July 2017 at a Glance: Clinical Assessment, Biomarkers, Cardiac Imaging and Treatment
Bibliographic record
Abstract
While the relation between absolute blood pressure (BP) and outcomes in patients with heart failure (HF) is relatively well established,1, 2 the prognostic significance of long-term BP changes has been assessed only in retrospective analyses of randomized clinical trials.3 The prognostic significance of long-term BP changes was assessed by Schmid et al. in a retrospective analysis of 927 HF patients followed at their centre for a median of 7.7 years.4 There were 220 deaths and 70 patients undergoing heart transplantation. Both baseline BP and long-term BP changes had independent prognostic value. The patients with the stable BP values during follow-up had the best prognosis. Either an increase or a decrease > ±10 mmHg per year was associated with reduced survival with hazard ratios of 1.8 and 2.0, respectively.4 The role of biomarkers is reviewed, altogether with that of cardiac imaging and treatment, with respect to the specific topic of organ injury and dysfunction in acute HF. This seems to be a major determinant of poor outcomes and a potential target for treatment. Biomarkers have allowed an accurate detection of organ injury and have shown its independent relation with the poor prognosis of patients with acute HF.5 Paraskevaidis et al. assessed the prognostic value of speckle tracking echocardiography, both at rest and during low-dose dobutamine infusion, in patients with HF and reduced ejection fraction (HFrEF).6 The only parameters that had an independent association with poorer outcomes, compared with traditional clinical and laboratory data, were a lower radial strain at rest and a smaller increase in global longitudinal strain (GLS) after low-dose dobutamine administration.6 These data must be added to the recent findings showing the value of GLS for the early detection of left ventricular (LV) systolic dysfunction in patients with normal LV ejection fraction (LVEF).7-9 Kloosterman et al. performed a meta-analysis and systematic review of all the studies evaluating the relation between contractile reserve, defined as a LVEF increase during dobutamine infusion, and the response to cardiac resynchronization therapy (CRT).10 The analysis included 757 patients, 66% with contractile reserve during dobutamine infusion and 63% with response to CRT. Contractile reserve was predictive of CRT response with an odds ratio of 4.42 (P<0.001).10 Taken together, these two studies show the importance of detecting a contractile reserve of dysfunctional but still viable myocardium in patients with HFrEF. These patients are more likely to respond to treatment and dobutamine echocardiography may be used for patient selection in clinical trials and treatment targeted at cardiac function.11 In addition to the study by Paraskevaidis et al.,6 GLS was assessed also in the study by DeVore et al., in this issue of the journal.12 In this second study, GLS was analysed in 187 patients enrolled in the RELAX trial of sildenafil treatment in patients with LVEF ≥50%. Baseline LV GLS was abnormal in 65% of the patients and correlated with increased NT-proBNP and collagen III N-terminal propeptide levels. No relation with symptoms and exercise capacity was found.12 Ghio et al. assessed the different determinants and the prognostic value of right ventricular (RV) dysfunction, assessed by the tricuspid annular plane systolic excursion (TAPSE) in 1663 patients with HF.2 Right ventricular dysfunction, assessed by TAPSE, normalized for the pulmonary artery systolic pressure (PASP) as the TAPSE/PASP ratio, was a powerful independent predictor of survival both in patients with HFrEF and in those with HF with mid-range (HFmrEF) and preserved ejection fraction (HFpEF). However, determinants of a low TAPSE were different in HFrEF versus HFpEF patients. Having HFrEF was associated with a more than three-fold increased risk of having a low TAPSE. Non-sinus rhythm, tachycardia, ischaemic aetiology and restrictive LV filling were the main determinants of RV dysfunction in HFrEF patients. In contrast, a high PASP was the main determinant of a low TAPSE in HFpEF and HFmrEF, but not in HFrEF patients.2 These data confirm the important prognostic significance of RV dysfunction and pulmonary hypertension also in HFpEF patients.13, 14 Increased right atrial pressure is a sensitive sign of RV dysfunction. Its clinical assessment is often difficult. In this issue of the journal, Pellicori et al. show the accuracy and clinical usefulness of a new non-invasive method to measure right atrial pressure through near-infrared spectroscopy in HF patients.15 Right ventricular dysfunction is a major cause of poor outcomes after LV assist device (LVAD) implantation but its main determinants are still partially unsettled.16 Bellavia et al. undertook a systematic review and meta-analysis of 36 observational studies of risk factors associated with RV dysfunction after LVAD implant.17 Among a pooled final population of 4428 patients, 35% developed post-LVAD RV failure. The variables with the strongest predictive value were the need for mechanical ventilation or continuous renal replacement therapy, higher INR and NT-proBNP values and signs of RV dysfunction such as a lower RV stroke work index, higher central venous pressure and signs of RV dysfunction, as assessed either qualitatively or as a greater RV/LV diameter ratio or by RV longitudinal systolic strain.17 The role of β-blocker selectivity and β-blocker dose was assessed by Paolillo et al. in 5242 patients from the large MECKI score database. It was confirmed the better survival of patients on β-blockers without, however, differences depending on the type of β-blocker administered. Patients receiving higher β-blocker doses had better outcomes.18
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".