Outcomes in patients with aortic stenosis and severely reduced ejection fraction following surgical aortic valve replacement and transcatheter aortic valve replacement.

BACKGROUND: Patients with severe aortic stenosis (AS) and left ventricular (LV) dysfunction demonstrate improvement in left ventricular injection fraction (LVEF) after aortic valve replacement (AVR). The timing and magnitude of recovery in patients with very low LVEF (≤ 25%) in surgical or transcatheter AVR is not well studied. OBJECTIVE: Determine clinical outcomes following transcatheter aortic valve replacement (TAVR) and surgical aortic valve repair (SAVR) in the subset of patients with severely reduced EF ≤ 25%. METHODS: Single-center, retrospective study with primary endpoint of LVEF 1-week following either procedure. Secondary outcomes included 30-day mortality and delayed postprocedural LVEF. T-test was used to compare variables and linear regression was used to adjust differences among baseline variables. RESULTS: 83 patients were enrolled (TAVR = 56 and SAVR = 27). TAVR patients were older at the time of procedure (TAVR 77.29 ± 8.69 vs. SAVR 65.41 ± 10.05, p < 0.001). One week post procedure, all patients had improved LVEF after both procedures (p < 0.001). There was no significant difference in LVEF between either group (TAVR 33.5 ± 11.77 vs. SAVR 35.3 ± 13.57, p = 0.60). Average LVEF continued to rise and increased by 101% at final follow-up (41.26 ± 13.70). 30-day mortality rates in SAVR and TAVR were similar (7.4% vs. 7.1%, p = 0.91). CONCLUSION: Patients with severe AS and LVEF ≤ 25% have a significant recovery in post-procedural EF following AVR regardless of method. LVEF doubled at two years post-procedure. There was no significant difference in 30-day mortality or mean EF recovery between TAVR and SAVR. TRIAL REGISTRATION: Indiana University institutional review board granted approval for above study numbered 15,322.

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.