May 2020 at a Glance: Ischaemic Heart Failure and Sex-Related Differences
Bibliographic record
Abstract
Almost half of patients with heart failure (HF) had an ischaemic aetiology.1 However, HF occurrence in patients with chronic coronary syndrome is unclear. During a 5-year follow-up, Parma et al.2 observed a 16.4% incidence of cardiovascular death, HF hospitalization and new-onset HF (primary endpoint) in patients with chronic coronary syndrome without history of HF. Hospitalization for HF and new-onset HF occurred in 2.8% and 11.6% of patients, respectively. Malik et al.3 showed that the risk of developing HF at 5 years after the diagnosis of diabetes is low in patients without ischaemic heart disease (IHD) or end-stage renal disease (ESRD) (4%), intermediate (8-12%) in diabetic subjects with IHD or ESRD, and high (20%) in those with diabetes complicated by both IHD and ESRD. Sama et al.4 performed a network analysis to recognize pathophysiological pathways distinguishing ischaemic from non-ischaemic HF. They identified six pathways, related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis, and five key network proteins, acid phosphatase 5, epidermal growth factor receptor, insulin-like growth factor binding protein-1, plasminogen activator urokinase receptor, and secreted phosphoprotein 1, as specific for ischaemic HF and potential targets for specific treatment. The role of coronary revascularization in asymptomatic patients with HF of ischaemic aetiology remains debated.5 A comprehensive review showing the pathophysiology and diagnosis of this condition, as well as the role of coronary revascularization, is reported in this issue.6 Thuijs et al.7 performed a pre-specified analysis from the Evaluation of XIENCE Versus Coronary Artery Bypass Surgery for Effectiveness of Left Main Revascularization (EXCEL) trial. They found no differences in the composite of death, stroke, or myocardial infarction at 3 years in patients stratified by left ventricular ejection fraction (LVEF) and treated by percutaneous vs. surgical revascularization of the left main coronary artery. However, patients with reduced LVEF had worse outcomes compared to the others. Whether indications for implantable cardioverter-defibrillator (ICD) therapy after acute myocardial infarction should be merely based on LVEF is debated.8 Docherty et al.9 developed and validated a risk model to predict the occurrence of sudden cardiac death early and up to 2 years after myocardial infarction. Including simple variables, this score could easily identify patients who may benefit from ICD therapy. Fudim et al.10 noted that 1-year mortality after ICD implantation, for primary or secondary prevention, increased with increasing age reaching an incidence of 32% in the oldest patients (median age 82 years). This confirms the importance of a careful selection of ICD candidates in order to avoid futility. Treatment of ischaemic HF with mesenchymal stem cells is an area of active research.11 Mathiesen et al.11 reported the long-term results of the Autologous Mesenchymal Stromal Cell Therapy in Heart Failure (MCS-HF) trial, a 2:1 randomized double-blind trial comparing intramyocardial injections of autologous mesenchymal stromal cells (MSCs) vs. placebo in patients with ischaemic HF. They found a significant reduction in left ventricular end-systolic volume in the treatment compared to the placebo arm. Also LVEF, stroke volume and myocardial mass improved after MCS injection. Studies exploring the efficacy of heterologous MCSs in ischaemic HF are ongoing.12 Sex-related differences in HF biomarkers are known.13 Suthahar et al.14 reviewed sex-specific differences and the interaction with obesity. Rossello et al.15 conducted a patient-level meta-analysis including three randomized trials comparing the effect of mineralocorticoid receptor antagonists (MRA) and placebo. Women differ from men in their clinical features and event rates. However, no sex-specific impact of MRA on cardiovascular mortality and HF hospitalization was noted. Interestingly, sex, and namely male sex, was an independent predictor of MRA non-use in a recent large survey.16
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.019 |
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".