Circulation: Heart Failure最新文献

Children with left heart disease are at risk for developing pulmonary hypertension, initially secondary to pulmonary venous hypertension that can progress to include elevated pulmonary vascular resistance, known as combined pre- and postcapillary pulmonary hypertension. Elevated pulmonary vascular resistance may pose a risk to the right ventricle of a newly transplanted heart because of increased afterload and is an important consideration for heart transplant eligibility. However, the epidemiology, pathophysiology, optimal diagnostic and treatment approaches, and thresholds for pulmonary vascular resistance in pulmonary hypertension associated with left heart disease remain unclear because of lack of evidence, particularly in pediatrics. The result is heterogeneity with respect to hemodynamic assessment, use of pulmonary vasodilator therapies, and heart transplant listing. This scientific statement aims to synthesize the available data and highlight areas of general consensus as well as important knowledge gaps.

Background: Heart failure with preserved ejection fraction (HFpEF) is a major public health problem characterized by multiple simultaneous comorbidities whose specific contribution is challenging to disentangle in humans, leading to a generalized therapeutic approach that may not account for the underlying pathology.

Methods: We followed distinct mouse models of major HFpEF comorbidities for 2.5 years to unveil their specific contribution to the syndrome.

Results: All comorbidities contributed to HFpEF through partially distinct routes. Aging alone resulted in HFpEF in old age, with delayed left ventricular relaxation and kidney fibrosis. Obesity induced a faster deterioration of relaxation associated with enlarged left ventricle and liver fibrosis. Hypertension caused delayed ventricular relaxation independent from structural changes that preceded left atrial dilatation linked to aortic stiffness and increased fibrosis in myocardium and kidney. Chronic intermittent hypoxia led to HFpEF and relaxation impairment associated with pulmonary hypertension. Hyperglycemia accelerated diastolic dysfunction and HFpEF onset associated with reduced arterial flow and left ventricular remodeling. Therefore, the pathological substrates contributing to HFpEF included cardiac and noncardiac alterations with differential features for each comorbidity. Critically, the characteristics linked to diastolic dysfunction and HFpEF across the various comorbidities agreed with phenogroups observed in human patients.

Conclusions: The identification of time-dependent pathological features provides a comprehensive picture of HFpEF progression associated with each comorbidity.

Background: Medical record review by a physician clinical events committee is the gold standard for identifying cardiovascular outcomes in clinical trials, but is labor-intensive and poorly reproducible. Automated outcome adjudication by artificial intelligence (AI) could enable larger and less expensive clinical trials but has not been validated in global studies.

Methods: We developed a novel model for automated AI-based heart failure adjudication (Heart Failure Natural Language Processing) using hospitalizations from 3 international clinical outcomes trials. This model was tested on potential heart failure hospitalizations from the DELIVER trial (Dapagliflozin Evaluation to Improve the Lives of Patients With Preserved Ejection Fraction Heart Failure), a cardiovascular outcomes trial comparing dapagliflozin with placebo in 6063 patients with heart failure with mildly reduced or preserved ejection fraction. AI-based adjudications were compared with adjudications from a clinical events committee that followed Food and Drug Administration-based criteria.

Results: AI-based adjudication agreed with the clinical events committee in 83% of events. A strategy of human review for events that the AI model deemed uncertain (16%) would have achieved 91% agreement with the clinical events committee while reducing the adjudication workload by 84%. The estimated effect of dapagliflozin on heart failure hospitalization was nearly identical with AI-based adjudication (hazard ratio, 0.76 [95% CI, 0.66-0.88]) compared with clinical events committee adjudication (hazard ratio, 0.77 [95% CI, 0.67-0.89]). The AI model extracted symptoms, signs, and treatments of heart failure from each medical record in tabular format and quoted sentences documenting them.

Conclusions: AI-based adjudication of clinical outcomes has the potential to improve the efficiency of global clinical trials while preserving accuracy and interpretability.