Ismayl, Mahmoud

Abstract Background Transcatheter aortic valve replacement (TAVR) continues to be increasingly performed. While machine learning has demonstrated utility in predicting adverse outcomes in various cardiovascular procedures, its effectiveness in predicting in-hospital mortality in TAVR remains uncertain. Purpose We aimed to examine the accuracy of machine learning in predicting in-hospital mortality in TAVR. Methods We searched PubMed and Cochrane databases up to February 2024 for observational studies assessing the diagnostic accuracy of machine learning for predicting in-hospital mortality in TAVR. Extracted data included study characteristics, patient demographics, type of predictive model used, and area under the receiver operating characteristic curves (AUC). Predictive accuracy was assessed by pooling AUC of machine-learning models across studies. AUC values ranging from 0.80 to 0.90, 0.70 to 0.80, 0.60 to 0.70, and 0.50 to 0.60 corresponded to excellent, good, fair, and poor discrimination ability, respectively (1). We used a random effects model to estimate the overall effect size with a 95% confidence interval (CI). Heterogeneity was evaluated with the I2 test.We conducted subgroup analyses based on the type of machine-learning model utilized to assess for any potential differences in predictive accuracy. Results A total of three studies were identified, comprising a study population of 70,714 patients. Across studies, eight machine learning models were utilized, consisting of three artificial neural network models, one logistic regression model, one naive Bayes model, two random forest models, and one support vector machine model. Pooled AUC showed excellent accuracy of machine learning in predicting in-hospital mortality in TAVR (0.92; 95% CI 0.90 - 0.95). Subgroup analyses revealed similar yet excellent accuracy across all types of machine learning models utilized: artificial neural network (0.92; 95% CI 0.86-0.99), logistic regression (0.92; 95% CI 0.89-0.95), naive Bayes (0.90; 95% CI 0.88-0.92), random forest (0.94; 95% CI 0.87-1.00), and support vector machine (0.94; 95% CI 0.91-0.96). Conclusion Our study suggests that utilizing machine learning can accurately predict in-hospital mortality in TAVR. Operators of TAVR procedures may benefit from incorporating machine learning prediction models in their clinical practice. Further exploration of machine learning on outcomes in TAVR is needed to verify our findings.

Abstract Background Data on the relative utilization and outcomes of transcatheter aortic valve replacement (TAVR) versus surgical AVR (SAVR) in patients <65 years are limited. Purpose To compare the relative utilization and outcomes of TAVR versus SAVR in patients <65 years in the United States. Methods We queried the National Inpatient Sample (2016-2020) to identify patients <65 years hospitalized for isolated AVR. In-hospital outcomes of TAVR versus SAVR were compared using multivariable regression and propensity-matching analyses. Results Of 74,155 patients aged <65 years hospitalized for isolated AVR, 17,300 (23.3%) underwent isolated TAVR, and 56,855 (76.7%) underwent isolated SAVR. From 2016-2020, the use of TAVR increased, while the use of SAVR decreased in patients <65 years (both ptrend<0.01). Factors independently associated with TAVR selection in patients <65 years included female sex, congestive heart failure, pulmonary disease, renal failure, liver disease, cancer, and prior revascularization. Factors independently associated with SAVR selection included bicuspid aortic valve. After adjustment for baseline characteristics, TAVR was associated with lower odds of in-hospital mortality (adjusted odds ratio [aOR] 0.61, 95% confidence interval [CI] 0.38-0.97), stroke (aOR 0.58, 95% CI 0.38-0.90), acute kidney injury (aOR 0.41, 95% CI 0.34-0.49), and major bleeding (aOR 0.62, 95% CI 0.38-0.96), and higher vascular complications compared with SAVR (aOR 2.39, 95% CI 1.75-3.24). Length of stay was shorter and nonhome discharges were lower with TAVR (all p<0.01). Similar findings were observed in the propensity-matched cohort. Conclusions This nationwide observational analysis showed that TAVR is a feasible treatment option for patients <65 years with lower in-hospital mortality and resource utilization compared with SAVR.Central Illustration

Abstract Background Patients with heart failure possess an increased risk for adverse outcomes with noncardiac surgery (1). While bariatric surgery is frequently performed in patients with obesity, there remains a limited understanding of the impact of bariatric surgery in obese patients with heart failure. Purpose The purpose of this study was to compare outcomes of bariatric surgery versus nonsurgical management in obese patients with heart failure. Methods We searched PubMed, EMBASE, and the Cochrane Library, through February 2024, for studies evaluating outcomes in obese patients with heart failure who underwent bariatric surgery compared to nonsurgical management. Outcomes assessed were postoperative atrial fibrillation, all-cause mortality, and hospital readmission. We used a fixed-effect model to calculate hazard ratios (HR) with 95% confidence intervals (CI) in R studio for outcomes assessed. In outcomes where significant heterogeneity was found (P< 0.05 and I2 > 60%), a random effects model was used. Results A total of 3 studies were identified, consisting of 260,186 patients. Compared to nonsurgical management, bariatric surgery was associated with a significant reduction in postoperative atrial fibrillation (HR 0.72; 95% CI: 0.62-0.84), all-cause mortality (HR 0.50; 95%CI 0.35-0.72), and hospital readmission (HR 0.73; 95% CI 0.69-0.78) in obese patients with heart failure. Conclusion Our study suggests that bariatric surgery significantly reduces post-operative atrial fibrillation, mortality, and hospital readmission among obese patients with heart failure.Our findings demonstrate that bariatric surgery should be considered as a viable intervention in obese patients with heart failure. Further studies are warranted to confirm our findings.Forest Plots of Outcomes Assessed

Abstract Context Patients with adrenal insufficiency (AI) have both increased risk of cardiovascular disease and adverse outcomes with many medical emergencies. However, limited data exist specifically regarding ST-segment elevation myocardial infarction (STEMI) in the context of AI. Objective To evaluate associations between AI and in-hospital outcomes of patients with STEMI. Methods Admissions for STEMI were identified in the 2016-2019 National Inpatient Sample. In-hospital outcomes were compared between patients with and without AI. The primary outcome was in-hospital mortality. Secondary outcomes included percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), intervention, acute kidney injury (AKI), vasopressor use, mechanical circulatory support (MCS), mechanical ventilation, ventricular tachycardia (VT), hospital length of stay (LOS), and total charges. Multivariable regression models were used to adjust for potential confounders. Results Among 690 430 STEMI hospitalizations, 1382 (0.2%) had a diagnosis of AI. AI was associated with higher odds of in-hospital mortality (adjusted OR [aOR] 1.51, 95% CI 1.03-2.2), lower odds of PCI (aOR 0.73, 95% CI 0.55-0.98), higher odds of CABG (aOR 2.8, 95% CI 1.89-4.2) and, AKI (aOR 2.38, 95% CI 1.72-3.3), VT (aOR 1.55, 95% CI 1.1-2.2), need for vasopressors (aOR 2.34, 95% CI 1.33-4.1), mechanical ventilation (aOR 2.11, 95% CI 1.54-2.89), and MCS (aOR 2.18, 95% CI 1.57-3.03). Patients with AI also had a longer LOS (10 days vs 4.2 days, P < .001) and higher charges ($258 475 vs $115 505, P < .001). Conclusion Patients with AI admitted for STEMI had higher in-hospital mortality, nonfatal adverse outcomes, and resource utilization than patients without AI.

Abstract Aims Patients with aortic stenosis (AS) and cardiogenic shock (CS) are an extremely high-risk population with a poor prognosis in the absence of definitive therapy. We aimed to compare the outcomes of transcatheter aortic valve replacement (TAVR) with those of surgical aortic valve replacement (SAVR) in patients with AS–CS. Methods and results We queried the Nationwide Readmission Database (2016–21) to identify patients hospitalized for AS–CS who underwent isolated TAVR or SAVR. In-hospital outcomes of TAVR vs. SAVR were compared using multivariable regression and propensity-matching analyses. Ninety-day readmissions were compared using a Cox proportional hazards regression model. A total of 16 161 patients were hospitalized for AS–CS, of whom 6470 (40.0%) underwent isolated TAVR and 9691 (60.0%) underwent isolated SAVR. From 2016 through 2021, the proportion of TAVR increased from 29.5 to 46.5% and the proportion of SAVR correspondingly decreased in AS–CS (Ptrend < 0.01). After adjustment for baseline characteristics, TAVR was associated with lower odds of stroke [adjusted odds ratio (aOR) 0.63, 95% confidence interval (CI) 0.47–0.84], acute kidney injury (aOR 0.80, 95% CI 0.69–0.93), and major bleeding (aOR 0.54, 95% CI 0.40–0.71) and higher odds of vascular complications (aOR 1.56, 95% CI 1.23–1.97) compared with SAVR. In-hospital mortality, myocardial infarction, permanent pacemaker placement, and 90-day all-cause and heart failure readmissions showed similar rates between TAVR and SAVR. However, hospital length of stay was shorter and total costs and non-home discharges were lower with TAVR than with SAVR. Conclusion This nationwide observational analysis showed that TAVR is increasingly performed in patients with AS–CS and is associated with similar rates of in-hospital mortality and 90-day readmissions but lower rates of in-hospital complications and resource utilization compared with SAVR.

Abstract Background A significant proportion of patients requiring mitral valve (MV) intervention have undergone prior coronary artery bypass grafting (CABG). Reoperative heart surgery is associated with increased risk. Aims To evaluate the utilization and outcomes of transcatheter vs. surgical MV interventions in patients with prior CABG. Methods We queried the Nationwide Readmission Database (2016–21) to identify adults with prior CABG hospitalized for transcatheter or surgical MV intervention. In-hospital outcomes were compared using multivariable regression and propensity-matching analyses. Readmissions were compared using Cox proportional hazards regression model. Results Of 305 625 weighted hospitalizations for MV intervention, 23 506 (7.7%) occurred in patients with prior CABG. From 2016Q1–2021Q4, the use of transcatheter MV interventions increased among patients with prior CABG (72 to 191 for repair and 6 to 45 for replacement per 100 000 hospitalizations, both ptrend < 0.001). Compared with surgical MV repair and replacement, transcatheter MV repair and replacement were associated with similar in-hospital mortality (adjusted odds ratio [aOR] 0.44, 95% confidence interval [CI] 0.20–1.03 for repair; aOR 0.61, 95% CI 0.38–1.02 for replacement) and 180-day heart failure (HF) readmissions (adjusted hazard ratio [aHR] 1.56, 95% CI 0.85–2.87 for repair; aHR 1.15, 95% CI 0.63–2.09 for replacement) and lower stroke, acute kidney injury, permanent pacemaker placement, length of stay, and non-home discharges, respectively. Vascular complications were higher with transcatheter vs. surgical MV replacement. Conclusion Transcatheter MV interventions are increasingly used as the preferred modality of MV intervention in patients with prior CABG and are associated with similar in-hospital mortality and 180-day HF readmissions compared with surgical MV interventions.

Background: Outcomes of device-detected AF remain unclear in individuals without a prior history of AF. Methods: A meta-analysis was conducted to evaluate outcomes in individuals with no prior history of AF who experienced device-detected AF. Outcomes assessed were clinical AF, thromboembolism and all-cause mortality. A fixed-effects model was used to calculate RRs with 95% CI. Results: Compared to individuals who did not experience device-detected AF, those who did had increased risks of clinical AF (RR 3.33, 95% CI [1.99–5.57]; p<0.0001) and thromboembolic events (RR 2.21; 95% CI [1.72–2.85]; p<0.0001). The risk of all-cause mortality was similar between both groups (RR 1.19; 95% CI [0.95–1.49]; p=0.13). Subgroup analysis revealed an increased risk of thromboembolic events among device-detected AF ≥24 hours (RR 12.34; 95% CI [2.70–56.36]). Conclusion: While there is an increased risk of clinical AF and thromboembolism in individuals with device-detected AF, mortality was insignificant.

Background: Data on racial/ethnic and sex disparities in the utilization and outcomes of tricuspid valve surgery (TVS) in the United States are scarce. The authors aimed to evaluate the impact of race/ethnicity and sex on the utilization and outcomes of TVS. Methods: The authors analyzed the National Inpatient Sample database from 2016 to 2020 to identify hospitalizations for TVS. Racial/ethnic and sex disparities in TVS outcomes were determined using logistic regression models. Results: Between 2016 and 2020, 19 395 hospitalizations for TVS were identified. The utilization rate (number of surgeries/100,000 hospitalizations) was lower in Black and Hispanic patients compared with White patients for surgical tricuspid valve repair (STVr) (331 versus 493 versus 634, P<0.01) and surgical tricuspid valve replacement (STVR) (312 versus 601 versus 728, P<0.01). Similarly, the utilization rate was lower for women compared with men for STVr (1021 versus 1364, P<0.01) and STVR (930 versus 1,316, P<0.01). Compared to White men undergoing TVS, all women had lower odds of acute kidney injury [adjusted odds ratio (aOR) 0.65, 95% CI 0.55–0.78] and higher odds of blood transfusion (aOR 1.30, 95% CI 1.07–1.59), and Black men had higher odds of blood transfusion (aOR 1.59, 95% CI 1.08–2.35). In-hospital mortality and other surgical complications were similar between all groups (all P>0.05). Conclusions: Significant racial/ethnic and sex disparities exist in the utilization of TVS in the United States. Further studies are needed to understand the reasons for these disparities and to identify effective strategies for their mitigation.