There are sex differences in presentation, treatment, and outcomes of myocardial infarction (MI) but less is known about these differences in a younger patient population. The objective of this study was to investigate sex differences among individuals who experience their first MI at a young age. Consecutive patients presenting to two large academic medical centres with a Type 1 MI at ≤50 years of age between 2000 and 2016 were included. Cause of death was adjudicated using electronic health records and death certificates. In total, 2097 individuals (404 female, 19%) had an MI (mean age 44 ± 5.1 years, 73% white). Risk factor profiles were similar between men and women, although women were more likely to have diabetes (23.7% vs. 18.9%, P = 0.028). Women were less likely to undergo invasive coronary angiography (93.5% vs. 96.7%, P = 0.003) and coronary revascularization (82.1% vs. 92.6%, P < 0.001). Women were significantly more likely to have MI with non-obstructive coronary disease on angiography (10.2% vs. 4.2%, P < 0.001). They were less likely to be discharged with aspirin (92.2% vs. 95.0%, P = 0.027), beta-blockers (86.6% vs. 90.3%, P = 0.033), angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers (53.4% vs. 63.7%, P < 0.001), and statins (82.4% vs. 88.4%, P < 0.001). There was no significant difference in in-hospital mortality; however, women who survived to hospital discharge experienced a higher all-cause mortality rate (adjusted HR = 1.63, P = 0.01; median follow-up 11.2 years) with no significant difference in cardiovascular mortality (adjusted HR = 1.14, P = 0.61). Women who experienced their first MI under the age of 50 were less likely to undergo coronary revascularization or be treated with guideline-directed medical therapies. Women who survived hospitalization experienced similar cardiovascular mortality with significantly higher all-cause mortality than men. A better understanding of the mechanisms underlying these differences is warranted.

Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such “incidental” CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.

The strong association between lipoprotein (a) [Lp(a)] and atherosclerotic cardiovascular disease has led to considerations of Lp(a) being a potential target for mitigating residual cardiovascular risk. While approximately 20 % of the population has an Lp(a) level greater than 50 mg/dL, there are no currently available pharmacological lipid-lowering therapies that have demonstrated substantial reduction in Lp(a). Novel therapies to lower Lp(a) include antisense oligonucleotides and small-interfering ribonucleic acid molecules and have shown promising results in phase 2 trials. Phase 3 trials are currently underway and will test the causal relationship between Lp(a) and ASCVD and whether lowering Lp(a) reduces cardiovascular outcomes. In this review, we summarize emerging insights related to Lp(a)’s role as a risk-enhancing factor for ASCVD, association with calcific aortic stenosis, effects of existing therapies on Lp(a) levels, and variations amongst patient populations. The evolving therapeutic landscape of emerging therapeutics is further discussed.

Objective: Accurate ascertainment of comorbidities is paramount in clinical research. While manual adjudication is labor‐intensive and expensive, the adoption of electronic health records enables computational analysis of free‐text documentation using natural language processing (NLP) tools.Hypothesis: We sought to develop highly accurate NLP modules to assess for the presence of five key cardiovascular comorbidities in a large electronic health record system.Methods: One‐thousand clinical notes were randomly selected from a cardiovascular registry at Mass General Brigham. Trained physicians manually adjudicated these notes for the following five diagnostic comorbidities: hypertension, dyslipidemia, diabetes, coronary artery disease, and stroke/transient ischemic attack. Using the open‐source Canary NLP system, five separate NLP modules were designed based on 800 “training‐set” notes and validated on 200 “test‐set” notes.Results: Across the five NLP modules, the sentence‐level and note‐level sensitivity, specificity, and positive predictive value was always greater than 85% and was most often greater than 90%. Accuracy tended to be highest for conditions with greater diagnostic clarity (e.g. diabetes and hypertension) and slightly lower for conditions whose greater diagnostic challenges (e.g. myocardial infarction and embolic stroke) may lead to less definitive documentation.Conclusion: We designed five open‐source and highly accurate NLP modules that can be used to assess for the presence of important cardiovascular comorbidities in free‐text health records. These modules have been placed in the public domain and can be used for clinical research, trial recruitment and population management at any institution as well as serve as the basis for further development of cardiovascular NLP tools.

Background: Lipoprotein (a) [Lp(a)] is a robust predictor of coronary heart disease outcomes, with targeted therapies currently under investigation. We aimed to evaluate the association of high Lp(a) with standard modifiable risk factors (SMuRFs) for incident first acute myocardial infarction (AMI). Methods and Results: This retrospective study used the Mass General Brigham Lp(a) Registry, which included patients aged ≥18 years with an Lp(a) measurement between 2000 and 2019. Exclusion criteria were severe kidney dysfunction, malignant neoplasm, and prior known atherosclerotic cardiovascular disease. Diabetes, dyslipidemia, hypertension, and smoking were considered SMuRFs. High Lp(a) was defined as >90th percentile, and low Lp(a) was defined as <50th percentile. The primary outcome was fatal or nonfatal AMI. A combination of natural language processing algorithms, International Classification of Diseases ( ICD ) codes, and laboratory data was used to identify the outcome and covariates. A total of 6238 patients met the eligibility criteria. The median age was 54 (interquartile range, 43–65) years, and 45% were women. Overall, 23.7% had no SMuRFs, and 17.8% had ≥3 SMuRFs. Over a median follow‐up of 8.8 (interquartile range, 4.2–12.8) years, the incidence of AMI increased gradually, with higher number of SMuRFs among patients with high (log‐rank P =0.031) and low Lp(a) (log‐rank P <0.001). Across all SMuRF subgroups, the incidence of AMI was significantly higher for patients with high Lp(a) versus low Lp(a). The risk of high Lp(a) was similar to having 2 SMuRFs. Following adjustment for confounders and number of SMuRFs, high Lp(a) remained significantly associated with the primary outcome (hazard ratio, 2.9 [95% CI, 2.0–4.3]; P <0.001). Conclusions: Among patients with no prior atherosclerotic cardiovascular disease, high Lp(a) is associated with significantly higher risk for first AMI regardless of the number of SMuRFs.

The ongoing Olpasiran Trials of Cardiovascular Events and Lipoprotein(a) Reduction [OCEAN(a)]-Outcomes trial is evaluating whether Lp(a) lowering can reduce the incidence of cardiovascular events among patients with prior myocardial infarction (MI) or percutaneous coronary intervention (PCI) and elevated Lp(a) (≥200 nmol/L). The purpose of this study is to evaluate the association of elevated Lp(a) with cardiovascular outcomes in an observational cohort resembling the OCEAN(a)-Outcomes trial main enrolment criteria. This study included patients aged 18–85 years with Lp(a) measured as part of their clinical care between 2000 and 2019. While patients were required to have a history of MI, or PCI, those with severe kidney dysfunction or a malignant neoplasm were excluded. Elevated Lp(a) was defined as ≥200 nmol/L consistent with the OCEAN(a)-Outcomes trial. The primary outcome was a composite of coronary heart disease death, MI, or coronary revascularization. Natural language processing algorithms, billing and ICD codes, and laboratory data were employed to identify outcomes and covariates. A total of 3142 patients met the eligibility criteria, the median age was 61 (IQR: 52–73) years, 28.6% were women, and 12.3% had elevated Lp(a). Over a median follow-up of 12.2 years (IQR: 6.2–14.3), the primary composite outcome occurred more frequently in patients with versus without elevated Lp(a) [46.0 vs. 38.0%, unadjHR = 1.30 (95% CI: 1.09–1.53), P = 0.003]. Following adjustment for measured confounders, elevated Lp(a) remained independently associated with the primary outcome [adjHR = 1.33 (95% CI: 1.12–1.58), P = 0.001]. In an observational cohort resembling the main OCEAN(a)-Outcomes Trial enrolment criteria, patients with an Lp(a) ≥200 nmol/L had a higher risk of cardiovascular outcomes.