Right ventricular insertion site fibrosis in a dilated cardiomyopathy referral population: phenotypic associations and value for the prediction of heart failure admission or death

Background Dilated cardiomyopathy (DCM) is increasingly recognized as a heterogenous disease with distinct phenotypes on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging. While mid-wall striae (MWS) fibrosis is a widely recognized phenotypic risk marker, other fibrosis patterns are prevalent but poorly defined. Right ventricular (RV) insertion (RVI) site fibrosis is commonly seen, but without objective criteria has been considered a non-specific finding. In this study we developed objective criteria for RVI fibrosis and studied its clinical relevance in a large cohort of patients with DCM. Methods We prospectively enrolled 645 DCM patients referred for LGE-CMR. All underwent standardized imaging protocols and baseline health evaluations. LGE images were blindly scored using objective criteria, inclusive of RVI site and MWS fibrosis. Associations between LGE patterns and CMR-based markers of adverse chamber remodeling were evaluated. Independent associations of LGE fibrosis patterns with the primary composite clinical outcome of heart failure admission or death were determined by multivariable analysis. Results The mean age was 56 ± 14 (28% female) with a mean left ventricular (LV) ejection fraction (LVEF) of 37%. At a median of 1061 days, 129 patients (20%) experienced the primary outcome. Any abnormal LGE was present in 306 patients (47%), inclusive of 274 (42%) meeting criteria for RVI site fibrosis and 167 (26%) for MWS fibrosis. All with MWS fibrosis showed RVI site fibrosis. Solitary RVI site fibrosis was associated with higher bi-ventricular volumes [LV end-systolic volume index (78 ± 39 vs. 66 ± 33 ml/m2, p = 0.01), RV end-diastolic volume index (94 ± 28 vs. 84 ± 22 ml/m2 (p < 0.01), RV end-systolic volume index (56 ± 26 vs. 45 ± 17 ml/m2, p < 0.01)], lower bi-ventricular function [LVEF 35 ± 12 vs. 39 ± 10% (p < 0.01), RV ejection fraction (RVEF) 43 ± 12 vs. 48 ± 10% (p < 0.01)], and higher extracellular volume (ECV). Patient with solitary RVI site fibrosis experienced a non-significant 1.4-fold risk of the primary outcome, increasing to a significant 2.6-fold risk when accompanied by MWS fibrosis. Conclusions RVI site fibrosis in the absence of MWS fibrosis is associated with bi-ventricular remodelling and intermediate risk of heart failure admission or death. Our study findings suggest RVI site fibrosis to be pre-requisite for the incremental development of MWS fibrosis, a more advanced phenotype associated with greater LV remodeling and risk of clinical events.

The Long-Term Impact of Bariatric Surgery on Development of Atrial Fibrillation and Cardiovascular Events in Obese Patients: An Historical Cohort Study

Objective: To determine whether early Roux-en-Y gastric bypass surgery (RYGB) reduces the risk of Major adverse cardiovascular events (MACE) in patients with obesity.Patients and Methods: We conducted a study of patients with class II and III obesity [body mass index (BMI) &gt; 35 kg/m2] from Olmsted County, Minnesota, who underwent obesity clinic consultation between the years 1993–2012, and had either RYGB surgery within 1 year (RYGB-1Y group), or medically managed (No-RYGB group). The composite endpoint of MACE (all-cause mortality, stroke, heart failure admission and acute myocardial infarction) was the primary endpoint, with new-onset AF as the secondary endpoint.Results: Of the 1,009 study patients, 308 had RYGB-1Y and 701 were medically managed (No-RYGB). Overall, the age was 44.0 ± 12.4 (mean ± SD) years; BMI was 45.0 ± 6.8 kg/m2. The RYGB-1Y group had a lower rate of MACE (adjusted hazard ratio (HR), 0.62; 95% CI, 0.44–0.88; P = 0.008) and lower mortality (adjusted HR, 0.51; 95% CI, 0.26–0.96; P = 0.04) than the No-RYGB group. The RYGB-1Y surgery was not associated with lower AF occurrence (HR, 0.66; 95% CI, 0.40–1.10; P = 0.11).Conclusion: An early RYGB approach for BMI reduction was associated with lower rates of MACE.

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

Background: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. Methods: The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated. Results: The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R2 = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran’s I and resultant R2; however, none of the traditional models performed well (R2 = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided. Conclusions. Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

Geospatial and Explanatory Models for Heart Failure Admissions, 2016 through 2018

Background. About 5.7 million individuals in the United States have heart failure, and the disease was estimated to cost about $42.9 billion in 2020. This research provides geographical incidence models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. The research also provides updated financial and demand estimates based on inflationary pressures and disease rate increases. Understanding patterns is important to both policymakers and health administrators for cost control and planning. Methods. The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnosis-related groups (DRGs) depict areas of high incidence. State and county level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts were calculated for 2016 through 2018. Results: The incidence of heart failure has increased over time with highest intensities in the East and center of the country; however, several Northern states (e.g., Minnesota) have seen large increases since 2016. The best predictive model for forecasting the number of diagnoses at the hospital unit of analysis was an extremely randomized tree ensemble (predictive R2 = 0.86 applied to a 20% unobserved test set.) The important variables in this model included workload metrics and hospital type. State level spatial lag models using 1st order Queen’s criteria were best at estimating heart failure admission rates (R2 =.816). At the county level, OLS was preferred over any GIS model based on a statistically insignificant Moran’s I and resultant R2; however, none of the traditional models performed well (R2=.169 for the OLS). Gradient boosted tree models were able to predict 36% of the total Sum of Squares; however, and the most important factors were facility workload, mean cash-on-hand of the hospitals in the county, and mean equity of those hospitals.. Online interactive maps at the state and county levels are provided. Conclusions. Heart failure and associated expenditures are increasing. Overall, the total cost of the three DRGs in the study has increased approximately $61 billion from 2016 through 2018 (average of two estimates). The increase in the more expensive DRG (DRG 291) has outpaced others with an associated increase of $92 billion in expenditures. With the increase in demand (linked to obesity and other factors) as well as the relatively steady-state supply of cardiologists over time, the costs are likely to balloon over the next decade. Models like the ones presented here that reliably forecast demand are needed to inform healthcare leaders.