Immediate Versus Staged Complete Revascularization in Patients With Acute Coronary Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

A strategy of complete revascularization (CR) is recommended in patients with acute coronary syndrome (ACS) and multivessel disease (MVD). However, the optimal timing of CR remains equivocal. We searched MEDLINE, Embase, the Cochrane Library, and ClinicalTrials.gov for randomized controlled trials (RCTs) comparing immediate CR (ICR) with staged CR in patients with ACS and MVD. Our primary outcomes were all-cause and cardiovascular mortality. All outcomes were assessed at 3 time points: in-hospital or at 30 days, at 6 months to 1 year, and at >1 year. Data were pooled in RevMan 5.4 using risk ratios as the effect measure. A total of 9 RCTs (7,506 patients) were included in our review. A total of 7 trials enrolled patients with ST-segment elevation myocardial infarction (STEMI), 1 enrolled patients with non-STEMI only, and 1 enrolled patients with all types of ACS. There was no difference between ICR and staged CR regarding all-cause and cardiovascular mortality at any time window. ICR reduced the rate of myocardial infarction and decreased the rate of repeat revascularization at 6 months and beyond. The rates of cerebrovascular events and stent thrombosis were similar between the 2 groups. In conclusion, the present meta-analysis demonstrated a lower rate of myocardial infarction and a reduction in repeat revascularization at and after 6 months with ICR strategy in patients with mainly STEMI and MVD. The 2 groups had no difference in the risk of all-cause and cardiovascular mortality. Further RCTs are needed to provide more definitive conclusions and investigate CR strategies in other ACS.

Early vs. Late Transcatheter Aortic Valve Replacement in Acute Heart Failure Hospitalizations: A Comparative Nationwide Analysis.

BACKGROUND: Severe aortic stenosis (AS) is the most common valvular disease in the USA. Patients undergoing urgent or emergent transcatheter aortic valve replacement (TAVR) have worse clinical outcomes than those undergoing non-urgent procedures. No studies have examined the impact of procedural TAVR timing on outcomes in AS complicated by acute heart failure (AHF). AIMS: We aimed to evaluate differences in in-hospital mortality and clinical outcomes between early (<48 hours) vs. late (≥48 hours) TAVR in patients hospitalized with AHF using a real-world US database. METHODS: We queried the National Inpatient Sample database to identify hospitalizations with a diagnosis of AHF, aortic valve disease, and a TAVR procedure (2015-2020). The associations between TAVR timing and clinical outcomes were examined using logistic regression model. RESULTS: A total of 25,290 weighted AHF hospitalizations were identified, of which 6,855 patients (27.1%) underwent early TAVR, and 18,435 (72.9%) late TAVR. Late TAVR patients had higher in-hospital mortality rate (2.2% vs. 2.8%, p<0.01) on unadjusted analysis but no significant difference following adjustment for demographic, clinical, and hospital characteristics [aOR 1.00 (0.82-1.23)]. Late TAVR was associated with higher odds of cardiac arrest (aOR 1.50, 95% CI: 1.18-1.90) and use of mechanical circulatory support (aOR 2.05, 95% CI: 1.68-2.51). Late TAVR was associated with longer hospital stay (11 days vs. 4 days, p<0.01) and higher costs ($72,851 vs. $53,209, p<0.01). CONCLUSION: Early TAVR was conducted in approximately 25% of the AS patients admitted with AHF, showing improved in-hospital outcomes before adjustment, with no significant differences observed after adjustment.

Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices.

Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.

Development and Validation of a Machine Learning Score for Readmissions After Transcatheter Aortic Valve Implantation.

Background: Identifying predictors of readmissions after transcatheter aortic valve implantation (TAVI) is an important unmet need. Objectives: We sought to explore the role of machine learning (ML) in predicting readmissions after TAVI. Methods: We included patients who underwent TAVI between 2016 and 2019 in the Nationwide Readmission Database. A total of 917 candidate predictors representing all International Classification of Diseases, Tenth Revision, diagnosis and procedure codes were included. First, we used lasso regression to remove noninformative variables and rank informative ones. Next, we used an unsupervised ML model (K-means) to identify patterns/clusters in the data. Furthermore, we used Light Gradient Boosting Machine and Shapley Additive exPlanations to specify the impact of individual predictors. Finally, we built a parsimonious model to predict 30-day readmission. Results: A total of 117,398 and 93,800 index TAVI hospitalizations were included in the 30- and 90-day analyses, respectively. Lasso regression identified 138 and 199 informative predictors for the 30- and 90-day readmission, respectively. Next, K-means recognized 2 distinct clusters: low risk and high risk. In the 30-day cohort, the readmission rate was 10.1% in the low risk group and 23.3% in the high risk group. In the 90-day cohort, the rates were 17.4% and 35.3%, respectively. The top predictors were the length of stay, frailty score, total discharge diagnoses, acute kidney injury, and Elixhauser score. These predictors were incorporated into a risk score (TAVI readmission score), which exhibited good performance in an external validation cohort (area under the curve 0.74 [0.7-0.78]). Conclusions: ML methods can leverage widely available administrative databases to identify patients at risk for readmission after TAVI, which could inform and improve post-TAVI care.