Citations

ABSTRACT Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model‐developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and CYP2C19 genetic information to predict 1‐year ischemic outcomes post‐PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR‐PCI trial ( n = 4572) and the Precision PCI registry ( n = 3745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1 year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR‐PCI and validated in Precision PCI using cross‐validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (> 75). ML models trained on large clinical trial and real‐world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de‐escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification. Trial Registration: TAILOR‐PCI URL: https://clinicaltrials.gov/ct2/show/NCT01742117

Abstract Aims Alcohol and its relationship with cardiovascular disease (CVD) remain contentious. Several observational studies have found cardioprotective associations, while Mendelian randomization studies have found null or harmful effects of moderate drinking. Biases, including abstainer misclassification and sick-quitter effects, may obscure true associations. It is widely understood that former drinkers are often sick quitters. However, removal of former drinkers from analyses addresses bias in the abstainer group but fails to account for induced survivorship among current drinkers. This study sought to determine whether adjusting for biases altered the association between alcohol and CVD by comparing conventional models with those using (i) occasional drinkers as the reference group instead of lifetime abstainers and (ii) ‘intention-to-treat’ reallocation of former drinkers based on past drinking patterns. Methods and results The Multi-Ethnic Study of Atherosclerosis is a prospective cohort study of 6814 participants aged 45–84 years between 2000 and 2002 without baseline CVD. A total of 6755 participants were included after excluding those with missing data. Alcohol consumption was self-reported and classified as lifetime abstainer, former, or current drinker [occasional (≤1 drink/week), light (2–7 drinks/week), moderate (8–14 drinks/week), heavy (>14 drinks/week)]. Outcomes included coronary artery calcium (CAC; 0, 1–100, 101–300, and >300) and clinical events [myocardial infarction (MI), stroke, cardiovascular mortality, composite major adverse cardiovascular events (MACEs), and heart failure]. Of 6755 participants, 20.6% were lifetime abstainers, 24.1% former drinkers, and 55.3% current drinkers. Adjusting for biases altered the observed relationship between alcohol and CVD, showing increased harm with light drinking, decreased protection with moderate drinking, and a dose–response relationship between alcohol and CAC, particularly CAC >300 in adjusted models. Protective associations between moderate drinking and MI [hazard ratio (HR): 0.64, 95% confidence interval (CI): 0.38–1.06] and cardiovascular mortality (HR: 0.81, 95% CI: 0.53–1.25) were attenuated and became non-significant. Light drinking shifted from null to increased risk for MI (HR: 1.46, 95% CI: 1.11–1.92), stroke (HR: 1.54, 95% CI: 1.17–2.03), MACE (HR: 1.33, 95% CI: 1.12–1.57), and heart failure (HR: 1.42, 95% CI: 1.10–1.84). Conclusion Associations between alcohol and CVD in observational studies are sensitive to methodologic variation. Results from models correcting for abstainer/sick-quitter biases may help explain discrepant results from previous studies.

Abstract Background Alcohol consumption and its relationship with cardiovascular disease (CVD) remains controversial. Observational studies have reported J-shaped associations suggesting cardioprotective effects of moderate drinking, while other observational and Mendelian Randomization studies have found dose-response harm. Biases such as abstainer misclassification, sick quitter effects, and residual confounding may obscure true associations. This study aimed to reassess alcohol’s impact on CVD using novel methods to minimize bias. Purpose We evaluated the relationship between alcohol and subclinical (coronary artery calcium [CAC]) and clinical (myocardial infarction [MI], stroke, cardiovascular death, major adverse cardiovascular events [MACE], and heart failure) outcomes in the Multi-Ethnic Study of Atherosclerosis (MESA). We compared conventional models with those using (1) occasional drinkers as the reference group instead of lifetime abstainers and (2) intention-to-treat (ITT) reallocation of former drinkers based on past drinking patterns. Methods MESA is a prospective cohort study of 6,814 participants aged 45-84 years without baseline CVD. Alcohol use was classified as lifetime abstainer, former drinker, or current drinker (occasional [≤1 drink/week], light [2-7 drinks/week], moderate [8-14 drinks/week], heavy [>14 drinks/week]). We used multinomial logistic regression for CAC and Cox proportional hazards models for clinical outcomes across four models: (1) non-drinker reference, (2) lifetime abstainer reference, (3) occasional drinker reference, and (4) occasional drinker reference with ITT reallocation of former drinkers to their previous drinking levels. Results Increasing alcohol consumption correlated with a dose-response increase in CAC. Heavy drinking was consistently associated with severe CAC (>300). In fully adjusted models, light (OR: 1.23, 95% CI: 1.02-1.48), moderate (OR: 1.27, 95% CI: 1.04-1.55), and heavy drinking (OR: 1.42, 95% CI: 1.12-1.78) were associated with higher CAC scores compared to occasional drinkers. Traditional models replicated protective effects of moderate drinking for MI and cardiovascular mortality. However, these associations attenuated in adjusted models. Moderate drinking showed an attenuated, non-significant benefit for MI (HR: 0.64, 95% CI: 0.38-1.06) and cardiovascular mortality (HR: 0.81, 95% CI: 0.53-1.25) in fully adjusted models. Light drinking shifted from null to increased risk for MI (HR: 1.46, 95% CI: 1.11-1.92), stroke (HR: 1.54, 95% CI: 1.17-2.03), MACE (HR: 1.33, 95% CI: 1.12-1.57), and heart failure (HR: 1.42, 95% CI: 1.10-1.84). Conclusion Adjusting for abstainer and sick quitter biases altered the observed relationship between alcohol and CVD, showing increased harm with light drinking, attenuated protective effects of moderate drinking, and a dose-response relationship for CAC in bias-adjusted models. These findings illustrate the importance of methodology in alcohol-CVD relationships.

Objective To estimate the proportion of female patients from Mayo Clinic Family Medicine and Community Internal Medicine clinics who are aware of alcohol use as a breast cancer (BC) risk factor. Patients and Methods One thousand women aged 21 to 70 completed a survey collecting information on demographics, medical/family history, frequency and quantity of alcohol consumption, and other health behaviors. Participants were also asked to grade how likely certain factors were to increase lifetime BC risk. Results Most participants (844/990 [85.3%]) identified a family history of BC as a BC risk factor. In contrast, 48.4% (475/982) identified alcohol use as a BC risk factor. The remaining 51.6% (507/982) reported either they were not sure about alcohol consumption as a risk factor (341/982 [34.7%]), believed that there was no association (143/982 [14.6%]), or believed that alcohol consumption decreases the risk of BC (23/982 [2.3%]). In multivariable analyses, factors associated with awareness of alcohol consumption and BC risk included having never been pregnant ( P = 0.048), more education ( P < 0.001), and higher frequency of alcohol consumption in the past 12 months ( P = 0.005). Conclusion Further education of patients and the public is needed to promote awareness of alcohol as a risk factor for BC.

Introduction/Objectives: Social determinants of health (SDOH) have the potential to differentially impact child developmental outcomes. This study examined whether scores on the Environmental Screening Questionnaire (ESQ), a newly developed SDOH screening tool, were associated with scores on the Brigance and Ages & Stages Questionnaires-Social-Emotional (ASQ:SE-2) child development assessments. Methods: Brigance, ASQ:SE-2, and ESQ scores from children enrolled in a Head Start Program in Northeast Iowa were collected during the 2021 to 2022 and 2022 to 2023 school years. Associations between scores in each ESQ domain and Brigance and ASQ:SE-2 scores were assessed. Results: Education-Employment and Community concerns on the ESQ were associated with reduced Brigance scores ( r = −.21, P < .001; r = −.17, P = .001). Concerns related to Housing, Child and Family Health, and Community were associated with more concerning ASQ:SE-2 scores ( r = .14, P = .005; r = .18, P < .001; r = 0.27, P < .001). In multivariable models controlling for sex and ethnicity, Education-Employment concerns were significant predictors of lower Brigance scores, while Child and Family Health and Community concerns were significant predictors of ASQ:SE-2 scores. Conclusions: ESQ scores in certain SDOH domains correlate significantly with child developmental outcomes. The ESQ domains of Child and Family Health and Community appear to be particularly important for appropriate child socio-emotional development. Interventions should focus on addressing critical SDOH domains to promote child resilience and counteract the non-medical factors that can interfere with child developmental outcomes.

Fundamental to medical education, gross anatomy first introduces medical students to human mortality, often provoking stress. Sense of purpose, a key aspect of well-being, may serve as a protective factor. We studied students’ sense of purpose during an anatomy course, examining its links to performance, spirituality, and belief in an afterlife. Fifty medical students who took a 7-week gross anatomy course in 2022 or 2023 completed the Life Engagement Test at the beginning, middle, and end of the course. We collected other data including demographics, religious/spiritual beliefs, and previous exposure to body donors. Practical and final examinations assessed course performance. Strong sense of purpose at the three measurement points was positively correlated with practical exam performance (r = 0.40, P = .004; r = 0.44, P = .003; r = 0.30, P = .047, respectively) but not National Board of Medical Examiners’ anatomy subject exam performance. Staunch believers and non-believers in an afterlife maintained a stable sense of purpose, while students uncertain about the afterlife experienced a decline from the beginning to the middle of the course and even more to the end ( P = .046; P = .008, respectively). These students also had significantly lower practical examination performance as compared to other students (95% CI [−10.84, −0.52]). In conclusion, a strong sense of purpose predicts better performance in a 7-week anatomy course. Both believers and non-believers in an afterlife do well, but students wavering in their beliefs experience a declining sense of purpose and lower grades. Supporting these students when they confront human mortality, including in other contexts such as palliative care/hospice settings, may be beneficial.

Introduction: Identifying patients at risk for ischemic events after percutaneous coronary intervention (PCI) relies on traditional analysis of limited clinical and imaging variables. Machine learning (ML) has shown promise in effectively predicting cardiovascular risk in population studies. While existing ML models mainly predict mortality and incorporate clinical variables, there is a lack of tools that have utilized genetic data and that predict ischemic events. Aims: This study aims to develop and validate a ML model incorporating genotyping and clinical data to enhance prediction of ischemic outcomes for PCI patients utilizing large prospectively derived diverse datasets. Methods: Patients from the TAILOR-PCI trial (n=5302) were utilized for model development. 50% of the sample was utilized for Boruta feature selection and 50% for training and testing using cross validation. Features included demographics, medical history, medications, PCI characteristics, and genetic data (specifically, CYP2C19 *2, *3, *17 alleles). The primary endpoint was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. Multiple ML classification algorithms, including Support Vector Machine (SVM) polynomial, Random Forest, Light gradient boost, XG Boost, among others, were benchmarked for their prediction performance on rare events. The top performing classifiers were externally validated on an independent dataset from the PRECISION PCI study (n=3,745). Results: Mean participant age of the training set was 64.2 ± 11.0 years, with 75.4% being male. During follow-up of 12 months, among 4,572 patients in the entire cohort 343 (7.5%) met the primary outcome. The SVM polynomial model demonstrated the highest area under the curve (AUC) of 0.67 for predicting the primary outcome with test dataset. The sensitivity, specificity, precision, and recall were 0.87, 0.28, 0.07, and 0.87 respectively (Figure). Peripheral arterial disease, body mass index, and age were among the top variables by feature importance. Conclusion: ML models incorporating both clinical and genetic data are feasible and highly promising in predicting major adverse cardiac events that may help guide use of anti-platelet drug therapy. The AUC values are reasonable given imbalances and misclassifications in datasets, and further model optimization with prospective utilization of the model will be paramount.