Cardio-Ankle Vascular Index and C-Reactive Protein Are Useful Parameters for Identification of Ischemic Heart Disease in Acute Heart Failure Patients
Bibliographic record
Abstract
BACKGROUND: The most common cause of heart failure (HF) is ischemic heart disease (IHD). Evaluation of IHD with non-invasive examinations is useful for the treatment of HF, and cardio-ankle vascular index (CAVI) is a good parameter for detecting systemic arteriosclerosis. However, the relationship between IHD and CAVI in acute HF (AHF) patients is still unclear. Therefore, we investigated the effect of non-invasive examinations, including CAVI to detect IHD. METHODS: We studied 53 consecutive patients (average age of 66.5 ± 10.9 years old, 36 males) with AHF from January 2009 to December 2012. These patients were classified into the IHD group (n = 19) and non-IHD group (n = 34) according to the coronary artery angiography results. We evaluated the vital signs, laboratory findings and CAVI. RESULTS: According to the laboratory findings, the C-reactive protein (CRP) in IHD group was significantly higher than non-IHD group (1.5 ± 2.1 mg/dL vs. 0.4 ± 0.4 mg/dL, P = 0.002). CAVI in IHD group was significantly higher than non-IHD group (9.58 ± 1.73 vs. 7.83 ± 1.86, P < 0.001). In the receiver operating characteristic (ROC) curve for discriminating the probability of IHD, the cut-off point of the CRP plus CAVI was 9.00. At that cut-off point, the sensitivity and the specificity were 69.7% and 89.5%, respectively. The mean area under the ROC curve (AUC) defined by the CRP plus CAVI was the greatest at all parameters. CONCLUSION: The CRP and CAVI were useful parameters for the identification of IHD in patients with AHF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".