Impact of Low Diastolic Blood Pressure on Risk of Cardiovascular Death in Elderly Patients With Coronary Artery Disease After Revascularization – The CREDO-Kyoto Registry Cohort-1 –
Bibliographic record
Abstract
BACKGROUND: We investigated the effects of age and low diastolic blood pressure (DBP) on cardiovascular death in patients with coronary artery disease (CAD) after coronary revascularization. METHODS AND RESULTS: Stable, chronic CAD patients after coronary revascularization in the CREDO-Kyoto registry cohort-1 were allocated to the Young (≤64 years, n=2,619), Young-Old (65-74 years, n=2,932), and Old-Old (≥75 years, n=1,629) groups. Kaplan-Meier analysis showed that the crude cumulative incidence of cardiovascular death was higher in Young-Old patients with DBP <70 mmHg (P<0.001) and in Old-Old patients with DBP <60 mmHg (P=0.017), but not <70 mmHg (P=0.629), compared with each counterpart. Low DBP did not increase cardiovascular death in young patients. After adjustments with independent predictors, DBP <60 mmHg did not increase the cardiovascular death in the Old-Old group (HR=1.579 [95% CI, 0.944-2.642], P=0.082) and DBP <70 mmHg remained a predictor in the Young-Old group (HR=1.665 [1.094-2.532], P=0.017). On multivariate stepwise Cox proportional hazard regression analysis, independent predictors for cardiovascular death in low DBP patients were creatinine clearance (CCr; inversely), prior cerebrovascular disease, and aortic disease in the Young-Old group and CCr (inversely) and malignancy in the Old-Old group. CONCLUSIONS: DBP <60 mmHg was not an independent factor for predicting cardiovascular death in Old-Old revascularized CAD patients, whereas DBP <70 mmHg remained a predictor in the Young-Old. (Circ J 2016; 80: 1232-1241).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".