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Record W2598563835 · doi:10.1002/ejhf.794

March 2017 at a Glance: Pathophysiology, Imaging, Biomarkers and Devices

2017· review· en· W2598563835 on OpenAlexaff
Marco Metra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2017
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsEjection fractionMedicineInternal medicineHeart failureCardiologyPathophysiologyAmbulatory

Abstract

fetched live from OpenAlex

Few things can be as much fascinating as finding a genetic determinant of symptoms and clinical outcomes in patients with heart failure (HF). Angermann et al. accomplished this. They analysed the polymorphism of the gene encoding for the neuropeptide S receptor in 924 patients with HF.1 The T-allele variant of this gene is associated with increased anxiety and overinterpretation of symptoms. In this study, TT genotype carriers had similar mortality but more re-hospitalizations and ambulatory visits and this association remained significant at multivariable analysis.1 The mechanisms of HF with preserved ejection fraction (HFpEF) are still debated. A landmark hypothesis is that it is caused by increased oxidative stress and reduced cGMP activity.2, 3 The mechanistic study by Mátyás et al. supports this. The administration of the phosphodiesterase-5A (PDE5) inhibitor vardenafil to diabetic rats prevented the development of diastolic dysfunction and restored cGMP levels and protein-kinase G activity.4 These data also suggest that PDE5 inhibition may be effective at an earlier stage, before the development of symptomatic HFpEF.5 Two-dimesional speckle tracking echocardiography can evaluate myocardial strain, namely radial, circumferential and longitudinal strain, and regional left ventricular (LV) deformation. This allows detection of early abnormalities of LV function occurring before changes in LV volumes and ejection fraction (EF) can be shown. Tops et al. review this topic and describe cases in which an abnormal global longitudinal strain shows an abnormality in LV function, in the presence of a normal LVEF.6 Two studies show the role of ferritin as an independent risk factor for HF development. These studies are based on two cohorts of subjects free of signs of cardiovascular disease at entry, one from the ARIC Study, with 1063 participants, and the other from PREVEND, with 6386 subjects.7, 8 Mean age was 53 years in both cohorts. Follow-up duration averaged 21 and 8 years, with an incidence of newly diagnosed HF of 13% and 3%, respectively. High serum ferritin levels were independent predictors of an increased risk of HF in both studies. In the first study, low ferritin levels were also associated with an increased risk of HF, compared to normal levels, whereas the association was significant only in women in the second study.7, 8 These studies add data to the complex relationship between iron metabolism and cardiovascular disease, namely HF. While iron deficiency is a major determinant of HF symptoms and, probably, outcomes and a target for treatment in patients with established HF or valve disease,9-11 ferritin can be a marker of increased risk of HF in the intially normal subjects, likely as a marker of inflammation. Symptoms, assessed by NYHA class and QRS duration, but not QRS morphology, had an independent association with mortality in a large Swedish registry including 13 423 patients.12 Retrospective analyses of randomized trials have shown the independent prognostic value of both QRS duration and QRS morphology.13, 14 The role of co-morbidities was assessed in 4334 patients who underwent implantable cardioverter defibrillator (ICD) implantation for primary or secondary prevention. The co-morbidity burden had no impact on the rate of ICD appropriate interventions but was an independent predictor of increased mortality with a greater likelihood of dying without prior appropriate ICD therapy, 72% when implanted for primary prevention and 45% for secondary prevention. The effects and safety of carotid body resection was assessed in a pilot trial. The procedure reduced muscle sympathetic activity and chemoreflex sensititviy with an improvement in exercise tolerance. Worsened oxygen saturation in the nightime was also observed.15 Veno-arterial extracorporeal membrane oxygenation (ECMO) is used for the treatment of acute HF and cardiogenic shock. This procedure may also cause an increased LV afterload secondary to the retrograde flow towards the heart, which may further impair transaortic valve flow. Hence, procedures for LV venting of patients on ECMO are used.16 Pappalardo et al. report a two-centre experienece in 157 patients on ECMO among whom 34 had concomitant Impella treatment. The two groups were compared by propensity matching and a better in-hospital outcome is suggested with the ECMO- Impella combined treatment.17 Remote monitoring with a biventricular ICD with advanced diagnostics was compared with traditional in-office follow-up in a prospective, randomized, multicentre controlled trial in 865 patients. No difference was found in the primary endpoint of death or cardiovascular or device-related hospitalizations. However, healthcare resource utilization was significantly reduced, mainly through a reduction in scheduled in-hospital visits.18 These data confirm a recent meta-analysis.19 Prevention of HF decompensation using implantable cardiac devices is a major area of research.20 Adamson et al. compare remote haemodynamic monitoring with implantable devices measuring intracardiac pressures with standard of care in a meta-anlaysis of 5 studies including 1296 patients with chronic HF. Remote haemodynamic monitoring was associated with a 38% reduction in HF hospitalizations, with a similar 32% reduction when only the three randomized prospective trials were analyzed.21

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.052
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0520.044

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.

Opus teacher head0.055
GPT teacher head0.351
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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