January 2019 at a Glance: Prognostic Assessment, Left Ventricular Assist Devices, Disease Management and Quality of Care
Bibliographic record
Abstract
Tromp et al.1 prospectively studied the clinical characteristics, echocardiographic parameters and outcomes in 1204 patients with heart failure (HF) and preserved ejection fraction from different areas from Asia. Seventy per cent of patients had ≥ 2 co-morbidities, including hypertension (71%), anaemia (57%), chronic kidney disease (50%), diabetes (45%), coronary artery disease (29%), atrial fibrillation (29%) and obesity (26%). Southeast Asian patients had the highest prevalence of all co-morbidities and were at higher risk for adverse outcomes, independent of co-morbidity burden and cardiac geometry.1 N-terminal pro B-type natriuretic peptide (NT-proBNP) and troponin T (TnT) are the two main biomarkers for prognostic assessment of patients with HF and reduced ejection fraction (HFrEF). They were the only biomarkers having an independent prognostic value in some retrospective analyses.2 Diabetes is a major determinant of outcomes in patients with HF.3 Rørth et al.4 assessed the prognostic value of NT-proBNP and TnT in HFrEF patients with and without diabetes enrolled in PARADIGM-HF (Prospective Comparison of ARNI With ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure). NT-proBNP plasma levels did not differ between patients with and without diabetes. In contrast, TnT levels were higher in diabetic, compared with non-diabetic, patients. Both biomarkers had an independent prognostic value in either diabetic or non-diabetic patients.4 Abnormalities in the autonomic nervous system play a major role in the progression of HF.5 Paleczny et al.6 assessed the prognostic value of cardiac baroreflex sensitivity (BRS) in contemporary, optimally treated patients with HFrEF. BRS, assessed using three different methods, was not related to survival, irrespective of the method used. BRS, assessed by the phenilephrine method, correlated with several clinically important variables, including left ventricular ejection fraction.6 Patient-reported outcomes have gained major interest also as endpoints of clinical trials.7, 8 Luo et al.9 analysed the relation between the change in the Kansas City Cardiomyopathy Questionnaire (KCCQ), from baseline to 3 months, in 2038 patients undergoing exercise training in ACTION-HF (A Controlled Trial Investigating Outcomes of Exercise Training). Worsening health status was associated with increased all-cause mortality/hospitalization. An improvement in health status, up to an 8-point increase in KCCQ, was associated with decreased all-cause mortality/hospitalization. Additional improvements in health status beyond an 8-point increase in KCCQ were not associated with different outcomes.9 Multiple algorithms were elaborated to predict the effects of cardiac resynchronization therapy (CRT).10 Cikes et al.11 tested the hypothesis that a machine learning algorithm utilizing both complex echocardiographic data and clinical parameters could be used to predict the response to CRT in HFrEF patients. This algorithm was applied to 1106 HF patients from MADIT-CRT (Multicenter Automatic Defibrillator Implantation Trial with CRT). Patients were categorized into four mutually exclusive phenogroups based on similarities in clinical parameters, and left ventricular volume and deformation traces at baseline. Two of these phenogroups were associated with a substantially better treatment effect of CRT with defibrillation on the primary outcome.11 Left ventricular assist device (LVAD) implantation has a major role for the treatment of patients with advanced HF.12, 13 Schmitto et al.14 report the 2-year outcomes of 50 adults implanted with the HeartMate 3 LVAD and enrolled in the CE Mark Study.14 At 2 years, Kaplan–Meier survival was 74 ± 6%, 5 patients (10%) were transplanted, and 32 patients (64%) remained with support. Adverse event rates included bleeding requiring surgery (16%), gastrointestinal bleeding (20%), driveline infection (24%), ischaemic stroke (16%), haemorrhagic stroke (8%), right HF (14%), and outflow graft thrombosis (2%). Notably, no haemolysis, pump thrombosis, or pump malfunction events occurred. At 2 years, 47% of patients remained in New York Heart Association (NYHA) class I and 41% in NYHA class II.14 Medical treatment still has a major role also in patients with advanced HF and LVADs.13, 15 Exercise training has a major role, too. A position statement by the HF Association reviews current knowledge and gives practical recommendations about this topic.16 HF clinics with specialist-trained nurses remain scarce in primary care (PC) in Sweden. Liljeroos and Strömberg17 describe the results of the implementation of PC HF clinics in Sweden. Their introduction was associated with a reduced number of HF hospitalizations and HF emergency room visits as well as with an increased proportion of patients treated according to guidelines and satisfied of their care. Ferreira et al.18 compared the characteristics and outcomes of HF patients with worsening HF enrolled in BIOSTAT-CHF (BIOlogy Study to TAilored Treatment in Chronic Heart Failure) either as inpatients or as outpatients. Inpatients had higher rates of the primary outcome of death or HF hospitalization with a rate of 33.4 vs. 18.5 per 100 person-years. However, the primary outcome event rates were high also for outpatients: 8.4%, 29.8% and 43.3% in the low, intermediate, and high-risk categories, respectively. These findings suggest that also outpatients with worsening HF have poor prognosis and may be the focus of future trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".