BMI, annual blood pressure measurements, and mortality in patients with obesity and hypertension
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: In-clinic blood pressure measurement is recommended annually for all hypertensive individuals, but can be difficult to perform in obese patients. We aimed to examine a population-based cohort of obese hypertensive patients to determine whether: (a) BMI was inversely associated with annual blood pressure measurement and (b) performing annual blood pressure measurements was associated with reduced mortality independent of BMI. PATIENTS/METHODS: We carried out a retrospective cohort analysis in 4972 obese hypertensive patients from UK primary care. Multivariable binary logistic regression was used to examine the association between obesity class (class 1=BMI 30-34.9; class 2=35.0-39.9; class 3=40 kg/m or greater) and annual blood pressure measurement. An accelerated failure time multivariable model was used to examine the association between annual blood pressure measurement and mortality. RESULTS: The mean age of the patients was 52.3 ± 9.6 years, the mean BMI was 34.1 ± 4.8 kg/m, and the median follow-up was 13.5 (interquartile range 6.8-20.3) years. A total of 519 (10.4%) patients died. Annual blood pressure measurements were performed in 1605 (47.0%) patients with class 1, 429 (40.2%) with class 2, and 198 (41.1%) with class 3 obesity (P<0.001). Compared with class 1, the covariate-adjusted odds of annual blood pressure measurement was 0.85 [95% confidence interval (CI) 0.73-0.98] for class 2 and 0.87 (95% CI 0.71-1.07) for class 3. Annual blood pressure measurement was associated with an 18.9% (95% CI 9.4-28.3) increase in survival. CONCLUSION: Less than 50% of obese hypertensive patients underwent annual blood pressure measurements and measurements were less frequent in severely obese patients. Annual blood pressure measurement was associated with increased survival. Strategies to improve measurement frequency in obese patients should be implemented.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".