Abstract 12418: Prevalence and Clinical Characteristics of Patients With Decompensated Heart Failure and Clonal Haematopoiesis of Indeterminate Potential
Bibliographic record
Abstract
Introduction: CHIP is associated with inflammation, atherogenesis and poor outcomes in patients with HFrEF. Although inflammation may be important in the pathophysiology of HFpEF, CHIP has not previously been assessed in patients with HFpEF. We examined the prevalence of CHIP and its associated clinical characteristics in patients with HFpEF or HFrEF. Methods: Error-corrected targeting sequencing of 75 known haematopoietic driver genes was performed on peripheral blood DNA from patients admitted to hospital with acute HF (HFrEF [LVEF<40%] and HFpEF [LVEF≥40%]). CHIP mutations and their associated clonal population size were analysed and associations with clinical phenotypes and inflammatory markers were assessed. Results: 96 patients were enrolled; 48 had HFrEF and 48 had HFpEF. CHIP mutations with VAF ≥2% were detected in 5 patients with HFrEF (10%) and 8 patients with HFpEF (17%). CHIP mutations with VAF ≥1% were detected in 25 patients with HFrEF (52%) and 21 patients with HFpEF (44%). HFrEF patients with CHIP were a similar age to those without CHIP (71.1 ± 14.2yrs CHIP vs 68.9 ± 13.0yrs without CHIP; p=0.58) but HFpEF patients with CHIP were older than those without CHIP (80.1 ± 8.8yrs CHIP vs 70.0 ± 12.3yrs no CHIP; p=0.002). There was an age-dependent rise in CHIP prevalence which appeared greater in patients with HFpEF than HFrEF. DNMT3A was the most commonly mutated gene. Patients with CHIP had higher concentrations of pro-inflammatory interleukins but CHIP was not associated with previous MI or NT-proBNP. Conclusions: There is a substantial prevalence of CHIP in patients admitted to hospital with HF. In this cohort, CHIP was uncommon in patients with HFpEF under the age of 70 years but was frequent in older patients. The association between age and CHIP was stronger in HFpEF than HFrEF. CHIP was associated with inflammation. Its potential role in the pathogenesis of HF warrants further exploration in larger groups, including in elderly patients with HFpEF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".