Sleep-Disordered Breathing, Risk of Target Organ Injury, and Role of Obesity in Youth Referred for Hypertension Disorders
Bibliographic record
Abstract
Background: Sleep-disordered breathing (SDB) has adverse consequences on blood pressure regulation in adults, but its relationship with target organ injury (TOI), especially in youth, is less clear. Our objective was to determine if SDB is associated with higher risk for TOI in youth referred for hypertension disorders and if obesity magnified this risk. Methods: Interim cross-sectional analysis of baseline data from the multisite Study of the Epidemiology of Pediatric Hypertension Registry (SUPERHERO) which retrospectively collects electronic health records data using biomedical informatics scripts. Inclusion criteria were initial visit for hypertension disorder based on ICD-10 codes between 1/1/2015 and 12/31/2022 and age <19 years. Exclusion criteria were kidney transplant, dialysis, or pregnancy per ICD-10 codes at the index visit. Exposures were ICD-10 code-defined SDB, and obesity by body mass index percentile was our effect modifier. Outcomes were ICD-10 code-defined heart and kidney TOI at the index visit. We used unadjusted generalized linear models. Results: In this analysis of 11,580 participants, mean age was 12.0 ± 5 years, 52% had obesity, 4% has SDB, and 8 % had TOI (Table). Compared to participants without SDB, those with SDB had a 57% lower risk of TOI (RR 0.43, 95% CL 0.26 to 0.69). Obesity was associated with a lower magnitude of association, though not significantly (interaction p-value 0.08). Conclusions: In a large multisite registry of youth referred for hypertension disorders, participants with an ICD-10 code for SDB at baseline had a lower risk of TOI by ICD-10 code. Next steps include better defining our exposure and outcome. Further studies are needed to determine if clinical interventions that impact sleep health can mitigate future cardiovascular risk in youth. Funding: Other NIH Support - NIH K23-HL-148394, L40-HL148910, UL1-TR001420
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".