Predictors and Outcomes of Sudden Cardiac Arrest in Heart Failure With Preserved Ejection Fraction: A Nationwide Inpatient Sample Analysis

Sudden cardiac arrest (SCA) is the leading cause of cardiovascular mortality in heart failure with preserved ejection fraction (HFpEF), contributing to around 25% of deaths observed in pivotal HFpEF trials. However, predictors and outcomes of in-hospital SCA in HFpEF have not been well characterized. We queried the Nationwide Inpatient Sample (2016 to 2017) to identify adult hospitalizations with a diagnosis of HFpEF. Patients with acute or chronic conditions associated with SCA (e.g., acute myocardial infarction, acute pulmonary embolism, sarcoidosis) were excluded. We ascertained whether SCA occurred during these hospitalizations, identified predictors of SCA using multivariate logistic regression, and determined outcomes of SCA in HFpEF. Of 2,909,134 hospitalizations, SCA occurred in 1.48% (43,105). The mean age of the SCA group was 72.3 ± 12.4 years, 55.8% were women, and 66.4% were White. Presence of third-degree atrioventricular block (odds ratio [OR] 5.95, 95% confidence interval [CI] 5.31 to 6.67), left bundle branch block (OR 1.96, 95% CI 1.72 to 2.25), and liver disease (OR 1.87, 95% CI 1.73 to 2.02) were the leading predictors of SCA in HFpEF. After excluding patients with do-not-resuscitate status, the SCA group versus those without SCA had higher mortality (25.9% vs 1.6%), major bleeding complications (4.1% vs 1.7%), increased use of percutaneous coronary intervention (2.5% vs 0.7%), and mechanical circulatory assist device (1.2% vs 0.1%). These observational inpatient data suggest identifiable risk factors for SCA in HFpEF including cardiac arrhythmias. Further research is warranted to identify the best tools to risk-stratify patients with HFpEF to implement targeted SCA prevention strategies

Precipitating factors for acute decompensated heart failure in patients with stable chronic left ventricular systolic dysfunction

Background: The burden of HF in India is high, with an estimated prevalence of about 5 million patients, an annual incidence of one million, an in hospital mortality as high as 30.8%, with postdischarge 6 month major adverse event and mortality rates at 39.5% and 26.3%. Acute decompensated HF is caused by a variety of precipitating factors and many are preventable. Methods: This 1 year study was a prospective study conducted on the patients admitted under a tertiary care unit in north India. Patients included in the study had chronic stable left ventricular systolic dysfunction and developed acute decompensated HF. Results: This study included 150 Patients with ADHF . Moderate to severe anemia was found to be the factor in 63.8% of the patients. New onset myocardial ischemia was the next most common factor leading to acute decompensated HF, Dietary indiscretion was seen in 45.3% of the patients. Noncompliance to drugs was also very common. The study revealed that higher rates of admissions with acute decompensated HF were seen in winters (October to December and January to March). Conclusion: Anemia and noncompliance with drugs were most common precipitating factors leading acute decompensated HF in North Indian population.Every patient needs a more intensive regular follow up and adequate diet pattern for prevention of acute decompensated HF. Systematic patient education and treatment can reduce the burden, risk of ADHF, and re hospitalization

sj-docx-1-jpc-10.1177_21501319231199014 – Supplemental material for The Use of Telemedicine to Improve Hypertension in an Urban Primary Care Clinic and Predictors of Improved Blood Pressure

Supplemental material, sj-docx-1-jpc-10.1177_21501319231199014 for The Use of Telemedicine to Improve Hypertension in an Urban Primary Care Clinic and Predictors of Improved Blood Pressure by Ajay Kerai, Namratha Meda, Khushboo Agarwal, Mohil Garg, Brototo Deb, Pooja Singh, Puneet Singla, Tareq Arar, Godwin Darko and Nnenna Oluigbo in Journal of Primary Care & Community Health</p

Comparison of endothelial shear stress between ultrathin strut Bioresorbable Polymer Drug Eluting Stent vs Durable Polymer Drug Eluting Stent post-stent implantation: An optical coherence tomography substudy from BIOFLOW II.

BACKGROUND Recent clinical data indicate a different performance of biodegradable polymer (BP)-drug eluting stent (DES) compared to durable polymer (DP)-DES. Whether this can be explained by a beneficial impact of BP-DES stent design on the local hemodynamic forces distribution remains unclear. OBJECTIVES To compare endothelial shear stress (ESS) distribution after implantation of ultrathin (us) BP-DES and DP-DES and examine the association between ESS and neointimal thickness (NIT) distribution in the two devices at 9 months follow up. METHODS AND RESULTS We retrospectively identified patients from the BIOFLOW II trial that had undergone OCT imaging. OCT data were utilized to reconstruct the surface of the stented segment at baseline and 9 months follow-up, simulate blood flow, and measure ESS and NIT in the stented segment. The patients were divided into 3 groups depending on whether DP-DES (N = 8, n = 56,160 sectors), BP-DES with a stent diameter of >3 mm (strut thickness of 80 μm, N = 6, n = 36,504 sectors), or BP-DES with a stent diameter of ≤3 mm (strut thickness of 60 μm, N = 8, n = 50,040 sectors) were used for treatment. The ESS, and NIT distribution and the association of these two variables were estimated and compared among the 3 groups. RESULTS In the DP-DES group mean NIT was 0.18 ± 0.17 mm and ESS 1.68 ± 1.66 Pa; for the BP-DES ≤3 mm group the NIT was 0.17 ± 0.11 mm and ESS 1.49 ± 1.24 Pa and for the BP-DES >3 mm group 0.20 ± 0.23 mm and 1.42 ± 1.24 Pa respectively (p < 0.001 for both NIT and ESS comparisons across groups). A negative correlation between NIT and baseline ESS was found, the correlation coefficient for all the stented segments was -0.33, p < 0.001. CONCLUSION In this OCT sub-study of the BIOFLOW II trial, the NIT was statistically different between groups of patients treated with BP-DES and DP-DES. In addition, regions of low ESS were associated with increased NIT in all studied devices

Reproducibility of an artificial intelligence optical coherence tomography software for tissue characterization: Implications for the design of longitudinal studies.

BACKGROUND To assess the reproducibility of coronary tissue characterization by an Artificial Intelligence Optical Coherence Tomography software (OctPlus, Shanghai Pulse Medical Imaging Technology Inc.). METHODS 74 patients presenting with multivessel ST-segment elevation myocardial infarction (STEMI) underwent optical coherence tomography (OCT) of the infarct-related artery at the end of primary percutaneous coronary intervention (PPCI) and during staged PCI (SPCI) within 7 days thereafter in the MATRIX (Minimizing Adverse Hemorrhagic Events by Transradial Access Site and angioX) Treatment-Duration study (ClinicalTrials.gov, NCT01433627). OCT films were run through the OctPlus software. The same region of interest between either side of the stent and the first branch was identified on OCT films for each patient at PPCI and SPCI, thus generating 94 pairs of segments. 42 pairs of segments were re-analyzed for intra-software difference. Five plaque characteristics including cholesterol crystal, fibrous tissue, calcium, lipid, and macrophage content were analyzed for various parameters (span angle, thickness, and area). RESULTS There was no statistically significant inter-catheter (between PPCI and SPCI) or intra-software difference in the mean values of all the parameters. Inter-catheter correlation for area was best seen for calcification [intraclass correlation coefficient (ICC) 0.86], followed by fibrous tissue (ICC 0.87), lipid (ICC 0.62), and macrophage (ICC 0.43). Some of the inter-catheter relative differences for area measurements were large: calcification 9.75 %; cholesterol crystal 74.10 %; fibrous tissue 5.90 %; lipid 4.66 %; and macrophage 1.23 %. By the intra-software measurements, there was an excellent correlation (ICC > 0.9) for all tissue types. The relative differences for area measurements were: calcification 0.64 %; cholesterol crystal 5.34 %; fibrous tissue 0.19 %; lipid 1.07 %; and macrophage 0.60 %. Features of vulnerable plaque, minimum fibrous cap thickness and lipid area showed acceptable reproducibility. CONCLUSION The present study demonstrates an overall good reproducibility of tissue characterization by the Artificial Intelligence Optical Coherence Tomography software. In future longitudinal studies, investigators may use discretion in selecting the imaging endpoints and sample size, accounting for the observed relative differences in this study