multi-Ethnic Study of Atherosclerosis], and the HUNT study [Trøndelag Health]) that identified 1371 incident noncancer VTEs among 20 737 participants followed for a maximum of 10 to 29 years. We used the SomaScan to measure baseline plasma levels of ≈5000 to 7000 proteins and examined the prospective relationships between the protein biomarkers and noncancer VTE. We then conducted an external replication of top VTE proteins in 783 incident noncancer VTEs among 39 097 participants in the UKB study (UK Biobank) based on the Olink proteomics platform. We used Cox proportional hazards regression to estimate the association between each protein biomarker and VTE risk. Mendelian randomization (MR) analysis was used to assess the possible causal associations between identified proteins and VTE risk. RESULTS: There were 23 proteins that exceeded a false discovery rate-adjusted CONCLUSIONS: We identified several novel plasma proteins for VTE that reflect biological processes outside established VTE pathophysiology, including extracellular matrix regulation, immunity, immune-vascular endothelium interactions, and vascular senescence. Results may provide new modifiable targets to improve VTE risk stratification, prevention, or treatment.

AIMS: The role of electrocardiography (ECG) has been limited in the preoperative risk evaluation in noncardiac surgery due to its low prognostic value. We aimed to evaluate the utility of an AI-enabled ECG (QCG-Critical score) in predicting 30-day postoperative mortality in non-cardiac surgery and compare its performance with traditional perioperative risk-assessment tools. METHODS AND RESULTS: A retrospective cohort of 46 135 adults who underwent non-cardiac surgery at a tertiary centre between 2020 and 2021 was analysed. Preoperative ECG images acquired within 30 days before surgery were used as input to previously developed CNN-based deep-learning algorithm to generate QCG-Critical score that reflects the risk for critical illness. The primary outcome was 30-day mortality, which occurred in 0.34% of patients. Individuals with QCG-Critical scores >40 had a markedly higher mortality rate of 11.7%. The QCG-Critical score demonstrated strong predictive performance for 30-day mortality (AUROC: 0.909), outperforming the ESC surgical category (0.728) and RCRI (0.725), and was comparable to the ASA classification (0.886). The performance of QCG-Critical score remained consistent across subgroups stratified by age, sex, emergency operation, anaesthesia type, and conventional risk groups. The QCG-Critical score also demonstrated good performance for predicting 7-day mortality (AUROC: 0.933), unplanned PCI (0.857), prolonged mechanical ventilation (0.829), and presumed heart failure (0.774). CONCLUSION: The preoperative QCG-Critical score accurately predicted postoperative mortality and other adverse outcomes, outperforming conventional risk-stratification tools. The QCG-Critical score may serve as a fast, accessible, and integrable tool for perioperative risk assessments in routine surgical care.

BACKGROUND: Cardiovascular disease (CVD) prevalence is rising among younger women in the United States. Hypertensive disorders of pregnancy (HDP) are early indicators of cardiovascular risk, yet it remains unclear whether HDP independently increase CVD risk or reflect poor prepregnancy health. We aimed to quantify the association between HDP and incident CVD in a diverse, real-world population, with replication of findings across health systems. METHODS: We used data from the All of Us research program, encompassing >50 health systems across the United States, to identify women with longitudinal pregnancy and postpartum data (n=17 357; between 2007 and 2022). Multivariable Cox regression estimated adjusted hazard ratios (aHRs) of HDP with CVD (ischemic heart disease, heart failure, or stroke), overall and stratified by prepregnancy cardiometabolic risk factors (hypertension, obesity, diabetes, hyperlipidemia, or chronic kidney disease). Analyses were replicated in an independent health system (n=56 549; between 2016 and 2025) using the Observational Medical Outcomes Partnership Common Data Model. RESULTS: Participants had a median [interquartile range] age of 30 [25, 35] years; 2719 (16%) identified as Black or African American, and 7267 (42%) identified as Hispanic or Latino. Among those who reported socioeconomic data, 4306 (35%) reported an income <$25 000, and 6429 (37%) a high school education at most. HDP occurred in 2098 (12%) of pregnancies. Over a median of 4.6 years of follow-up, 701 women developed CVD. Overall, HDP were associated with elevated CVD risk (aHR, 1.82 [95% CI, 1.49-2.22]). Regardless of prepregnancy cardiometabolic risk factors, HDP were independently associated with CVD risk (aHR, 2.06 [95% CI, 1.55-2.74] among women without risk factors, and aHR, 1.33 [95% CI, 1.00-1.77] among women with risk factors). Main findings showed similar effect estimates for the risk of HDP with composite CVD (aHR, 2.62 [95% CI, 2.17-3.16]) in the external replication cohort. CONCLUSIONS: In a diverse, national sample of young US women, HDP were a significant marker of premature CVD risk, even in the absence of prepregnancy cardiometabolic risk factors. Integrating pregnancy complications into CVD risk stratification and promoting cardiometabolic health before, during, and after pregnancy may reduce the growing burden of early-onset CVD among women.