April 2020 at a Glance: Epidemiology, Prevention, and Biomarkers
Bibliographic record
Abstract
Data about epidemiology of heart failure (HF) in Europe are largely insufficient.1, 2 The Heart Failure Association Atlas registry is the first European study providing information about HF epidemiology, resource, and reimbursement policies for HF management.3 Studies defining prevalence and outcome of HF in Asia are also limited.4, 5 The Heart Failure Registry of Patient Outcomes (HERO) includes patients admitted for HF in 73 Chinese centres. In-hospital mortality rate was 3.2%, and incidence of early mortality or readmission was 22%.6 Incidence and trends of cardiogenic shock complicating acute myocardial infarction have recently been reported in a Danish cohort.7. In this issue, French data are provided. The prevalence of cardiogenic shock after acute myocardial infarction decreased from 2005 (5.9%) to 2015 (2.8%), whilst the use of invasive management increased over time. In-hospital mortality remained unchanged.8 Inflammation has a central role in the pathophysiology of HF.9 Kaluza et al.10 investigated the association between anti-inflammatory potential of diet, estimated using an anti-inflammatory diet index (AIDI), and risk of HF. An inverse association between the AIDI and HF incidence was observed in current and ex-smokers but not in never-smokers. Bio-adrenomedullin (bio-ADM) is a possible marker of congestion.11 Pandhi et al.12 confirmed that high bio-ADM levels at discharge are strongly associated with residual congestion in patients with acute HF. Moreover, combined with high dose of loop diuretics, high bio-ADM was associated with a four-fold increased risk of HF rehospitalization at 60 days.12 Natriuretic peptides have a main role in the diagnosis and stratification of HF patients.13 However, their role may be limited in patients with atrial fibrillation. Kuan et al.14 evaluated the diagnostic performance of natriuretic peptides and troponin as diagnostic markers in patients with acute HF and atrial fibrillation. They found that mid-regional proADM yielded the best results for HF diagnosis. Fibroblast growth factor 23 (FGF23) is involved in HF pathophysiology.15. Stöhr et al.16 investigated the role of intact and C-terminal FGF23 (iFGF23 and cFGF23) in predicting prognosis of elderly patients with HF. Only cFGF23 was a predictor of HF events at 3 months. Sodium–glucose co-transporter 2 inhibitors are one of the most promising agents for the treatment of chronic HF.17 Their role in the acute setting is less known. In a pilot trial, 80 patients with acute HF were randomized to empagliflozin 10 mg/day or placebo for 30 days. Empagliflozin did not improve significantly symptoms, diuretic response, natriuretic peptide levels, and length of hospital stay but reduced worsening HF, rehospitalization for HF and death at 60 days.18 Janwanishstaporn et al.19 investigated the prognostic role of left ventricular ejection fraction (LVEF) in patients hospitalized for acute HF. A progressively lower risk of clinical events was found as LVEF increased. The prognostic impact of LVEF was greater in patients with ischaemic aetiology or pre-existing HF. No trial evaluated the benefits of left ventricular assist device as destination therapy in Europe, to date.20 Karason et al.21 show the design of a randomized comparison between left ventricular assist device and optimal medical therapy in patients with advanced HF not suitable for cardiac transplantation. Two-year survival, mid-term benefits, and costs will be explored.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.178 | 0.123 |
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".