Cardiac and Noncardiac Disease Burden and Treatment Effect of Sacubitril/Valsartan
Bibliographic record
Abstract
Background: The net clinical benefit of cardiac disease-modifying drugs might be influenced by the interaction of different domains of disease burden. We assessed the relative contribution of cardiac, comorbid, and demographic factors in heart failure (HF) and how their interplay might influence HF prognosis and efficacy of sacubitril/valsartan across the spectrum of left ventricular ejection fraction. Methods: We combined data from 2 global trials that evaluated the efficacy of sacubitril/valsartan compared with a renin-angiotensin antagonist in symptomatic HF patients (PARADIGM-HF [Prospective Comparison of Angiotensin Receptor Neprilysin Inhibitor With an Angiotensin-Converting Enzyme Inhibitor to Determine Impact on Global Mortality and Morbidity in Heart Failure; n=8399] and PARAGON-HF [Prospective Comparison of Angiotensin-Converting Enzyme Inhibitor With Angiotensin Receptors Blockers Global Outcomes in Heart Failure With Preserved Ejection Fraction; n=4796]). We decomposed the previously validated Meta-Analysis Global Group in Chronic Heart Failure risk score into cardiac (left ventricular ejection fraction, New York Heart Association class, blood pressure, time since HF diagnosis, HF medications), noncardiac comorbid (body mass index, creatinine, diabetes, chronic obstructive pulmonary disease, smoking), and demographic (age, gender) categories. Based on these domains, an index representing the balance of cardiac to noncardiac comorbid burden was created (cardiac-comorbid index). Clinical outcomes were time to first HF hospitalization or cardiovascular deaths and all-cause mortality. Results: Higher scores of the cardiac domain were observed in PARADIGM-HF (10 [7–13] versus 5 [3–6], P <0.001) and higher scores of the demographic domain in PARAGON-HF (10 [8–13] versus 5 [2–9], P <0.001). In PARADIGM-HF, the contribution of the cardiac domain to clinical outcomes was greater than the noncardiac domain ( P <0.001), while in PARAGON-HF the attributable risk of the comorbid and demographic categories predominated. Individual scores from each sub-domain were linearly associated with the risk of clinical outcomes ( P <0.001). Beneficial effects of sacubitril/valsartan were observed in patients with preponderance of cardiac over noncardiac comorbid burden (cardiac-comorbid index >5 points), suggesting a significant treatment effect modification (interaction P <0.05 for both outcomes). Conclusions: Domains of disease burden are clinically relevant features that influence the prognosis and treatment of patients with HF. The therapeutic benefits of sacubitril/valsartan vary according to the balance of components of disease burden, across different ranges of left ventricular ejection fraction.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".