March 2020 at a Glance: Heart Failure with Preserved Ejection Fraction, Left Atrial Myopathy, Atrial Fibrillation and Cardiac Amyloidosis
Bibliographic record
Abstract
Diagnosis of heart failure (HF) with preserved ejection fraction (HFpEF) remains challenging.1 The algorithm developed by the Heart Failure Association (HFA) and the validation of its second step in two independent study groups are reported in this issue. The positive and negative predictive values of a high and a low HFA-PEFF score were 98% and 73%, respectively. However, 36% of patients were classified in the intermediate-likelihood category, where additional testing, e.g. diastolic stress testing, is advised.2 Coronary microvascular function may concur to the pathogenesis of HFpEF.3 Yang et al.4 analysed it in HFpEF patients. Coronary microvascular function was abnormal in 117 patients (72%). Isolated endothelium-dependent and independent microvascular dysfunction were present in 29% and 33% of patients, respectively. Combined microvascular dysfunction was found in 10%. Endothelium-independent microvascular dysfunction was associated with worse diastolic function and higher mortality.4 Epicardial fat may contribute to diastolic dysfunction.3, 5 Wu et al.6 showed that also the amount of intramyocardial fat, measured by cardiac magnetic resonance, is higher in HFpEF compared to patients with HF and reduced ejection fraction (HFrEF) and to patients without HF. Intramyocardial fat was independently related with diastolic dysfunction in HFpEF. Left atrial (LA) function is associated with an increased risk of HF in patients with atrial fibrillation (AF).7 Khan et al.8 conducted a meta-analysis of 22 studies and showed worse LA function in HFpEF patients compared to controls. Decreased LA reservoir strain was associated with more clinical events. Cardiopulmonary exercise test (CPET) is useful for the risk stratification of HFpEF patients.9, 10 Pugliese et al.11 evaluated patients with hypertension with and without HFpEF by CPET and exercise stress echocardiography. Reduced peak oxygen consumption, low cardiac output, high E/e' and B-lines were found more frequently in patients with HFpEF and hypertension compared to the others. Severe functional mitral regurgitation (FMR) is associated with poor outcomes and may be a target for treatment of HFrEF.12-14 Tamargo et al.15 compared HFpEF patients with or without mild/moderate FMR. They found that HFpEF patients with FMR have larger and less compliant atria, worse pulmonary haemodynamics and reduced cardiac output compared to non-FMR-HFpEF patients, regardless of AF. Inciardi et al.,16 evaluating patients undergoing echocardiography, observed a significant association between FMR (even mild), LA function and pulmonary pressure. Kloosterman et al.17 calculated an AF genetic risk score in 3759 HF patients. It was an independent predictor of AF prevalence, regardless of HF phenotypes, and improved AF prediction when added to clinical risk factors. The role of heart rate in patients with AF and HF is still controversial.14, 18 Docherty et al.19 demonstrated that, independently of natriuretic peptides, high heart rate is associated with poor outcomes in patients with HFrEF and sinus rhythm, but not in those with HFrEF and AF. Telemonitoring may have a crucial role for HF management.14, 20 Previous studies suggested that it may be more useful in patients with AF and HF. However, these findings were not confirmed in an analysis of the Remote Management of Heart Failure Using Implanted Electronic Devices (REM-HF) trial.21 The two most common types of cardiac amyloidosis are transthyretin (TTR)-related and light-chain amyloidosis (AL).22 Milandri et al.23 evaluated the prevalence and impact of carpal tunnel syndrome (CTS) in patients with TTR and AL amyloidosis, compared to the general population. CTS was most common in male, elderly patients with TTR amyloidosis and cardiac involvement. It was an independent predictor of mortality and was associated with an increased risk of cardiomyopathy development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.256 | 0.117 |
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".