Polysomnographic Predictors of Blood Pressure and Hypertension: Is One Index Best?
Bibliographic record
Abstract
STUDY OBJECTIVES: Numerous indexes derived from polysomnography are available to characterize sleep-disordered breathing, with no consensus over which measures best predict clinical outcomes. This study addresses the relative merits of using alternative polysomnography indexes by characterizing the consistency and strength of the association of each index with blood pressure and hypertension. DESIGN: Cross-sectional analyses of the association of alternative polysomnography indexes with blood pressure and hypertension were performed in construction and validation data sets. Linear and logistic regression models were used to identify the best variable sets. PATIENTS: Data were obtained from 6433 men and women (age 62.9 +/- 11.0 years, 52.8% women) who participated in the Sleep Heart Health Study. RESULTS: In multivariable models, most indexes showed weak linear associations with systolic, with slightly stronger associations for diastolic blood pressure, and the log odds of hypertension. No single index showed consistent superiority over others. Systolic blood pressure, diastolic blood pressure, and hypertension each were associated with distinct sets of polysomnography variables. Slightly more-consistent associations were demonstrated for indexes that included hypopneas that were linked with either a 3% or 4% desaturation level than indexes that did not require hypopneas to have linked desaturation. For indexes that combined apneas and hypopneas, there was no evidence that linking obstructive apneas to desaturation or arousal altered prediction compared with counting all apneas. CONCLUSION: In summary, using a rigorous cross-validation assessment, we did not identify a clear superiority of any single index for blood pressure or hypertension prediction. Detailed analyses of alternative definitions of the respiratory disturbance index support current scoring guidelines, where desaturation criteria are recommended for hypopneas but not apneas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".