November 2018 at a Glance: From Prediction of Heart Failure in Asymptomatic Subjects to Advanced Chronic Heart Failure
Bibliographic record
Abstract
Patients with heart failure (HF) progress to an advanced stage characterized by severe signs and symptoms, frequent episodes of decompensation and high mortality despite optimized treatment with evidence-based medical, device and surgical therapy. These patients need advanced therapies such as cardiac transplantation and mechanical circulatory assistance.1 Intermittent inotropic infusions of inotropes, ultrafiltration, peritoneal dialysis and palliative care should also be considered when evidence-based therapies have failed. It is therefore mandatory to better identify these patients with advanced chronic HF (ACHF). The Heart Failure Association has issued an updated version of a first statement about ACHF.2, 3 This second statement provides a new definition of ACHF, including aspects such as the possibility that episodes of decompensation are treated without patient's hospitalization, the inclusion of patients with HF and preserved ejection fraction (HFpEF), the role of co-morbidities or malignant arrhythmias. Prognostic variables, treatment options, including devices, and disease management programmes are described and discussed.3 On the opposite side of the spectrum, we have the challenge to identify patients at greatest risk of HF. In a study of 623 asymptomatic subjects at risk for HF, only measurements of diastolic dysfunction could predict the development of symptomatic HF or death, independent of baseline left ventricular ejection fraction (EF) values, mildly impaired (40–53%), or not. Diastolic dysfunction was defined based on at least two of three abnormal parameters: increased left atrial volume, abnormal septal E', or E/E' ratio.4 Kristensen et al.5 analysed 2707 patients with HF and reduced EF (HFrEF) from BEST (Beta-blocker Evaluation of Survival Trial). Patients were divided into three groups: no diabetes and diabetes without or with microvascular complications, neuropathy, nephropathy, or retinopathy. Patients with diabetes and microvascular complications had more severe symptoms, worse quality of life and poorer outcomes. Their risk of cardiovascular death or HF hospitalizations (primary composite outcome) was of 50 per 100 person-years of follow-up, compared with 34 and 29 per 100 person-years in those with diabetes and no microvascular complications and with no diabetes, respectively. Compared with non-diabetics, the adjusted hazard ratio (HR) for the composite outcome was 1.44, [95% confidence interval (CI) 1.22–1.70] in patients with diabetes and microvascular complications. It was much lower in patients with diabetes without complications (HR 1.18, 95% CI 1.03–1.35). Similar results were found for mortality. Thus, the presence of microvascular complications identifies HFrEF diabetic patients with the highest risk of poor outcomes.5 Obesity and metabolic syndrome seem to be associated with a distinct phenotype of HFpEF.6 Van Woerden et al.7 analysed the role of epicardial fat in 64 HFpEF patients and 20 controls of comparable age, sex and body mass index, studied by cardiac magnetic resonance. Total epicardial fat volume was higher in HFpEF patients compared to controls, with the highest values in patients with atrial fibrillation and/or diabetes mellitus. Positive correlations between epicardial fat volume and plasma levels of biomarkers of cardiac injury, creatine kinase-MB and troponin T, were also found. The role of frailty as an independent predictor of poorer outcomes in HFrEF patients is established.8 Sanders et al.9 assessed frailty in 1767 patients enrolled in Americas in TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial). A frailty index, based on the total sum of deficits present divided by the number of deficits measured, was constructed at baseline using 39 clinical, laboratory, and self-reported variables. Patients with the highest frailty index had more co-morbidities, including renal dysfunction, diabetes, a higher body mass index and blood pressure. Frailty was a predictor of cardiovascular death, aborted cardiac arrest or HF hospitalizations as well as all of the individual outcomes. No interaction with the effects of spironolactone was found. Emami et al.10 assessed the role of sarcopenia, compared with cachexia, in ambulatory HF patients. Patients with sarcopenia had lower strength and exercise capacity than both the no wasting and the cachectic HF group. Muscle strength, peak oxygen uptake, distance in the 6-minute walk test and quality of life were lowest in the sarcopenia + cachexia group. The study shows the independent role of sarcopenia vs. weight loss in HF.10 Treatment of acute HF remains based on loop diuretic administration.11 Diuretic resistance and insufficient congestion relief occur in many patients and are associated with poorer outcomes.12-15 New diuretic strategies are needed. Acetazolamide inhibits sodium reabsorption in the nephron proximal tubule, and may also block distal sodium reabsorption and cause renal vasodilatation. ADVOR is a multicentre, randomized, double-blind, placebo-controlled study to test the hypothesis that acetazolamide may improve decongestion when combined with loop diuretic therapy in patients with acute HF.16 The primary endpoint is successful decongestion after 3 days of treatment and secondary endpoints are death or HF rehospitalizations at 3 months, length of the initial hospitalization, changes in quality of life.16 Partial adenosine A1-receptor agonists can improve left ventricular function and remodelling. These effects have been shown in experimental models and small patient groups.17-19 Further data are expected from the two phase 2b clinical trials PANTHEON and PANACHE.20 They are multicentre, randomized, double-blind, placebo controlled, parallel-group, dose-finding phase 2 trials testing the safety and efficacy of 20 week treatment with neladenoson bialanate vs. placebo in HFrEF and HFpEF patients, respectively. Primary endpoints are the absolute change from baseline in left ventricular EF and N-terminal pro brain natriuretic peptide in PANTHEON and the 6-minute walk test distance change from baseline in PANACHE.20
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".