Association Between Frailty and Management and Outcomes of Acute Myocardial Infarction Complicated by Cardiogenic Shock.

Background: Cardiogenic shock (CS) in the setting of acute myocardial infarction (AMI) is associated with high morbidity and mortality. Frailty is a common comorbidity in patients with cardiovascular disease and is also associated with adverse outcomes. The impact of preexisting frailty at the time of CS diagnosis following AMI has not been studied. Objectives: The purpose of this study was to examine the prevalence of frailty in patients admitted with AMI complicated by CS (AMI-CS) hospitalizations and its associations with in-hospital outcomes. Methods: We retrospectively analyzed the National Inpatient Sample from 2016 to 2020 and identified all hospitalizations for AMI-CS. We classified them into frail and nonfrail groups according to the hospital frailty risk score cut-off of 5 and compared in-hospital outcomes. Results: A total of 283,700 hospitalizations for AMI-CS were identified. Most (70.8%) occurred in the frail. Those with frailty had higher odds of in-hospital mortality (adjusted OR [aOR]: 2.17, 95% CI: 2.07 to 2.26, P < 0.001), do-not-resuscitate status, and discharge to a skilled nursing facility compared with those without frailty. They also had higher odds of in-hospital adverse events, including intracranial hemorrhage, gastrointestinal hemorrhage, acute kidney injury, and delirium. Importantly, AMI-CS hospitalizations in the frail had lower odds of coronary revascularization (aOR: 0.55, 95% CI: 0.53-0.58, P < 0.001) or mechanical circulatory support (aOR: 0.89, 95% CI: 0.85-0.93, P < 0.001). Lastly, hospitalizations for AMI-CS showed an overall increase from 53,210 in 2016 to 57,065 in 2020 (P trend <0.001), with this trend driven by a rise in the frail. Conclusions: A high proportion of hospitalizations for AMI-CS had concomitant frailty. Hospitalizations with AMI-CS and frailty had higher rates of in-hospital morbidity and mortality compared to those without frailty.

Association of cardiovascular diseases with cognitive performance in older adults.

BACKGROUND: Cognitive function and cardiovascular disease (CVD) have a bidirectional relationship, but studies on the impact of CVD subtypes and aging spectrum have been scarce. METHODS: We assessed older adults aged ≥60 years from the 2011 to 2012 and 2013 to 2014 cycles of the National Health and Nutrition Examination Survey who had coronary heart disease, angina, prior myocardial infarction, congestive heart failure, or prior stroke. We compared CERAD-IR, CERAD-DR, Animal Fluency test, and DSST scores to assess cognitive performance in older adults with and without CVD. RESULTS: We included 3,131 older adults, representing 55,479,673 older adults at the national level. Older adults with CVD had lower CERAD-IR (mean difference 1.8, 95% CI 1.4-2.1, P < .001), CERAD-DR (mean difference 0.8, 95% CI 0.6-1.0, P < .001), Animal Fluency test (mean difference 2.1, 95% CI 1.6-2.6, P < .001), and DSST (mean difference 9.5, 95% CI 8.0-10.9, P < .001) scores compared with those without CVD. After adjustment, no difference in CERAD-IR, CERAD-DR, and Animal Fluency test scores was observed, but DSST scores were lower in older adults with CVD (adjusted mean difference 2.9, 95% CI 1.1-4.7, P = .001). Across CVD subtypes, individuals with congestive heart failure had lower performance on the DSST score. The oldest-old cohort of patients ≥80 years old with CVD had lower performance than those without CVD on both the DSST and Animal Fluency test. CONCLUSION: Older adults with CVD had lower cognitive performance as measured than those free of CVD, driven by pronounced differences among those with CHF and those ≥80 years old with CVD.

The association of timing of coronary artery bypass grafting for non-ST-elevation myocardial infarction and clinical outcomes in the contemporary United States.

BACKGROUND: In contrast to the timing of coronary angiography and percutaneous coronary intervention, the optimal timing of coronary artery bypass grafting (CABG) in non-ST-elevation myocardial infarction (NSTEMI) has not been determined. Therefore, we compared in-hospital outcomes according to different time intervals to CABG surgery in a contemporary NSTEMI population in the USA. METHODS: We identified all NSTEMI hospitalizations from 2016 to 2020 where revascularization was performed with CABG. We excluded NSTEMI with high-risk features using prespecified criteria. CABG was stratified into ≤24 h, 24-72 h, 72-120 h, and >120 h from admission. Outcomes of interest included in-hospital mortality, perioperative complications, length of stay (LOS), and hospital cost. RESULTS: A total of 147 170 NSTEMI hospitalizations where CABG was performed were assessed. A greater percentage of females, Blacks, and Hispanics experienced delays to CABG surgery. No difference in in-hospital mortality was observed, but CABG at 72-120 h and at >120 h was associated with higher odds of non-home discharge and acute kidney injury compared with CABG at ≤24 h from admission. In addition to these differences, CABG at >120 h was associated with higher odds of gastrointestinal hemorrhage and need for blood transfusion. All 3 groups with CABG delayed >24 h had longer LOS and hospital-associated costs compared with hospitalizations where CABG was performed at ≤24 h. CONCLUSION: CABG delays in patients with NSTEMI are more frequently experienced by women and minority populations and are associated with an increased burden of complications and healthcare cost.

Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.

Background: Hypertrophic cardiomyopathy (HCM) affects 1 in every 200 individuals and is the leading cause of sudden cardiac death in young adults. HCM can be identified using an electrocardiogram (ECG) raw voltage data and deep learning approaches, but their point-of-care application is limited by the inaccessibility of these signal data. We developed a deep learning-based approach that overcomes this limitation and detects HCM from images of 12-lead ECGs across layouts. Methods: We identified ECGs from patients with HCM features present on cardiac magnetic resonance imaging (CMR) or those within 30 days of an echocardiogram documenting thickened interventricular septum (end-diastolic interventricular septum thickness > 15mm). Patients with CMR-confirmed HCM were considered as cases during the final model evaluation. The model was validated within clinical settings at YNHH and externally on ECG images from the prospective, population-based UK Biobank cohort. We localized class-discriminating signals in ECG images using gradient-weighted class activation mapping. Results: Overall, 124,553 ECGs from 66,987 individuals (HCM cases and controls) were used for model development. The model demonstrated high discrimination for HCM across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROC] 0.96) and external sets of ECG images from UK Biobank (AUROC 0.94). A positive screen for HCM was associated with a 100-fold higher odds of CMR-confirmed HCM (OR 102.4, 95% Confidence Interval, 57.4 - 182.6) in the held-out set. Class-discriminative patterns localized to the anterior and lateral leads (V4-V5). Conclusions: We developed and externally validated a deep learning model that identifies HCM from ECG images with excellent discrimination. This approach represents an automated, efficient, and accessible screening strategy for HCM.