Predictors of cardiovascular events in a contemporary population with impaired glucose tolerance: an observational analysis of the Nateglinide and Valsartan in impaired glucose tolerance outcomes research (NAVIGATOR) trial
Bibliographic record
Abstract
OBJECTIVES: Risk factors for cardiovascular events are well established in general populations and those with diabetes but have been sparsely studied in impaired glucose tolerance (IGT). We sought to identify predictors of (1) a composite cardiovascular outcome (cardiovascular death, non-fatal myocardial infarction and non-fatal stroke) and (2) cardiovascular death, among patients with IGT. DESIGN: We studied participants enrolled in the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial. Predictors of cardiovascular events were identified in observational analyses. SETTING: Clinical trial participants in 40 countries. PARTICIPANTS: 9306 participants with biochemically confirmed IGT at high risk of cardiovascular events participated in NAVIGATOR. PRIMARY AND SECONDARY OUTCOME MEASURES: Cox proportional hazard regression models were constructed using variables (demographic data, medical history, clinical features, biochemical results and ECG findings) recorded at baseline to identify variables associated with and predictive of cardiovascular events. RESULTS: Over 6.4 years, 639 (6.9%) participants experienced a cardiovascular event, and 244 (2.6%) cardiovascular death. While predictors of both outcomes included established risk factors such as existing cardiovascular disease, male gender, older age, current smoking status and higher low-density lipoprotein cholesterol, other variables such as reduced estimated glomerular filtration rate, previous thromboembolic disease, atrial fibrillation, higher urinary albumin/creatinine ratio and chronic obstructive pulmonary disease were also important predictors. Glycaemic measures were not predictive of cardiovascular events. c-Statistics for predicting cardiovascular events and cardiovascular death were 0.74 and 0.82, respectively. This compares with c-statistics for cardiovascular events and cardiovascular death of 0.65 and 0.71, respectively, using the classical Framingham risk factors of age, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension and smoking status. CONCLUSIONS: The most powerful independent predictors of cardiovascular events in IGT included both established risk factors and other variables excluding measures of glycaemia, allowing effective identification of high-risk individuals.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".