Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.

Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduced ejection fraction (HFrEF); however, many candidates for CRT based on clinical guidelines do not have a favorable response. A better way to identify patients expected to benefit from CRT that applies machine learning to accessible and cost-effective diagnostic tools such as the 12-lead electrocardiogram (ECG) could have a major impact on clinical care in HFrEF by helping providers personalize treatment strategies and avoid delays in initiation of other potentially beneficial treatments. This study addresses this need by demonstrating that a novel approach to ECG waveform analysis using functional principal component decomposition (FPCD) performs better than measures that require manual ECG analysis with the human eye and also at least as well as a previously validated but more expensive approach based on cardiac magnetic resonance (CMR). Analyses are based on five-fold cross validation of areas under the curve (AUCs) for CRT response and survival time after the CRT implant using Cox proportional hazards regression with stratification of groups using a Gaussian mixture model approach. Furthermore, FPCD and CMR predictors are shown to be independent, which demonstrates that the FPCD electrical findings and the CMR mechanical findings together provide a synergistic model for response and survival after CRT. In summary, this study provides a highly effective approach to prognostication after CRT in HFrEF using an accessible and inexpensive diagnostic test with a major expected impact on personalization of therapies.

Change in Systemic Arterial Pulsatility Index (SAPi) during Heart Failure Hospitalization is Associated with Improved Outcomes.

Study Objective: To identify Change in Systemic Arterial Pulsatitlity index (ΔSAPi) as a novel hemodynamic marker associated with outcomes in heart failure (HF). Design: The ESCAPE trial was a randomized controlled trial. Setting: The ESCAPE trial was conducted at 26 sites. Participants: 134 patients were analyzed (mean age 56.8 ± 13.4 years, 29% female). Interventions: We evaluated the change in SAPi, ([systemic pulse pressure/pulmonary artery wedge pressure) obtained at baseline and at the final hemodynamic measurement in the ESCAPE trial. Main Outcome Measures: Change in SAPi, (ΔSAPi), was analyzed for the primary outcomes of death, heart transplant, left ventricular assist device (DTxLVAD) or hospitalization, (DTxLVADHF) and secondary outcome of DTxLVAD using Cox proportional hazards regression. Results: Median change in SAPi was 0.81 (IQR 0.20-1.68). ΔSAPi in uppermost quartile was associated with reductions in DTxLVADHF (HR 0.55 [95% CI 0.32, 0.93]). ΔSAPi in the uppermost and lowermost quartiles combined was similarly associated with significant reductions in DTxLVADHF (HR 0.62 [95% CI 0.41, 0.94]). ΔSAPi higher than 1.17 was associated with improved DTxLVADHF. ΔSAPi was also associated with troponin levels at discharge (regression coefficient p = 0.001) and trended with 6-minute walk at discharge (Spearman correlation r = 0.179, p = 0.058). Conclusion: ΔSAPi was strongly associated with improved HF clinical profile and adverse outcomes. These findings support further exploration of Δ SAPi in the risk stratification of HF.

Cardiac Magnetic Resonance, Electromechanical Activation, Kidney Function, and Natriuretic Peptides in Cardiac Resynchronization Therapy Upgrades.

As the mechanism for worse prognosis after cardiac resynchronization therapy (CRT) upgrades in heart failure patients with RVP dependence (RVP-HF) has clinical implications for patient selection and CRT implementation approaches, this study's objective was to evaluate prognostic implications of cardiac magnetic resonance (CMR) findings and clinical factors in 102 HF patients (23.5% female, median age 66.5 years old, median follow-up 4.8 years) with and without RVP dependence undergoing upgrade and de novo CRT implants. Compared with other CRT groups, RVP-HF patients had decreased survival (p = 0.02), more anterior late-activated LV pacing sites (p = 0.002) by CMR, more atrial fibrillation (p = 0.0006), and higher creatinine (0.002). CMR activation timing at the LV pacing site predicted post-CRT LV functional improvement (p < 0.05), and mechanical activation onset < 34 ms by CMR at the LVP site was associated with decreased post-CRT survival in a model with higher pre-CRT creatinine and B-type natriuretic peptide (AUC 0.89; p < 0.0001); however, only the higher pre-CRT creatinine partially mediated (37%) the decreased survival in RVP-HF patients. In conclusion, RVP-HF had a distinct CMR phenotype, which has important implications for the selection of LV pacing sites in CRT upgrades, and only chronic kidney disease mediated the decreased survival after CRT in RVP-HF.

Impact of a Center of Excellence in Confirming or Excluding a Diagnosis of Hypertrophic Cardiomyopathy.

Tertiary hospitals with expertise in hypertrophic cardiomyopathy (HCM) are assuming a greater role in confirming and correcting HCM diagnoses at referring centers. The objectives were to establish the frequency of alternate diagnoses from referring centers and identify predictors of accuracy of an HCM diagnosis from the referring centers. Imaging findings from echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging (CMR) in 210 patients referred to an HCM Center of Excellence between September 2020 and October 2022 were reviewed. Clinical and imaging characteristics from pre-referral studies were used to construct a model for predictors of ruling out HCM or confirming the diagnosis using machine learning methods (least absolute shrinkage and selection operator logistic regression). Alternative diagnoses were found in 38 of the 210 patients (18.1%) (median age 60 years, 50% female). A total of 17 of the 38 patients (44.7%) underwent a new CMR after their initial visit, and 14 of 38 patients (36.8%) underwent review of a previous CMR. Increased left ventricular end-diastolic volume, indexed, greater septal thickness measurements, greater left atrial size, asymmetric hypertrophy on echocardiography, and the presence of an implantable cardioverter-defibrillator were associated with higher odds ratios for confirming a diagnosis of HCM, whereas increasing age and the presence of diabetes were more predictive of rejecting a diagnosis of HCM (area under the curve 0.902, p <0.0001). In conclusion, >1 in 6 patients with presumed HCM were found to have an alternate diagnosis after review at an HCM Center of Excellence, and both clinical findings and imaging parameters predicted an alternate diagnosis.

Noninvasive Electrical Mapping Compared with the Paced QRS Complex for Optimizing CRT Programmed Settings and Predicting Multidimensional Response.

The aim was to test the hypothesis that left ventricular (LV) and right ventricular (RV) activation from body surface electrical mapping (CardioInsight 252-electrode vest, Medtronic) identifies optimal cardiac resynchronization therapy (CRT) pacing strategies and outcomes in 30 patients. The LV80, RV80, and BIV80 were defined as the times to 80% LV, RV, or biventricular electrical activation. Smaller differences in the LV80 and RV80 (|LV80-RV80|) with synchronized LV pacing predicted better LV function post-CRT (p = 0.0004) than the LV-paced QRS duration (p = 0.32). Likewise, a lower RV80 was associated with a better pre-CRT RV ejection fraction by CMR (r = - 0.40, p = 0.04) and predicted post-CRT improvements in myocardial oxygen uptake (p = 0.01) better than the biventricular-paced QRS (p = 0.38), while a lower LV80 with BIV pacing predicted lower post-CRT B-type natriuretic peptide (BNP) (p = 0.02). RV pacing improved LV function with smaller |LV80-RV80| (p = 0.009). In conclusion, 3-D electrical mapping predicted favorable post-CRT outcomes and informed effective pacing strategies.

StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE.

Purpose: To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI. Materials and Methods: In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models. Results: The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc. Conclusion: StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.

First-in-human noninvasive left ventricular ultrasound pacing: A potential screening tool for cardiac resynchronization therapy.

Background: A screening tool to predict response to cardiac resynchronization therapy (CRT) could improve patient selection and outcomes. Objective: The purpose of this study was to investigate the feasibility and safety of noninvasive CRT via transcutaneous ultrasonic left ventricular (LV) pacing applied as a screening test before CRT implants. Methods: P-wave-triggered ultrasound stimuli were delivered during bolus dosing of an echocardiographic contrast agent to simulate CRT noninvasively. Ultrasound pacing was delivered at a variety of LV locations with a range of atrioventricular delays to achieve fusion with intrinsic ventricular activation. Three-dimensional cardiac activation maps were acquired via the Medtronic CardioInsight 252-electrode mapping vest during baseline, ultrasound pacing, and after CRT implantation. A separate control group received only the CRT implants. Results: Ultrasound pacing was achieved in 10 patients with a mean of 81.2 ± 50.8 ultrasound paced beats per patient and up to 20 consecutive beats of ultrasound pacing. QRS width at baseline (168.2 ± 17.8 ms) decreased significantly to 117.3 ± 21.5 ms (P <.001) in the best ultrasound paced beat and to 125.8 ± 13.3 ms (P <.001) in the best CRT beat. Electrical activation patterns were similar between CRT pacing and ultrasound pacing with stimulation from the same area of the LV. Troponin results were similar between the ultrasound pacing and the control groups (P = .96), confirming safety. Conclusion: Noninvasive ultrasound pacing before CRT is safe and feasible, and it estimates the degree of electrical resynchronization achievable with CRT. Further study of this promising technique to guide CRT patient selection is warranted.

Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning.

PURPOSE: To introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoes (DENSE) and to use the model to develop a deep convolutional neural network to aid in first-order respiratory motion compensation for self-navigated free-breathing cine DENSE of the heart. METHODS: The motion model includes conventional position shifts of magnetization and further describes the phase shift of the stimulated echo due to breathing. These image-domain effects correspond to linear and constant phase errors, respectively, in k-space. The model was validated using phantom experiments and Bloch-equation simulations and was used along with the simulation of respiratory motion to generate synthetic images with phase-shift artifacts to train a U-Net, DENSE-RESP-NET, to perform motion correction. DENSE-RESP-NET-corrected self-navigated free-breathing DENSE was evaluated in human subjects through comparisons with signal averaging, uncorrected self-navigated free-breathing DENSE, and breath-hold DENSE. RESULTS: Phantom experiments and Bloch-equation simulations showed that breathing-induced constant phase errors in segmented DENSE leads to signal loss in magnitude images and phase corruption in phase images of the stimulated echo, and that these artifacts can be corrected using the known respiratory motion and the model. For self-navigated free-breathing DENSE where the respiratory motion is not known, DENSE-RESP-NET corrected the signal loss and phase-corruption artifacts and provided reliable strain measurements for systolic and diastolic parameters. CONCLUSION: DENSE-RESP-NET is an effective method to correct for breathing-associated constant phase errors. DENSE-RESP-NET used in concert with self-navigation methods provides reliable free-breathing DENSE myocardial strain measurement.

Academic cardiac electrophysiologists' perspectives on sleep apnea care.

PURPOSE: Obstructive sleep apnea syndrome (OSAS) is an important, modifiable risk factor in the pathophysiology of arrhythmias including atrial fibrillation (AF). The purpose of the study was to evaluate cardiac electrophysiologists' (EPs) perception of OSAS. METHODS: We designed a 27-item online Likert scale-based survey instrument entailing several domains: (1) relevance of OSAS in EP practice, (2) OSAS screening and diagnosis, (3) perception on treatments for OSAS, (4) opinion on the OSAS care model. The survey was distributed to 89 academic EP programs in the USA and Canada. While the survey instrument questions refer to the term sleep apnea (SA), our discussion of the diagnosis, management, and research on the sleep disorder is more accurately described with the term OSAS. RESULTS: A total of 105 cardiac electrophysiologists from 49 institutions responded over a 9-month period. The majority of respondents agreed that sleep apnea (SA) is a major concern in their practice (94%). However, 42% reported insufficient education on SA during training. Many (58%) agreed that they would be comfortable managing SA themselves with proper training and education and 66% agreed cardiac electrophysiologists should become more involved in management. Half of EPs (53%) were not satisfied with the sleep specialist referral process. Additionally, a majority (86%) agreed that trained advanced practice providers should be able to assess and manage SA. Time constraints, lack of knowledge, and the referral process are identified as major barriers to EPs becoming more involved in SA care. CONCLUSIONS: We found that OSAS is widely recognized as a major concern for EP. However, incorporation of OSAS care in training and routine practice lags. Barriers to increased involvement include time constraints and education. This study can serve as an impetus for innovation in the cardiology OSAS care model.

Systemic arterial pulsatility (SAPi) in advanced heart failure: a novel hemodynamic risk stratification tool.

Systemic arterial pulsatility index (SAPi) is a novel hemodynamic marker for ventriculo-arterial coupling (VAC), as it integrates the contractile properties of the left ventricle with the aortic impendence. SAPi can identify heart failure patients at increased risk for adverse events. Systemic pulsatility decreases as heart failure progresses, and there is a decrease in pulse pressure accompanied by an increase in left ventricular filling pressure. Decreasing SAPi is associated with worse prognosis in advanced heart failure patients.

JOINT DEEP LEARNING FOR IMPROVED MYOCARDIAL SCAR DETECTION FROM CARDIAC MRI.

Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect. This paper presents a novel joint deep learning (JDL) framework that improves such tasks by utilizing simultaneously learned myocardium segmentations to eliminate negative effects from non-region-of-interest areas. In contrast to previous approaches treating scar detection and myocardium segmentation as separate or parallel tasks, our proposed method introduces a message passing module where the information of myocardium segmentation is directly passed to guide scar detectors. This newly designed network will efficiently exploit joint information from the two related tasks and use all available sources of myocardium segmentation to benefit scar identification. We demonstrate the effectiveness of JDL on LGE-CMR images for automated left ventricular (LV) scar detection, with great potential to improve risk prediction in patients with both ischemic and non-ischemic heart disease and to improve response rates to cardiac resynchronization therapy (CRT) for heart failure patients. Experimental results show that our proposed approach outperforms multiple state-of-the-art methods, including commonly used two-step segmentation-classification networks, and multitask learning schemes where subtasks are indirectly interacted.

Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance.

Background: Cardiac resynchronization therapy (CRT) response is complex, and better approaches are required to predict survival and need for advanced therapies. Objective: The objective was to use machine learning to characterize multidimensional CRT response and its relationship with long-term survival. Methods: Associations of 39 baseline features (including cardiac magnetic resonance [CMR] findings and clinical parameters such as glomerular filtration rate [GFR]) with a multidimensional CRT response vector (consisting of post-CRT left ventricular end-systolic volume index [LVESVI] fractional change, post-CRT B-type natriuretic peptide, and change in peak VO2) were evaluated. Machine learning generated response clusters, and cross-validation assessed associations of clusters with 4-year survival. Results: Among 200 patients (median age 67.4 years, 27.0% women) with CRT and CMR, associations with more than 1 response parameter were noted for the CMR CURE-SVD dyssynchrony parameter (associated with post-CRT brain natriuretic peptide [BNP] and LVESVI fractional change) and GFR (associated with peak VO2 and post-CRT BNP). Machine learning defined 3 response clusters: cluster 1 (n = 123, 90.2% survival [best]), cluster 2 (n = 45, 60.0% survival [intermediate]), and cluster 3 (n = 32, 34.4% survival [worst]). Adding the 6-month response cluster to baseline features improved the area under the receiver operating characteristic curve for 4-year survival from 0.78 to 0.86 (P = .02). A web-based application was developed for cluster determination in future patients. Conclusion: Machine learning characterizes distinct CRT response clusters influenced by CMR features, kidney function, and other factors. These clusters have a strong and additive influence on long-term survival relative to baseline features.

Cardiac magnetic resonance defines mechanisms of sex-based differences in outcomes following cardiac resynchronization therapy.

Background: Mechanisms of sex-based differences in outcomes following cardiac resynchronization therapy (CRT) are poorly understood. Objective: To use cardiac magnetic resonance (CMR) to define mechanisms of sex-based differences in outcomes after CRT and describe distinct CMR-based phenotypes of CRT candidates based on sex and non-ischemic/ischemic cardiomyopathy type. Materials and methods: In a prospective study, sex-based differences in three short-term CRT response measures [fractional change in left ventricular end-systolic volume index 6 months after CRT (LVESVI-FC), B-type natriuretic peptide (BNP) 6 months after CRT, change in peak VO2 6 months after CRT], and long-term survival were evaluated with respect to 39 baseline parameters from CMR, exercise testing, laboratory testing, electrocardiograms, comorbid conditions, and other sources. CMR was also used to quantify the degree of left-ventricular mechanical dyssynchrony by deriving the circumferential uniformity ratio estimate (CURE-SVD) parameter from displacement encoding with stimulated echoes (DENSE) strain imaging. Statistical methods included multivariable linear regression with evaluation of interaction effects associated with sex and cardiomyopathy type (ischemic and non-ischemic cardiomyopathy) and survival analysis. Results: Among 200 patients, the 54 female patients (27%) pre-CRT had a smaller CMR-based LVEDVI (p = 0.04), more mechanical dyssynchrony based on the validated CMR CURE-SVD parameter (p = 0.04), a lower frequency of both late gadolinium enhancement (LGE) and ischemic cardiomyopathy (p < 0.0001), a greater RVEF (p = 0.02), and a greater frequency of LBBB (p = 0.01). After categorization of patients into four groups based on cardiomyopathy type (ischemic/non-ischemic cardiomyopathy) and sex, female patients with non-ischemic cardiomyopathy had the lowest CURE-SVD (p = 0.003), the lowest pre-CRT BNP levels (p = 0.01), the lowest post-CRT BNP levels (p = 0.05), and the most favorable LVESVI-FC (p = 0.001). Overall, female patients had better 3-year survival before adjustment for cardiomyopathy type (p = 0.007, HR = 0.45) and after adjustment for cardiomyopathy type (p = 0.009, HR = 0.67). Conclusion: CMR identifies distinct phenotypes of female CRT patients with non-ischemic and ischemic cardiomyopathy relative to male patients stratified by cardiomyopathy type. The more favorable short-term response and long-term survival outcomes in female heart failure patients with CRT were associated with lower indexed CMR-based LV volumes, decreased presence of scar associated with prior myocardial infarction and ICM, and greater CMR-based dyssynchrony with the CURE-SVD.

The impact of COVID-19 on clinical outcomes among acute myocardial infarction patients undergoing early invasive treatment strategy.

BACKGROUND: The implications of coronavirus disease 2019 (COVID-19) infection on outcomes after invasive therapeutic strategies among patients presenting with acute myocardial infarction (AMI) are not well studied. HYPOTHESIS: To assess the outcomes of COVID-19 patients presenting with AMI undergoing an early invasive treatment strategy. METHODS: This study was a cross-sectional, retrospective analysis of the National COVID Cohort Collaborative database including all patients presenting with a recorded diagnosis of AMI (ST-elevation myocardial infarction (MI) and non-ST elevation MI). COVID-19 positive patients with AMI were stratified into one of four groups: (1a) patients who had a coronary angiogram with percutaneous coronary intervention (PCI) within 3 days of their AMI; (1b) PCI within 3 days of AMI with coronary artery bypass graft (CABG) within 30 days; (2a) coronary angiogram without PCI and without CABG within 30 days; and (2b) coronary angiogram with CABG within 30 days. The main outcomes were respiratory failure, cardiogenic shock, prolonged length of stay, rehospitalization, and death. RESULTS: There were 10 506 COVID-19 positive patients with a diagnosis of AMI. COVID-19 positive patients with PCI had 8.2 times higher odds of respiratory failure than COVID-19 negative patients (p = .001). The odds of prolonged length of stay were 1.7 times higher in COVID-19 patients who underwent PCI (p = .024) and 1.9 times higher in patients who underwent coronary angiogram followed by CABG (p = .001). CONCLUSION: These data demonstrate that COVID-19 positive patients with AMI undergoing early invasive coronary angiography had worse outcomes than COVID-19 negative patients.

A systemic congestive index (systemic pulse pressure to central venous pressure ratio) predicts adverse outcomes in patients undergoing valvular heart surgery.

BACKGROUND AND AIMS: Invasive hemodynamics may provide a more nuanced assessment of cardiac function and risk phenotyping in patients undergoing cardiac surgery. The systemic pulse pressure (SPP) to central venous pressure (CVP) ratio represents an integrated index of right and left ventricular function and thus may demonstrate an association with valvular heart surgery outcomes. This study hypothesized that a low SPP/CVP ratio would be associated with mortality in valvular surgery patients. METHODS: This retrospective cohort study examined adult valvular surgery patients with preoperative right heart catheterization from 2007 through 2016 at a single tertiary medical center (n = 215). Associations between the SPP/CVP ratio and mortality were investigated with univariate and multivariate analyses. RESULTS: Among 215 patients (age 69.7 ± 12.4 years; 55.8% male), 61 died (28.4%) over a median follow-up of 5.9 years. A SPP/CVP ratio <7.6 was associated with increased mortality (relative risk 1.70, 95% confidence interval [CI] 1.08-2.67, p = .019) and increased length of stay (11.56 ± 13.73 days vs. 7.93 ± 4.92 days, p = .016). It remained an independent predictor of mortality (adjusted odds ratio 3.99, 95% CI 1.47-11.45, p = .008) after adjusting for CVP, mean pulmonary artery pressure, aortic stenosis, tricuspid regurgitation, smoking status, diabetes mellitus, dialysis, and cross-clamp time. CONCLUSIONS: A low SPP/CVP ratio was associated with worse outcomes in patients undergoing valvular heart surgery. This metric has potential utility in preoperative risk stratification to guide patient selection, prognosis, and surgical outcomes.

Survival Probability and Survival Benefit Associated With Primary Prevention Implantable Cardioverter-Defibrillator Generator Changes.

Background As patients derive variable benefit from generator changes (GCs) of implantable cardioverter-defibrillators (ICDs) with an original primary prevention (PP) indication, better predictors of outcomes are needed. Methods and Results In the National Cardiovascular Data Registry ICD Registry, patients undergoing GCs of initial non-cardiac resynchronization therapy PP ICDs in 2012 to 2016, predictors of post-GC survival and survival benefit versus control heart failure patients without ICDs were assessed. These included predicted annual mortality based on the Seattle Heart Failure Model, left ventricular ejection fraction (LVEF) >35%, and the probability that a patient's death would be arrhythmic (proportional risk of arrhythmic death [PRAD]). In 40 933 patients undergoing GCs of initial noncardiac resynchronization therapy PP ICDs (age 67.7±12.0 years, 24.5% women, 34.1% with LVEF >35%), Seattle Heart Failure Model-predicted annual mortality had the greatest effect size for decreased post-GC survival (P<0.0001). Patients undergoing GCs of initial noncardiac resynchronization therapy PP ICDs with LVEF >35% had a lower Seattle Heart Failure Model-adjusted survival versus 23 472 control heart failure patients without ICDs (model interaction hazard ratio, 1.21 [95% CI, 1.11-1.31]). In patients undergoing GCs of initial noncardiac resynchonization therapy PP ICDs with LVEF ≤35%, the model indicated worse survival versus controls in the 21% of patients with a PRAD <43% and improved survival in the 10% with PRAD >65%. The association of the PRAD with survival benefit or harm was similar in patients with or without pre-GC ICD therapies. Conclusions Patients who received replacement of an ICD originally implanted for primary prevention and had at the time of GC either LVEF >35% alone or both LVEF ≤35% and PRAD <43% had worse survival versus controls without ICDs.

Systemic arterial pulsatility index (SAPi) predicts adverse outcomes in advanced heart failure patients.

Ventriculo-arterial (VA) coupling has been shown to have physiologic importance in heart failure (HF). We hypothesized that the systemic arterial pulsatility index (SAPi), a measure that integrates pulse pressure and a proxy for left ventricular end-diastolic pressure, would be associated with adverse outcomes in advanced HF. We evaluated the SAPi ([systemic systolic blood pressure-systemic diastolic blood pressure]/pulmonary artery wedge pressure) obtained from the final hemodynamic measurement in patients randomized to therapy guided by a pulmonary arterial catheter (PAC) and with complete data in the Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness (ESCAPE) trial. Cox proportional hazards regression was performed for the outcomes of (a) death, transplant, left ventricular assist device (DTxLVAD) or hospitalization, (DTxLVADHF) and (b) DTxLVAD. Among 142 patients (mean age 56.8 ± 13.3 years, 30.3% female), the median SAPi was 2.57 (IQR 1.63-3.45). Increasing SAPi was associated with significant reductions in DTxLVAD (HR 0.60 per unit increase in SAPi, 95% CI 0.44-0.84) and DTxLVADHF (HR 0.81 per unit increase, 95% CI 0.70-0.95). Patients with a SAPi ≤ 2.57 had a marked increase in both outcomes, including more than twice the risk of DTxLVAD (HR 2.19, 95% CI 1.11-4.30) over 6 months. Among advanced heart failure patients with invasive hemodynamic monitoring in the ESCAPE trial, SAPi was strongly associated with adverse clinical outcomes. These findings support further investigation of the SAPi to guide treatment and prognosis in HF undergoing invasive hemodynamic monitoring.

A Slice-Low-Rank Plus Sparse (slice-L + S) Reconstruction Method for k-t Undersampled Multiband First-Pass Myocardial Perfusion MRI.

PURPOSE: The synergistic use of k-t undersampling and multiband (MB) imaging has the potential to provide extended slice coverage and high spatial resolution for first-pass perfusion MRI. The low-rank plus sparse (L + S) model has shown excellent performance for accelerating single-band (SB) perfusion MRI. METHODS: A MB data consistency method employing ESPIRiT maps and through-plane coil information was developed. This data consistency method was combined with the temporal L + S constraint to form the slice-L + S method. Slice-L + S was compared to SB L + S and the sequential operations of split slice-GRAPPA and SB L + S (seq-SG-L + S) using synthetic data formed from multislice SB images. Prospectively k-t undersampled MB data were also acquired and reconstructed using seq-SG-L + S and slice-L + S. RESULTS: Using synthetic data with total acceleration rates of 6-12, slice-L + S outperformed SB L + S and seq-SG-L + S (N = 7 subjects) with respect to normalized RMSE and the structural similarity index (P < 0.05 for both). For the specific case with MB factor = 3 and rate 3 undersampling, or for SB imaging with rate 9 undersampling (N = 7 subjects), the normalized RMSE values were 0.037 ± 0.007, 0.042 ± 0.005, and 0.031 ± 0.004; and the structural similarity index values were 0.88 ± 0.03, 0.85 ± 0.03, and 0.89 ± 0.02 for SB L + S, seq-SG-L + S, and slice-L + S, respectively (P < 0.05 for both). For prospectively undersampled MB data, slice-L + S provided better image quality than seq-SG-L + S for rate 6 (N = 7) and rate 9 acceleration (N = 7) as scored by blinded experts. CONCLUSION: Slice-L + S outperformed SB-L + S and seq-SG-L + S and provides 9 slice coverage of the left ventricle with a spatial resolution of 1.5 mm × 1.5 mm with good image quality.

Association of colchicine use for acute gout with clinical outcomes in acute decompensated heart failure.

BACKGROUND: Gout is a common comorbidity in heart failure (HF) patients and is frequently associated with acute exacerbations during treatment for decompensated HF. Although colchicine is often used to manage acute gout in HF patients, its impact on clinical outcomes when used during acute decompensated HF is unknown. METHODS: This was a single center, retrospective study of hospitalized patients treated for an acute HF exacerbation with and without acute gout flare between March 2011 and December 2020. We assessed clinical outcomes in patients treated with colchicine for a gout flare compared to those who did not experience a gout flare or receive colchicine. The primary outcome was in-hospital all-cause mortality. RESULTS: Among 1047 patient encounters for acute HF during the study period, there were 237 encounters (22.7%) where the patient also received colchicine for acute gout during admission. In-hospital all-cause mortality was significantly reduced in the colchicine group compared with the control group (2.1% vs. 6.5%, p = .009). The colchicine group had increased length of stay (9.93 vs. 7.96 days, p < .001) but no significant difference in 30-day readmissions (21.5% vs. 19.5%, p = .495). In a Cox proportional hazards model adjusted for age, inpatient colchicine use was associated with improved survival to discharge (hazards ratio [HR] 0.163, 95% confidence interval [CI] 0.051-0.525, p = .002) and a reduced rate of in-hospital CV mortality (HR 0.184, 95% CI 0.044-0.770, p = .021). CONCLUSION: Among patients with a HF exacerbation, treatment with colchicine for a gout flare was associated with significantly lower in-hospital mortality compared with those not treated for acute gout.