June 2016 at a Glance: Epidemiology, Renal Impairment, Heart Failure with Preserved Ejection Fraction
Bibliographic record
Abstract
A path linking kidney function, endothelial dysfunction and heart failure with preserved ejection fraction (HFpEF) can be found in this issue.1-3 The review by ter Maaten et al. sets the stage.4 It shows how renal impairment can cause inflammatory activation and endothelial dysfunction leading to myocardial stiffening, hypertrophy and fibrosis.4 Then, Rammos et al. show how a dietary supplementation of inorganic nitrates can reverse age-dependent abnormalities in left ventricular (LV) diastolic function in wild-type mice5 pointing out, again, the central role of nitric oxide deficit as a cause of diastolic dysfunction in aging and heart failure (HF).5 With respect to biomarkers, Emmens et al. evaluated plasma kidney injury molecule-1 (KIM-1), a marker of tubular damage, in 2033 patients with acute HF. KIM-1 was associated with glomerular filtration rate and urinary NGAL and was an independent predictor of 60-day HF rehospitalizations but not mortality.6 The connection between the kidney and HFpEF is further explored by Patel et al. who assessed renal denervation therapy (RDT) in 25 patients with HFpEF in a single-centre, randomized, open-controlled study.7 The study was terminated early and was therefore underpowered to reach significant results. Although no changes in the prespecified endpoints were found with renal denervation compared with optimal medical treatment, more patients had an improvement in peak VO2 and E/e' in the intervention group.7 Further studies are needed to assess the effects of RDT in patients with HF.8 Other highlights of this issue regard the epidemiology of HF. The results of the ESC-HF-Long-Term Registry are now available. It includes 12 440 patients recruited between May 2011 and April 2013 in 211 cardiology centres from 21 European and/or Mediterranean countries. It shows a relatively low one-year mortality rate of 6.4% for outpatients with a still high mortality rate of 23.6% for patients hospitalized for acute HF. In addition, one-year mortality and HF hospitalization rates remain high in both outpatients and patients with acute HF (14.5% and 36%, respectively). Geographical differences are also shown.9 Prognosis of patients with Takotsubo syndrome is still a matter of debate. Long-term all-cause mortality is shown as relatively high, actually higher than in patients admitted for ST-elevation myocardial infarction, in one study in this issue.10 The role of non-adherence to treatment is shown as an important independent risk factor for major outcomes in the patients enrolled in the Systolic Heart failure treatment with the If-inhibitor ivabradine Trial (SHIFT).11
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.017 |
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".