Allam, Mohamed

Abstract Aims Sodium-glucose cotransporter 2 inhibitors (SGLT2-Is) improve heart failure (HF) outcomes but their effects on acute myocardial infarction (AMI) remain poorly characterized. This study aimed to evaluate the 1-year cardiovascular outcomes of SGLT2-Is among patients with AMI. Methods and results We conducted an observational, retrospective cohort study using TriNetX data, including patients aged ≥18 with AMI identified via ICD-10 codes regardless of left ventricular ejection fraction (LVEF), categorized by SGLT2-Is use. Propensity score matching (PSM) was performed to balance baseline demographics, comorbidities, and medication use. Adjusted odds ratios (aORs) were estimated for the primary outcome (recurrent AMI) and the secondary outcomes (acute HF hospitalizations, stroke, all-cause hospitalizations, all-cause mortality, new-onset atrial fibrillation, and cardiac arrest). After PSM, 89 554 patients were analysed (44 777 SGLT2-Is users; 44 777 non-users). The mean age was ∼68 years in both cohorts with a similarly high burden of cardiovascular comorbidities. Mean follow-up duration was 290.854 days for SGLT2-Is users and 284.465 days for non-users. SGLT2-Is use was linked to lower rates of recurrent AMI [aOR: 0.459; 95% confidence interval (CI): 0.367–0.551], all-cause hospitalizations (aOR: 0.782; 95% CI: 0.762–0.803), all-cause mortality (aOR: 0.640; 95% CI: 0.612–0.670), and cardiac arrest (aOR: 0.834; 95% CI: 0.773–0.900). No differences were observed in acute HF hospitalizations, new-onset atrial fibrillation, or stroke. Conclusion SGLT2-Is are associated with improved cardiovascular outcomes in patients with AMI, including reductions in recurrent AMI, all-cause hospitalizations and mortality, and cardiac arrest. These findings emphasize the need for prospective clinical trials involving patients with AMI and other cardiovascular comorbidities, regardless of LVEF, to confirm these results.

Background: Aortic stenosis (AS) imposes a significant mortality burden. Understanding demographic influences on the location of AS-related death is crucial for advancing equitable end-of-life care. Therefore, we investigated how demographic factors influence the location of death among AS patients in the United States. Methods: We completed a cross-sectional study utilizing US mortality data from the CDC’s WONDER database for 2019. All files related to decedents with AS identified as the primary cause of death were obtained, including demographic information and death locations (i.e., inpatient facilities, outpatient/ER facilities, home, or hospice/nursing facilities). Associations between demographic factors (age, sex, race/ethnicity, marital status, and education) and place of death were assessed using multivariable logistic regression models, yielding odds ratios (ORs). Results: In 2019, most AS-related deaths occurred in inpatient facilities (38.3%, n = 5062), home (29.2%, n = 3859), or hospice/nursing facilities (28.6%, n = 3775). Higher odds of inpatient death were observed among Black (OR 1.67, p < 0.001) and Hispanic individuals (OR 1.91, p < 0.001) compared to White decedents. Those aged >85 years were more likely to die at home (OR 1.76, p < 0.001) or in hospice/nursing facilities (OR 7.80, p < 0.001). Males had increased odds of inpatient death (OR 1.09, p = 0.044) but decreased odds of hospice/nursing facility death (OR 0.87, p = 0.003). Higher education levels were associated with increased odds of home death (OR 1.33, p = 0.023) and decreased odds of hospice/nursing facility death (OR 0.71, p = 0.015). Conclusions: Demographic factors significantly influence the location of death among AS patients, emphasizing the need for culturally and socioeconomically tailored interventions to promote equitable end-of-life care.

Systemic vasculitides are a rare and complex group of diseases that can affect multiple organ systems. Clinically, presentation may be vague and non-specific and as such, diagnosis and subsequent management are challenging. These entities are typically classified by the size of vessel involved, including large-vessel vasculitis (giant cell arteritis, Takayasu’s arteritis, and clinically isolated aortitis), medium-vessel vasculitis (including polyarteritis nodosa and Kawasaki disease), and small-vessel vasculitis (granulomatosis with polyangiitis and eosinophilic granulomatosis with polyangiitis). There are also other systemic vasculitides that do not fit in to these categories, such as Behcet’s disease, Cogan syndrome, and IgG4-related disease. Advances in medical imaging modalities have revolutionized the approach to diagnosis of these diseases. Specifically, color Doppler ultrasound, computed tomography and angiography, magnetic resonance imaging, positron emission tomography, or invasive catheterization as indicated have become fundamental in the work up of any patient with suspected systemic or localized vasculitis. This review presents the key diagnostic imaging modalities and their clinical utility in the evaluation of systemic vasculitis.

Background: Identifying patients at a high risk of mortality post percutaneous coronary intervention (PCI) is of vital clinical importance. We investigated the utility of machine learning algorithms to predict short and intermediate-term risk of all-cause mortality in patients undergoing PCI. Methods: Patient-level demographics, clinical, electrocardiographic ,echocardiographic and angiographic data from January 2006 to December 2017 were extracted from the Mayo Clinic CathPCI registry and clinical records. For patients with multiple PCI events, data collected at the time of the index PCI was used for analysis. Patients who underwent bailout coronary artery bypass graft surgery (CABG) prior to discharge were excluded. 306 variables were incorporated into random forest machine learning model (RF) to predict all-cause mortality at 6 months and 1 year after PCI. Ten-fold cross-validation repeated five times was used to optimize the hyperparameters and estimate its external performance. The National Cardiovascular Data Registry (NCDR) based logistic regression model was used for comparison. The area under receiver operator characteristic curves (AUC) was calculated to assess the ability of the models to predict all-cause mortality. Results: A total of 17356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery during the index hospitalization. The mean age was 66.9 ± 12.5 years;71% were male. Indications for PCI were ST-elevation myocardial infarction (9.4%), non-ST elevation myocardial infarction (12.9%), unstable angina (17.7%), and stable angina (52.8%) in the cohort. In-hospital, 6-month & 1 year mortality rates were 1.9%,4.2% & 5.8% respectively. The RF model was superior to the NCDR model in predicting inhospital, 6-month, 1 year mortality (p<0.0001) ( Figure 1 ). Conclusion: Machine learning is superior to NCDR model in predicting short and intermediate risk of all-cause mortality post PCI.

AbstractIntroductionThe right ventricle (RV) strain measured by speckle tracking (RVS) is an echocardiographic parameter used to assess RV function. We compared RVS to RV fractional area change (FAC%), tricuspid annular plane systolic excursion (TAPSE) and Doppler tissue imaging‐derived peak systolic velocity (Sʹ) in the assessment of right ventricular (RV) systolic function measured using cardiac magnetic resonance imaging (MRI).MethodsWe enrolled consecutive patients who underwent cardiac MRI between Jan 2012 and Dec 2017 and a transthoracic echocardiogram (TTE) within 1 month of the MRI with no interval event. Baseline clinical characteristics and MRI parameters were extracted from chart review. Echocardiographic parameters were measured prospectively. TTE parameters including RVS, TAPSE, Sʹ, and FAC% were tested for accuracy to identify impaired RV EF (EF < 45% & <30%) using receiver operator curves.ResultsThe study cohort included 500 patients with mean age 55 years ± 18 and peak tricuspid regurgitation velocity 2.7 ± 1.4 m/s. The area under ROC for RVS was 0.69 (95% CI 0.63–0.75) and 0.78 (95% CI 0.70–0.88) to predict RVEF < 45% & RVEF < 30%, respectively. The RV FAC% had second highest accuracy of predicting RVEF among all the TTE parameters tested in study.ConclusionRight ventricular strain is the most accurate echocardiographic method to detect impaired right ventricular systolic function when using MRI as the gold standard.

Introduction: The right ventricle (RV) strain measured by speckle tracking (RVS) is a novel method of assessing RV function. We compared RVS to RV fractional area change (FAC%), tricuspid annular peak systolic excursion (TAPSE) and Doppler tissue imaging-derived peak systolic velocity (S’) in the assessment of right ventricular (RV) systolic function measured using cardiac magnetic resonance imaging (MRI). Methods: We enrolled consecutive patients who underwent cardiac MRI between Jan 2012- Dec 2017 and a transthoracic echocardiogram (TTE) within 1 month of the MRI with no interval event. Baseline clinical characteristics and MRI parameters were extracted from chart review. Echocardiographic parameters were measured prospectively. TTE parameters including RVS, TAPSE, S’ and FAC% were tested for accuracy to identify impaired RV EF (EF <45% & <30%) using receiver operator curves. Results: The study cohort included 500 patients with mean age 55 yr ± 18 and right ventricular systolic pressure 33.7 ± 13.6 mmHg. The area under ROC for RVS was 0.69 (95% CI 0.63 – 0.75) and 0.78 (95% CI 0.70 – 0.88) to predict RVEF <45% & RVEF <30% respectively. The RV FAC % had second highest accuracy of predicting RVEF among all the TTE parameters tested in study. Conclusion: Right ventricular strain is the most accurate echocardiographic method to detect impaired right ventricular systolic function when using MRI as the gold standard.