Prevalence of elevated hemoglobin and hematocrit levels in patients with obstructive sleep apnea and the impact of treatment with continuous positive airway pressure: a meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Obstructive sleep apnea (OSA) is reported to be a cause of secondary polycythemia. The present study (i) reviewed the literature reporting the prevalence of secondary polycythemia in patients with OSA and (ii) determined the effect of continuous positive airway pressure (CPAP) therapy on hemoglobin and hematocrit levels in patients with OSA. METHODS: We searched MEDLINE, Embase and Cochrane for studies of adult patients with OSA that reported hemoglobin and/or hematocrit levels. We performed summary estimates of (i) polycythemia prevalence and a subgroup analysis according to OSA severity, and (ii) change in hemoglobin and hematocrit levels following treatment with CPAP. RESULTS: Synthesis of seven studies including 3,654 patients revealed an overall polycythemia prevalence of 2% (95% CI 1-4%); 2% (95% CI 1-3%) in mild-to moderate and 6 % (95% CI 3-12%) in severe OSA. In the pooled analysis of ten single-arm trials including 434 patients, CPAP treatment reduced hemoglobin by 3.76 g/L (95% CI -4.73 to -2.80 g/L). Similarly, pooled analysis of ten single-arm trials including 356 patients without baseline polycythemia showed that CPAP treatment reduced hematocrit by 1.1% (95% CI -1.4 to -0.9%). CONCLUSION: Our pooled analysis supports an increased prevalence of secondary polycythemia in OSA. This estimated prevalence is likely underestimated due to the change in the polycythemia diagnostic criteria in 2016. Future randomized controlled trials are needed to evaluate the effect of CPAP in patients with baseline polycythemia. HIGHLIGHTS: Pooled analysis shows OSA is associated with an increased prevalence of secondary polycythemiaPrevalence of polycythemia is greater in severe OSACPAP treatment for OSA reduces both the hemoglobin and hematocrit.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.067 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".