Predicting poor outcome in COPD-heart failure overlap: The role of resting echocardiography and cardiopulmonary exercise testing
Bibliographic record
Abstract
Background: Heart failure (HF) carries a dismal prognosis in COPD. There is an unmet clinical need for validation of reliable non-invasive prognosticators in this fast-growing sub-population. We therefore investigated the utility of resting echocardiography and cardiopulmonary exercise tests in predicting a short-term negative outcome in COPD-HF overlap. Methods: After treatment optimization by a cardiologist and a respirologist, 30 patients (ejection fraction= 35.1 ± 6.5 %, FEV1= 62.4 ± 18.3 % pred) were followed-up for death or major cardiovascular events. Results: 14/30 (46.6 %) patients had a negative outcome after a median (IQR) follow-up of 359 (370) days. There was no significant association between left ventricular echocardiographic variables and a negative outcome (p>0.05). In contrast, event-positive patients had significantly lower right ventricular fractional area change (RVFAC= 40.6 ± 11.6 % vs. 49.0 ± 8.2 %; p<0.05). Among the exercise variables, event-positive patients had higher ventilatory inefficiency and lower peak end-tidal partial pressure for CO2 (PETCO2) (p<0.01). In fact, RVFAC and PETCO2 were the only independent predictors of a short term negative outcome (Figure). Conclusion: Resting echocardiographic and CPET variables suggesting RV-pulmonary vascular impairment reliably predicted short term clinical deterioration or death in COPD-HF overlap.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".