Diagnostic Value of D-Dimer in Acute Myocardial Infarction Among Patients With Suspected Acute Coronary Syndrome
Bibliographic record
Abstract
Background: The role of D-dimer as a diagnostic marker in myocardial infarction (MI) and acute coronary syndrome (ACS) is still a question. The aim of this study was to evaluate the diagnostic value of D-dimer in the diagnosis of AMI in patients suspected with ACS. Methods: This cross-sectional study was conducted on patients suspected with ACS. Serial standard 12-lead electrocardiogram (ECG), D-dimer, and troponin tests were done for all the patients. According to the examinations, ECG changes, and troponin, patients were allocated into two groups of MI and unstable angina (UA). Chi-square, independent t -test, and Pearson correlation test were used by SPSS ver, 17. Cut-off point of D-dimer for MI diagnosis was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Seventy-five patients with a mean age of 63.1 ± 9.75 years were studied in two groups of MI (n = 34) and UA (n = 41). Patients were homogeneous based on age, gender, and risk factors for diabetes and dyslipidemia. D-dimer in patients with MI patients was higher than in patients with UA (P = 0.001). The optimal cut-off point of D-dimer for diagnosis of MI was 548 mEq/L with sensitivity and specifity of 63.4% and 91.2%, respectively. Conclusions: Based on the results of this study, it seems that the measurement of D-dimer serum level can be appropriate as a marker with high sensitivity and relatively high specificity for differentiating MI from UA in patients with suspected ACS. Cardiol Res. 2018;9(1):17-21 doi: https://doi.org/10.14740/cr620w Â
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".