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Record W4200601702 · doi:10.1002/ejhf.1896

December 2021 at a glance: focus on medical treatment, valvular heart disease and prognostic models

2021· article· en· W4200601702 on OpenAlexaff
Daniela Tomasoni, Marianna Adamo, Marco Metra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2021
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineSacubitrilEjection fractionHeart failureValsartanInternal medicineEnalaprilClinical endpointCardiologyHeart failure with preserved ejection fractionRandomized controlled trialClinical trialBlood pressureAngiotensin-converting enzyme

Abstract

fetched live from OpenAlex

Guideline-directed medical therapy (GDMT) represents a powerful tool to prevent and reduce cardiovascular mortality and hospitalizations in patients with heart failure (HF) with reduced ejection fraction (HFrEF).1 In clinical practice, many factors may lead to GDMT underutilization and only a minority of patients receive target doses used in the landmark trials.2-5 Seferovic et al.6 proposed new strategies for the implementation of GDMT. Professional education, motivation, and training, as well as patient empowerment for self-care, modern technologies, multidisciplinary team management, novel drugs and better patient profiling are possible solutions to implement GDMT. ACTIVITY-HF is a randomized controlled trial that enrolled 201 patients with HFrEF and aimed at comparing the effects of sacubitril/valsartan versus enalapril on exercise capacity. The primary endpoint of change from baseline to 12 weeks in peak oxygen consumption did not differ between groups.7 Results are consistent with previous findings form the OUTSTEP-HF trial.8 Sacubitril/valsartan was compared to valsartan alone in patients with HF with preserved ejection fraction (HFpEF) in the Prospective Comparison of ARNI with ARB Global Outcomes in HF with Preserved Ejection Fraction (PARAGON-HF) trial.9-11 Suzuki et al.12 showed that non-completion of the run-in period, an event occurring in 16.1% of the 4822 randomized patients, was associated with multiple factors, including lower systolic blood pressure, lower serum sodium and haemoglobin, worse renal function, higher N-terminal pro-B-type natriuretic peptide, higher New York Heart Association functional class, prior HF hospitalization, and lack of prior use of renin–angiotensin system inhibitors or beta-blocker. Most of these factors seem to be related to a more advanced disease. Heart failure with preserved ejection fraction is a heterogeneous syndrome with multiple aetiologies and phenotypes.13-15 Kammerlander et al.16 investigated the prevalence of HFpEF following left-sided valve repair. Out of 973 patients included, 673 underwent surgery and 300, with HFpEF, did not (control group). After surgery, 67.4% of patients fulfilled the criteria for the diagnosis of HFpEF, 20.6% were without HF and 12% developed HF with either mid-range or reduced ejection fraction. Of note, only a minority of patients were correctly diagnosed with HFpEF by cardiologists at follow-up. Patients who developed HF, irrespective of ejection fraction, had a higher risk of death, compared to those without HF, and a similar risk compared to the HFpEF control group. The role of biomarkers for the prognostic stratification of HF patients is well established.17, 18 In a cohort of 708 patients with aortic stenosis, several biomarkers were associated with the endpoints of death and death or HF hospitalizations. Using a machine-learning method, interleukin-6 (a marker of inflammation) and fibroblast growth factor-23 (a marker of calcification) resulted the most strongly associated with adverse outcomes.19 Many risk prediction models have been developed.20, 21 Codina et al.22 proposed a head-to-head comparison of different prediction models, including Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, Seattle Heart Failure Model (SHFM), PARADIGM Risk of Events and Death in the Contemporary Treatment of Heart Failure (PREDICT-HF) and Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator. A total of 1166 patients were included. The four scores had similar performance. However, correlation between them was relatively poor. Furthermore, SHFM and PREDICT-HF underestimated, whereas BCN-Bio-HF, even if it showed the best accuracy, overestimated the risk of events. The outcome of patients with acute myocarditis and life-threatening ventricular arrhythmias is still unsettled. Gentile et al.23 retrospectively studied the incidence and the predictors of recurrent major arrhythmic events (MAEs), defined as sudden cardiac death or successfully defibrillated ventricular fibrillation, or sustained ventricular tachycardia (sVT) after discharge. Out of 156 patients, 58 (37.2%) experienced MAEs after discharge. MAEs with sVT at presentation, late gadolinium enhancement involving ≥2 myocardial segments and absence of positive short-tau inversion recovery at first cardiac magnetic resonance were identified as valuable tools for risk stratification. Peripartum cardiomyopathy (PPCM) usually occurs during pregnancy or soon after delivery, in the absence of other causes of HF.24, 25 The extent of hypertension in women with PPCM has been investigated by Jackson et al.26 using data from the European Society of Cardiology EURObservational Research Programme PPCM Registry. Maternal and neonatal outcomes were analysed in three phenotypes of women: PPCM with no hypertension (PPCM-noHTN), hypertension with no pre-eclampsia (PPCM-HTN) and PPCM with pre-eclampsia (PPCM-PE). Women with PPCM-PE presented with more severe symptoms and signs of HF than those with PPCM-noHTN, despite having better baseline left ventricular ejection fraction and a greater likelihood of left ventricular recovery. Differences were also found in neonatal outcomes.

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)0.004
metaresearch head score (Gemma)0.013
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: Editorial · Consensus signal: none
Teacher disagreement score0.191
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.1910.069

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 head0.020
GPT teacher head0.265
Teacher spread0.246 · 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
GenreEditorial

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
Published2021
Admission routes1
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