Association of QT dispersion with mortality and arrhythmic events—A meta‐analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: The risk stratification of coronary heart disease (CHD) and/or heart failure (HF) patients with easily measured electrocardiographic markers is of clinical importance. The aim of this meta-analysis is to indicate whether increased QT dispersion (QTd) is associated with fatal and nonfatal outcomes in patients with CHD and/or HF. METHODS: We systematically searched MEDLINE and Cochrane databases without restrictions until August 15, 2018 using the keyword "QT dispersion". Studies including data on the association between QTd and all-cause mortality, sudden cardiac death (SCD) or arrhythmic events in patients with HF and/or CHD were classified as eligible. RESULTS: In the analysis including patients with CHD and/or HF, we found that QTd did not differ significantly in patients with SCD compared to no SCD patients while QTd was significantly greater in the group of all-cause mortality patients and in patients who experienced a sustained ventricular arrhythmia. Subgroup analysis showed that in myocardial infarction studies, QTd was significantly higher in patients with an arrhythmic event compared to arrhythmic event-free patients while a nonsignificant difference was found in QTd in patients who died from any cause compared to survivors. Similarly, in HF patients, the QTd was significantly greater in patients with an arrhythmic event while a nonsignificant difference was found regarding all-cause mortality and SCD outcomes. CONCLUSIONS: QTd has a prognostic role for stratifying myocardial infarction or HF patients who are at higher risk of arrhythmic events. However, no prognostic role was found regarding all-cause mortality or SCD in this patient population.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.033 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".