Epicardial Adipose Tissue and Invasive Hemodynamics in Heart Failure With Preserved Ejection Fraction

Objectives: This study examined associations between epicardial adipose tissue (EAT), invasive hemodynamics, and exercise capacity in patients with heart failure with preserved ejection fraction (HFpEF). Background: EAT is increased in patients with HFpEF and may play a role in the pathophysiology of this disorder. Methods: Patients with heart failure and a left ventricular ejection fraction >45% who underwent right and left heart catheterization with simultaneous echocardiography were included. Pulmonary capillary wedge pressure (PCWP), left ventricular end-diastolic pressure (LVEDP), right ventricular end-diastolic pressure (RVEDP), and pulmonary vascular resistance (PVR) were invasively measured. Obesity was defined as body mass index (BMI) ≥30 kg/m2. EAT thickness alongside the right ventricle was measured on echocardiographic long- and short-axis views. Cardiopulmonary exercise testing was performed to obtain maximal oxygen uptake (VO2-max). Results: This study examined 75 patients, mean age 74 ± 9 years; 68% were women, mean BMI was 29 ± 6 kg/m2, and 36% were obese. Higher BMI was strongly associated with increased EAT (r = 0.74; p < 0.001). Increased EAT was associated with higher RVEDP, independent of PVR (odds ratio [OR]: 1.16; 95% confidence interval [CI]: 1.02 to 1.34; p = 0.03), but not independent of obesity (p = 0.10). Increased EAT and higher RVEDP were both associated with lower VO2-max (r = −0.43; p < 0.001 and r = −0.43; p = 0.001, respectively). Increased EAT remained associated with lower VO2-max after adjustment for PVR (OR: 0.64; 95% CI: 0.49 to 0.84; p = 0.002) and obesity (OR: 0.69; 95% CI: 0.53 to 0.92; p = 0.01). EAT thickness was not associated with left-sided filling pressures (i.e., PCWP and LVEDP). Conclusions: In HFpEF, obesity and increased EAT were associated with higher right-sided filling pressures and with reduced exercise capacity

Impact of Thoracoscopic Pulmonary Vein Isolation on Right Ventricular Function:A Pilot Study

Objective. Thoracoscopic surgical pulmonary vein isolation (sPVI) has been added to the treatment of atrial fibrillation (AF), showing excellent efficacy outcomes. However, data on right ventricular (RV) function following sPVI has never been studied. Our aim was to investigate RV function following sPVI and compare it to patients who underwent endocardial cryoballoon PVI. Methods. 25 patients underwent sPVI and were pair-matched according to age, sex, and AF type with 21 patients who underwent cryoballoon PVI. RV function was measured using tricuspid annular plane systolic excursion (TAPSE) and RV strain with 2D speckle tracking. Echocardiography was performed at baseline and at median 6-month follow-up. Results. Age was 54 ± 9 years and 84% were male; AF was paroxysmal in 92%. In the sPVI group, TAPSE was reduced with 31% at follow-up echocardiography (p<0.001) and RV strain showed a 25% reduction compared to baseline (p=0.018). In the control group, TAPSE and RV strain did not change significantly (−3% and +13%, p=0.410 and p=0.148). Change in TAPSE and RV strain was significantly different between groups (p≤0.001 and p=0.005). Conclusions. This study shows that RV function is significantly decreased following sPVI. This effect was not observed in the cryoballoon PVI control group

Effects of Eprosartan on Diastolic Function and Neurohormones in Patients with Hypertension and Diastolic Dysfunction

To compare the effects of an angiotensin receptor blocker(ARB)-based regimen versus a non-ARB based regimen on diastolic function and neurohormones in patients with hypertension and diastolic dysfunction. 97 patients with a systolic blood pressure (SBP) a parts per thousand yen140 mmHg, a left ventricular ejection fraction > 0.50, and echocardiographic evidence of diastolic dysfunction were randomly assignment to open-label treatment with eprosartan (with other anti-hypertensives; n = 47) or other anti-hypertensives alone (n = 50). Echocardiography, including tissue Doppler imaging (TDI), and neurohormones were done at baseline and after 6 months. Mean age was 65 (+/- 10) years and 64% was female. During 6 months of treatment, SBP decreased from 157 +/- 16 to 145 +/- 18 mmHg in the eprosartan group and from 158 +/- 17 to 141 +/- 18 mmHg in the control group (both p <0.001; p = ns between groups). Diastolic function was unaffected in both groups and there was no correlation between changes in SBP and changes in mean TDI (r = -0.06; p = 0.58). Aldosterone levels decreased in the eprosartan group, but other neurohormones remained largely unchanged. Change in SBP was however related to the change in NT-proBNP (r = 0.26; p = 0.019). Lowering blood pressure, either with eprosartan or other anti-hypertensives in hypertensive patients with diastolic dysfunction did not change diastolic function after 6 months of treatment, but was associated with a decrease of NT-proBNP

Myocardial dysfunction in long-term breast cancer survivors treated at ages 40-50years

AimsAnthracyclines increase heart failure (HF) risk, but the long-term prevalence of myocardial dysfunction in young breast cancer (BC) survivors is unknown. Early measures of left ventricular myocardial dysfunction are needed to identify BC patients at risk of symptomatic HF. Methods and resultsWithin an established cohort, we studied markers for myocardial dysfunction among 569 women, who were 5-7years (n = 277) or 10-12years (n = 292) after BC treatment at ages 40-50years. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) were assessed by echocardiography. N-terminal pro-brain natriuretic peptide (NT-proBNP) was measured in serum. Associations between patient-related and treatment-related risk factors and myocardial dysfunction were evaluated using linear and logistic regression. Median ages at BC diagnosis and cardiac assessment were 46.7 and 55.5years, respectively. Anthracycline-treated patients (n = 313), compared to the no-anthracycline group (n = 256), more often had decreased LVEF (10% vs. 4%), impaired GLS (34% vs. 27%) and elevated NT-proBNP (23% vs. 8%). GLS and LVEF declined in a linear fashion with increasing cumulative anthracycline dose (GLS: +0.23 and LVEF: -0.40 per cycle of 60mg/m(2); P125ng/L was highest for patients who received 241-300mg/m(2) anthracycline dose compared to the no-anthracycline group (odds ratio: 3.30, 95% confidence interval: 1.83-5.96). ConclusionImpaired GLS and increased NT-proBNP levels are present in a substantial proportion of young BC survivors treated with anthracyclines. Whether this will lead to future cardiac disease needs to be evaluated by longitudinal assessment

Feasibility of cardiac output measurements in critically ill patients by medical students

Background: Critical care ultrasonography (CCUS) is increasingly applied also in the intensive care unit (ICU) and performed by non-experts, including even medical students. There is limited data on the training efforts necessary for novices to attain images of sufficient quality. There is no data on medical students performing CCUS for the measurement of cardiac output (CO), a hemodynamic variable of importance for daily critical care. Objective: The aim of this study was to explore the agreement of cardiac output measurements as well as the quality of images obtained by medical students in critically ill patients compared to the measurements obtained by experts in these images. Methods: In a prospective observational cohort study, all acutely admitted adults with an expected ICU stay over 24 h were included. CCUS was performed by students within 24 h of admission. CCUS included the images required to measure the CO, i.e., the left ventricular outflow tract (LVOT) diameter and the velocity time integral (VTI) in the LVOT. Echocardiography experts were involved in the evaluation of the quality of images obtained and the quality of the CO measurements. Results: There was an opportunity for a CCUS attempt in 1155 of the 1212 eligible patients (95%) and in 1075 of the 1212 patients (89%) CCUS examination was performed by medical students. In 871 out of 1075 patients (81%) medical students measured CO. Experts measured CO in 783 patients (73%). In 760 patients (71%) CO was measured by both which allowed for comparison; bias of CO was 0.0 L min−1 with limits of agreement of − 2.6 L min−1 to 2.7 L min−1. The percentage error was 50%, reflecting poor agreement of the CO measurement by students compared with the experts CO measurement. Conclusions: Medical students seem capable of obtaining sufficient quality CCUS images for CO measurement in the majority of critically ill patients. Measurements of CO by medical students, however, had poor agreement with expert measurements. Experts remain indispensable for reliable CO measurements. Trial registration Clinicaltrials.gov; http://www.clinicaltrials.gov; registration number NCT02912624

Right Heart Dysfunction in Heart Failure with Preserved Ejection Fraction:the Impact of Atrial Fibrillation

BACKGROUND: Right ventricular (RV) dysfunction and atrial fibrillation (AF) frequently coexist in heart failure with preserved ejection fraction (HFpEF). The mechanisms underlying the association between AF and RV dysfunction are incompletely understood. METHODS AND RESULTS: 102 patients were identified. RV function was assessed using multiple echocardiographic parameters and dysfunction was present if ≥2 parameters were below the recommended cutoff. RV function, right atrial (RA) reservoir strain and RA emptying fraction, were compared between AF and sinus rhythm. 91 patients with sufficient echocardiographic quality were included: 45 (50%) had no history of AF; 14 (15%) had prior AF while in sinus rhythm; 32 (35%) had current AF. The prevalence of RV dysfunction varied across subgroups never AF, prior AF and current AF (20%, 43% and 63%, respectively, p=0.001). AF was associated with RV dysfunction (OR 4.70 [1.82-12.1], p=0.001) - independent of pulmonary pressures. In patients in sinus rhythm with prior AF, RA emptying fraction was lower compared to patients without AF history (41 vs. 60%, p=0.002). Prior AF was also associated with reduced RA reservoir strain (OR 4.57 [1.05-19.9], p=0.04) - independent of RV end-diastolic pressure. CONCLUSIONS: Atrial fibrillation is strongly related to reduced RV and RA function in HFpEF, independent of pulmonary pressures

Long-term survivors of early breast cancer treated with chemotherapy are characterized by a pro-inflammatory biomarker profile compared to matched controls

Background: Chemo- and radiotherapy for breast cancer (BC) can lead to cardiotoxicity even years after the initial treatment. The pathophysiology behind these late cardiac effects is poorly understood. Therefore, we studied a large panel of biomarkers from different pathophysiological domains in long-term BC survivors, and compared these to matched controls. Methods and results: In total 91 biomarkers were measured in 688 subjects: 342 BC survivors stratified either to treatment with chemotherapy ± radiotherapy (n = 170) or radiotherapy alone (n = 172) and matched controls. Mean age was 59 ± 9 years and 65 ± 8 years for women treated with chemotherapy ± radiotherapy and radiotherapy alone, respectively, with a mean time since treatment of 11 ± 5.5 years. No biomarkers were differentially expressed in survivors treated with radiotherapy alone vs. controls (P for all >0.1). In sharp contrast, a total of 19 biomarkers were elevated, relative to controls, in BC survivors treated with chemotherapy ± radiotherapy after correction for multiple comparisons (P <0.05 for all). Network analysis revealed upregulation of pathways relating to collagen degradation and activation of matrix metalloproteinases. Furthermore, several inflammatory biomarkers including growth differentiation factor 15, monocyte chemoattractant protein 1, chemokine (C-X-C motif) ligand 16, tumour necrosis factor super family member 13b and proprotein convertase subtilisin/kexin type 9, elevated in survivors treated with chemotherapy, showed an independent association with lower left ventricular ejection fraction. Conclusion: Breast cancer survivors treated with chemotherapy ± radiotherapy show a distinct biomarker profile associated with mild cardiac dysfunction even 10 years after treatment. These results suggest that an ongoing pro-inflammatory state and activation of matrix metalloproteinases following initial treatment with chemotherapy might play a role in the observed cardiac dysfunction in late BC survivors

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Agreement of 2D transthoracic echocardiography with cardiovascular magnetic resonance imaging after ST-elevation myocardial infarction

Background: This study was designed to investigate the agreement of 2D transthoracic echocardiography (2D TTE) with cardiovascular magnetic resonance imaging (CMR) in a contemporary population of ST-elevation myocardial infarction (STEMI) patients. Methods: In this subanalysis of the GIPS-III trial, a randomized controlled trial investigating the administration of metformin in STEMI patients to prevent reperfusion injury, we studied 259 patients who underwent same-day CMR and 2D TTE assessments four months after hospitalization for a first STEMI. Bland-Altman analyses were performed to assess agreement between LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV ejection fraction (LVEF), and LV mass measurements. Sensitivity and specificity of 2D TTE to detect categories of LVEF (≤35%, 35–50%, ≥50%) was determined. Linear regression of absolute differences in measurements between imaging modalities was used to investigate whether patient characteristics impact measurement bias. Results: Pairwise difference (bias) and 95% limits of agreement between CMR and 2D TTE measurements were +84 (37, 147) ml for LVEDV, +39 (6, 85) ml for LVESV, -1.1 ± 13.5% for LVEF, and -75 (-154, -14) g for LV mass. Sensitivity and specificity of 2D TTE to detect subjects with moderately depressed LVEF (35–50%) as measured by CMR were 52% and 88% respectively. We observed a significant effect of enzymatic infarct size on bias between 2D TTE and CMR in measuring LVESV and LVEF (P = 0.029, P = 0.001 respectively), of age and sex on bias between 2D TTE and CMR in measuring LV mass (P = 0.027, P < 0.001) and LVEDV (P = 0.001, P = 0.039), and of heart rate on bias between 2D TTE and CMR in LV volume measurements (P = 0.004, P = 0.016). Conclusions: Wide limits of agreement, underestimation of LV volumes and overestimation of LV mass was observed when comparing 2D TTE to CMR. Enzymatic infarct size, age, sex, and heart rate are potential sources of bias between imaging modalities