Differing Effects of Obstructive and Central Sleep Apneas on Stroke Volume in Patients with Heart Failure
Bibliographic record
Abstract
RATIONALE: Obstructive sleep apnea and central sleep apnea increase risk of mortality in patients with heart failure (HF), possibly because of hemodynamic compromise during sleep. However, beat-to-beat stroke volume (SV) has not been assessed in response to obstructive and central events during sleep in patients with HF. Because obstructive events generate negative intrathoracic pressure that reduces left ventricular (LV) preload and increases afterload, but central events do not, obstructive events should lead to greater hemodynamic compromise than central events. OBJECTIVES: To determine the effects of obstructive and central apneas and hypopneas during sleep on SV in patients with HF. METHODS: Patients with systolic HF (LV ejection fraction ≤ 45%) and sleep apnea underwent beat-to-beat measurement of SV by digital photoplethysmography during polysomnography. Change in SV from before to the end of obstructive and central respiratory events was calculated and compared between these types of events. MEASUREMENTS AND MAIN RESULTS: Changes in SV were assessed during 252 obstructive and 148 central respiratory events in 40 patients with HF. Whereas SV decreased by 6.8 (±8.7)% during obstructive events, it increased by 2.6 (±5.4)% during central events (P < 0.001 for difference). For obstructive events, reduction in SV was associated independently with LV ejection fraction, duration of respiratory events, and degree of oxygen desaturation. CONCLUSIONS: In patients with HF, obstructive and central respiratory events have opposite hemodynamic effects: whereas obstructive sleep apnea appears to have an adverse effect on SV, central sleep apnea appears to have little or slightly positive effects on SV. These observations may have implications for therapeutic approaches to these two breathing disturbances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".