Effects of Bariatric Surgery on Human Small Artery Function Evidence for Reduction in Perivascular Adipocyte Inflammation, and the Restoration of Normal Anticontractile Activity Despite Persistent Obesity

ObjectivesThe aim of this study was to investigate the effects of bariatric surgery on small artery function and the mechanisms underlying this.BackgroundIn lean healthy humans, perivascular adipose tissue (PVAT) exerts an anticontractile effect on adjacent small arteries, but this is lost in obesity-associated conditions such as the metabolic syndrome and type II diabetes where there is evidence of adipocyte inflammation and increased oxidative stress.MethodsSegments of small subcutaneous artery and perivascular fat were harvested from severely obese individuals before (n = 20) and 6 months after bariatric surgery (n = 15). Small artery contractile function was examined in vitro with wire myography, and perivascular adipose tissue (PVAT) morphology was assessed with immunohistochemistry.ResultsThe anticontractile activity of PVAT was lost in obese patients before surgery when compared with healthy volunteers and was restored 6 months after bariatric surgery. In vitro protocols with superoxide dismutase and catalase rescued PVAT anticontractile function in tissue from obese individuals before surgery. The improvement in anticontractile function after surgery was accompanied by improvements in insulin sensitivity, serum glycemic indexes, inflammatory cytokines, adipokine profile, and systolic blood pressure together with increased PVAT adiponectin and nitric oxide bioavailability and reduced macrophage infiltration and inflammation. These changes were observed despite the patients remaining severely obese.ConclusionsBariatric surgery and its attendant improvements in weight, blood pressure, inflammation, and metabolism collectively reverse the obesity-induced alteration to PVAT anticontractile function. This reversal is attributable to reductions in local adipose inflammation and oxidative stress with improved adiponectin and nitric oxide bioavailability

Treatment adherence in a randomised controlled trial of pirfenidone in HFpEF: determinants and impact on efficacy

ObjectivesMedication adherence in patients with heart failure with preserved ejection fraction is unclear. This study sought to evaluate treatment adherence in the Pirfenidone in Patients with Heart Failure and Preserved Left Ventricular Ejection Fraction (PIROUETTE) trial.Methods and resultsAdherence was evaluated through pill counts and diary cards. Univariable and multivariable regression models were used to assess the relationship between adherence and baseline characteristics. Instrumental variable regression was used to estimate the causal effect of pirfenidone treatment duration on myocardial fibrosis. Complete adherence data were available in 54 of 80 participants completing the trial. Mean adherence to study medication was 94.7% and 96.9% in the pirfenidone and placebo groups, respectively. Each additional day of treatment with pirfenidone resulted in a significant decrease in myocardial extracellular volume (-0.004%; 95% confidence interval: -0.007% to -0.001%; P = 0.007). Associations with adherence included older age, higher symptom burden, lower body weight, and smaller right ventricular size.ConclusionAdherence to study medication in the PIROUETTE trial was very high among patients for whom complete adherence data were available. Importantly, each additional day of treatment reduced myocardial fibrosis. Potential predictors of adherence were identified. Implementation of improved methods for assessing adherence is required

Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data

Background: Heart failure with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, compare phenogroup characteristics and outcomes, and identify factors to straightforwardly predict phenogroup membership, from electronic health record data.Methods: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction &gt; 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. Penalised multinomial logistic regression was applied to predict phenogroup membership.Results: Three phenogroups were identified: 1. Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; 2. More frail patients, with higher rates of lung disease and atrial fibrillation; 3. Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p &lt; 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF. A combination of ten variables assigned patients to phenogroups with 90% accuracy.Conclusions: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles.<br/

Identification of heart failure hospitalisation from NHS Digital data:comparison with expert adjudication

Background and Aims: Population-wide, person-level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalisation for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF.Methods: Patients experiencing at least one HHF, as determined by NHS Digital data, and age and sex matched patients not experiencing HHF, were identified from a prospective cohort study and underwent expert adjudication. Three code sets commonly used to identify HHF were applied to the data and compared with expert adjudication (I50: International Classification of Diseases (ICD)-10 codes beginning I50; OIS: Clinical Commissioning Groups Outcomes Indicator Set; NICOR: National Institute for Cardiovascular Outcomes Research, used as the basis for the National Heart Failure Audit in England and Wales). Results: 504 patients underwent expert adjudication, of which 10 (2%) were adjudicated to have experienced HHF. Specificity was high across all three code sets in the first diagnosis position (I50: 96·2% [95% confidence interval, CI: 94·1 – 97·7%]; NICOR: 93·3% [CI 90·8 – 95·4%]; OIS: 95·6% [CI 93·3 – 97·2%]), but decreased substantially as the number of diagnosis positions expanded. Sensitivity (40·0% [CI 12·2 – 73·8%]) and positive predictive value (PPV) (highest with I50: 17·4% [CI 8·1 – 33·6%]) were low in the first diagnosis position for all coding sets. PPV was higher for the National Heart Failure Audit criteria, albeit modestly (36·4%; [16·6 – 62·2%]).Conclusions: NHS Digital data were not able to accurately identify HHF, and should not be used in isolation for this purpose. <br/

Development and validation of imaging-free myocardial fibrosis prediction models, association with outcomes, and sample size estimation for phase 3 trials

Background and Aims Phase 3 trials testing whether pharmacologic interventions targeting myocardial fibrosis (MF) improve outcomes require MF measurement that does not rely on tomographic imaging with intravenous contrast. Methods We developed and externally validated extracellular volume (ECV) prediction models incorporating readily available data (comorbidity and natriuretic peptide variables), excluding tomographic imaging variables. Survival analysis tested associations between predicted ECV and incident outcomes (death or hospitalization for heart failure). We created various sample size estimates for a hypothetical therapeutic clinical trial testing an anti-fibrotic therapy using: a) predicted ECV, b) measured ECV, or c) no ECV. Results Multivariable models predicting ECV had reasonable discrimination (optimism corrected C-statistic for predicted ECV ≥27% 0.78 (95%CI 90.75-0.80) in the derivation cohort (n=1663) and 0.74 (95%CI 0.71-0.76) in the validation cohort (n=1578)) and reasonable calibration. Predicted ECV associated with adverse outcomes in Cox regression models: ECV ≥27% (binary variable) HR 2.21 (1.84–2.66). For a hypothetical clinical trial with an inclusion criterion of ECV ≥27%, use of predicted ECV (with probability threshold of 0.69 and 80% specificity) compared to measured ECV would obviate the need to perform 3940 CMR scans, at the cost of an additional 3052 participants screened and 705 participants enrolled. Conclusions Predicted ECV (derived without tomographic imaging) associates with outcomes and efficiently identifies vulnerable patients who might benefit from treatment. Predicted ECV may foster the design of phase 3 trials targeting MF with higher numbers of screened and enrolled participants, but with simplified eligibility criteria, avoiding the complexity of tomographic imaging. Key Question Phase 3 trials targeting myocardial fibrosis (MF) to improve outcomes require MF measurement that does not rely on tomographic imaging with intravenous contrast. So, we developed and validated extracellular volume (ECV) prediction models incorporating clinical data, excluding tomographic imaging. Key Finding Predicted ECV had reasonable discrimination and associated with outcomes. For a hypothetical trial with an ECV ≥27% inclusion criterion, using predicted ECV versus measured ECV would avoid 3940 cardiovascular magnetic resonance (CMR) scans, but require an additional 3052 participants screened and 705 enrolled. Take-home Message Predicted ECV (derived without imaging) associates with outcomes and efficiently identifies vulnerable patients. Predicted ECV may foster phase 3 trials targeting MF with higher numbers of screened and enrolled participants, but simplified eligibility criteria, avoiding the complexity of tomographic imaging