Effects of individual risk factors on the incidence of cardiovascular events in the treated hypertensive patients of the Hypertension Optimal Treatment Study
Bibliographic record
Abstract
BACKGROUND: The Hypertension Optimal Treatment (HOT) Study has provided information about cardiovascular events in 18790 hypertensives, subjected to pronounced blood pressure (BP) lowering for a mean of 3.8 years. The HOT study data have subsequently been analysed after stratification of the patients according to global cardiovascular risk, and it has been found that, despite intensive blood pressure lowering in all risk strata, morbid event rates increased with increasing risk stratum. OBJECTIVES: Previously analysed global risk strata were based on combinations of risk factors. The analyses presented here were intended to provide information on the relative role that the presence of each individual factor may have in increasing cardiovascular risk, despite good BP control. METHODS: Risk ratios (RR) for patients with and those without a risk factor were calculated with 95% confidence intervals (CI) using a Cox proportional hazard model, and adjusted for all variables except the one under examination. RESULTS: For all risk factors considered and for all types of event, RR were always greater than 1, indicating a greater risk in the presence, compared with that in the absence of each factor. The male gender was a statistically significant risk for cardiovascular (CV) events, CV and total mortality and particularly for myocardial infarction (MI); age > or = 65 years for CV events, stroke, CV and particularly total mortality; smoking for all events analysed, but particularly for total mortality (twice higher in smokers than in non-smokers); high serum cholesterol (> 6.8 mmol/l) for CV events, MI and CV mortality; high serum creatinine (> 155 micromol/l) for CV events, stroke, CV and total mortality; diabetes for CV events, stroke, total mortality and particularly CV mortality; and ischaemic heart disease for all events analysed. Adjusted RR were often close to or greater than 2. CONCLUSIONS: Each of the risk factors considered was found to be an important cause of residual risk, despite good BP control. These findings emphasize the importance of addressing other correctable risk factors, e.g. smoking, hypercholesterolaemia and diabetes, as well as rigorous control of blood pressure, and of initiating antihypertensive therapy before cardiovascular and renal damage becomes manifest.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".