Heart failure with preserved ejection fraction: health services implications of a stealth syndrome
Bibliographic record
Abstract
Heart failure (HF) is burdensome. What is recognized far less is that HF with preserved ejection fraction (HFPEF) is even more disruptive and justifies urgent attention to understand and improve outcomes. Why? Firstly, HFPEF is the fastest growing form of HF in high-income countries.1 For example, in the United States nearly 50% of current HF patients have HFPEF.1 Originally termed diastolic heart failure, these patients have a divergent left ventricular remodelling pattern (e.g. via increased wall thickness and concomitant decreased cavity size) compared with HF patients with reduced ejection fraction (HFREF). This results in wider variations in outcomes in HFPEF patients around exercise intolerance, morbidity and mortality.2,3 Compounding these variations, while medicines continue to improve the life expectancy and quality in patients with HFREF, such therapies do not usually have the same benefits for patients with HFPEF.2 As these patients also tend to be older women with hypertension and obesity,2 to reduce the future burden of HF and improve the prospects of the sizable population with HF, better understanding and treatment of HFPEF are now urgent priorities. Yet, surprisingly basic gaps still exist in addressing HFPEF and improving outcomes. Most clinical guidelines and research studies remain focused predominantly on patients with HFREF and/or do not differentiate between patients with HFPEF and HFREF.2 Uncertainty remains regarding what constitutes a ‘normal’ ejection fraction. HFPEF raises a number of complex challenges for nursing and health services research. Ongoing uncertainty over whether HFPEF worsens prognosis, exercise capacity and life quality creates the possibility that HFPEF is a common, but usually hidden, confounding factor in pharmacological and non-pharmacological trials. This has particularly troublesome consequences because HFPEF is more common in older adults and women – the two main populations which will contribute most to the burden of heart disease in coming decades.4 Patients with HFPEF are also likely to have more co-morbidities and, thus, arguably be in greater need of beneficial and efficient therapies and health services. Yet, these patient groups also tend to be excluded from or are less likely to participate in trials.2 Ensuring that study populations contain representative populations remains a challenging but important aim to support generalizability of results to vulnerable clinical populations. Trials, especially those examining the influence of exercise, should also include mechanistic measures of heart function2 to ensure that intervention effects are not only measured but also understood. Unless study populations are adequately described and the presence of HFPEF determined, variations in outcomes may not result from chance or any intervention being evaluated but from the prevalence of HFPEF in the population being studied. As is well established in evidence-based practice: populations, the nature of interventions and comparisons, and what outcomes are studied influence the effects of interventions. However, HFPEF is difficult to diagnose, systolic and diastolic dysfunction can co-exist in the same patients and some patients with HFPEF may not even be characterized as having HF. While non-pharmacological interventions to promote HF self-care are often poorly described in studies and meta-analyses,3,5 this suggests that the characteristics of the populations in studies are also an important but potentially neglected facet. While research into non-pharmacological interventions for HF patients continues apace, in many respects future research needs to go back to the ‘drawing board’ to address how we as healthcare providers, researchers, academics and scientists singularize HF. Future studies should examine patients with HFPEF as a distinct population and consider the aetiology of the disease, the underlying mechanisms to the disease, and therapies to improve health outcomes. The complexity and the breadth of issues involved in this endeavour epitomize the great challenges facing clinical and health services research. Working together across disciplinary boundaries will be essential. Further, the challenges set by HFPEF call for work that creates a shared understanding of HFPEF. This likely requires that interactions among researchers of different disciplines take place in the framing of research problems, the coordination of knowledge to and across fields of research and the undertaking of research and its analyses. High performing eclectic research teams will be necessary to achieve this and such teams should adopt a translational focus that addresses the generation and transfusion of knowledge across the basic sciences, physiology and diagnostics. Alberta HEART (http://albertaheartresearch.ca/) is a landmark project that brings together 22 scientists across the research and healthcare spectrum to develop new therapies for HFPEF. This team includes researchers who lead work in basic physiology, exercise science, nursing, cardiology, biomedical engineering and health services, practitioners who provide health care in five heart failure clinics and experts in knowledge translation. Over the past 15 years, nursing research has made remarkable advances across disciplines in increasing the perceived importance of psychosocial and behavioural aspects of HF, its self-care and non-pharmacological management. Many of these advances have featured in the pages of this journal. Our ability to understand how key socio-contextual factors such as sex, age and socioeconomic status influences HF, its care and outcomes continues to progress rapidly and can inform more personalized health services that respond to such factors.6 However, the quality of this work is constrained by the degree to which variables are adequately conceptualized and measured in past and ongoing studies, registries and other administrative data sets.6,7 Complex problems do not conform to arbitrary or organizational disciplinary boundaries. By coming together in new and innovative ways to systematically define, measure and then intervene to improve outcomes for patients with HFPEF, researchers can begin to discover the nature and solutions to this most pressing of syndromes. By studying HFPEF across its trajectory and the health continuum, we can improve our understanding, management and also prevention of this most severe yet stealthy syndrome. None declared. This work was supported by an Alberta Innovates – Health Solutions / AHFMR Team Grant via Alberta HEART, grant number AHFMR ITG 200801018.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.031 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".