The Value of P wave dispersion in predicting reperfusion and infarct related artery patency in acute anterior myocardial infarction
Bibliographic record
Abstract
PURPOSE: The aim of this study is to investigate whether P wave dispersion (PWD), measured before, during and after fibrinolytic therapy (FT,) is able to predict successful reperfusion and infarct related artery (IRA) patency in patients with acute anterior MI who received FT. METHODS: Sixty-eight patients who presented with acute anterior MI were enrolled in the study. An electrocardiogram was performed before and at 30, 60, 90 and 120 minutes after the start of FT. PWD was defined as the difference between maximum and minimum P wave duration on standard 12-lead surface electrocardiogram. A multivariate logistic regression model was used to assess whether PWD was predictor of IRA patency and ST-segment resolution (STR) on electrocardiogram. RESULTS: PWD120 was significantly lower in patients with STR on electrocardiogram (38 patients) compared with those without STR (30 patients) (44.8±11.5 vs. 52.9±10.3 ms; p < 0.001). PWD120 was found to be significantly lower in patients with patent IRA (31 patients) compared to those with occluded IRA (37 patients) (42.3±9.7 vs. 53.5±10.6 ms; p < 0.001). Logistic regression analysis revealed that PWD120 significantly predicted STR and IRA patency. A ≥51.6 ms PWD120 can predict an occluded IRA with a 87% sensitivity, ≥51 ms PWD120 can predict no reperfusion with a 74% sensitivity. CONCLUSION: PWD values, which were higher than 51 ms and 51.6 ms in patients who received fibrinolytic therapy, can serve as a marker of failed reperfusion and occluded IRA. PWD values, in combination with other reperfusion parameters, can contribute to the identification of rescue PCI candidates.
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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) | 0.000 | 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 | 0.000 |
| 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".