November 2016 at a Glance: The Left Atrium, Screening for Heart Failure, Multimodality Imaging for Cardiac Resynchronization Therapy
Bibliographic record
Abstract
Omersa et al. have analysed the Slovenian hospitalization database to examine the burden of heart failure (HF) hospitalizations in the years 2004–2012.1 Age standardized HF hospitalization rates have decreased by approximately 7% over the years. Crude rates have increased, likely because of aging of the population. HF hospitalizations remain burdened by a high readmission rates, 55% for all-cause readmissions and 37.5% for HF readmissions, in the year after discharge. Comorbidities were confirmed as major risk factors.1 These data are consistent with recent findings from other European countries.2-4 We have two articles, both based on the screening of asymptomatic subjects with ≥1 risk factor for HF and with normal left ventricular (LV) ejection fraction. The first study excluded also the subjects with valve disease or atrial fibrillation and was based on echocardiography. Overall, 62% of the subjects had ≥1 abnormality of the cardiac structure or function: LV hypertrophy, 13%, abnormal E/e', 12%, impaired global longitudinal strain (GLS), 33%, left atrial enlargement, 31%. During a median follow-up of 14 months, 12% of the subjects developed symptomatic HF or had a cardiovascular death and these were accurately predicted by the echocardiographic abnormalities.5 The second study was based on biomarkers. Among them, N-terminal-pro-brain natriuretic peptide (NT-proBNP) and high-sensitivity troponin I (hsTnI) were the most powerful predictors of HF events and major adverse cardiac events (MACE). The Authors then evaluated the efficiency of a marker-based approach using a STOP-HF-like subgroup of subjects. It was estimated that the number needed to screen to prevent one HF event or MACE would have been <100 and the number needed to treat ≤20 or 10 to prevent one HF event or one MACE, respectively.6 A marker-based screening strategy seems therefore as potentially useful to prevent cardiovascular events. The role of the left atrium in HF has often been neglected, compared with the left ventricle. However, its importance is steadily increasing along with the growing need to better evaluate HF with preserved ejection fraction (HFpEF) and concomitant arrhythmias, namely atrial fibrillation.7-9 The review by Triposkiadis et al. in this issue of the journal gives justice to this need. It shows the abnormalities in the mechanical, neurohormonal and regulatory function of the left atrium in HF and their contribution to LV diastolic function, exercise capacity, outcomes and effects of cardiac resynchronization therapy (CRT) in HF. Lastly, the effects of medical treatment, CRT and transcatheter therapies on left atrial function are discussed.10 Tokitsu et al. have examined the prognostic role of a very simple parameter, the pulse pressure, in a cohort of 951 consecutive patients with HFpEF.11 The pulse pressure was directly correlated with the pulse wave velocity and the stroke volume index and inversely correlated with serum haemoglobin and estimated glomerular filtration rate. It had a U-shaped relation with the rate of major cardiovascular events and HF hospitalizations with the higher rates in the patients with the highest or the lowest quintile of pulse pressure.11 Variability in the response to CRT and how to maximize its benefits remain a major issue.12-14 Lead positioning is one of the most important variables. Two studies in this issue of the journal regard the use of multimodality imaging for lead placement for CRT.15, 16 In the first study, the non-scarred myocardial segment with the latest mechanical activation was identified by 99 m Technetium myocardial perfusion imaging, for vitality, and speckle-tracking echocardiography, for dissynchrony. Then, cardiac computed tomography venography was used to visualize coronary sinus branches in relation to LV myocardium and select the sinus branch closest to the centre of the optimal pacing site. The study was designed as a double-blind, prospective, randomized trial. The primary endpoint was based on clinical data to identify the non-responders and was a composite of death, hospitalizations, lack of improvement in New York Heart Association class and/or in the 6 min walk distance. A total of 215 patients were enrolled and 182 were randomised to the imaging group or routine lead placement. Lead placement with multimodality imaging was associated with a lower proportion of non responders (26% vs. 42%, P = 0.02). No differences in LV volumes were found.15 The second study used cardiac magnetic resonance imaging to detect non-scarred myocardial areas and longitudinal myocardial strain imaging by speckle-tracking echocardiography to identify the area with the greatest dissynchrony. The primary endpoint was, in this case, a ≥15% reduction in LV end-systolic volume and was reached in 78% of the patients assigned to the imaging modality vs. 56% of those who underwent lead placement with the routine procedure.16 A nice editorial comment by Gorcsan puts these two studies in context. We still need to know how much imaging do we need to maximize the results of CRT.17 Thus, this month we are left with new evidence of the increasing burden of HF, data showing that echocardiography or biomarkers can be used for screening of patients with asymptomatic cardiac dysfunction, a new, very simple, prognostic marker for patients with HFpEF, the pulse pressure, a thorough review of the role of the left atrium in HF and two trials suggesting that multimodality imaging may help for lead positioning for CRT.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".