Accuracy of Left Bundle Branch Block Chronology and Electrocardiography Criteria for Acute Myocardial Infarction Diagnosis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background The diagnostic utility of new or presumed new left bundle branch block (LBBB) for acute myocardial infarction (AMI) in the setting of acute coronary syndrome (ACS) remains controversial. Objective To evaluate whether the timing of LBBB predicts AMI and to compare its diagnostic accuracy with ischemic electrocardiography (ECG) criteria, particularly the Modified Sgarbossa Criteria (MSC). Methods We searched PubMed and Scopus for studies involving patients with ACS with LBBB through December 2023. Sensitivity, specificity, positive (LR+) and negative (LR–) likelihood ratios, and diagnostic odds ratios (DOR) were calculated to assess diagnostic accuracy. Incidence and mortality data were also analyzed. Risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS) and the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results A total of 51 studies were included. LBBB occurred in 3.3% of ACS presentations and was associated with higher in-hospital mortality. Differentiating new from old LBBB was diagnostically neutral: LR+ 1.30 (95% CI: 0.75 to 1.85), LR– 0.90 (95% CI: 0.79 to 1.02), and DOR 1.44 (95% CI: 0.93 to 2.24); all confidence intervals crossed the null value of 1.0. In contrast, MSC demonstrated 83.6% sensitivity (95% CI: 55.4 to 95.5%) and 92.6% specificity (95% CI: 78.9 to 97.7%) for angiographically confirmed occlusive AMI, with LR+ 11.34 (95% CI: 3.67 to 34.99) and LR– 0.18 (95% CI: 0.054 to 0.575). Conclusion LBBB chronology alone does not significantly impact the likelihood of AMI. Ischemic ECG criteria — especially the MSC — provide substantially greater diagnostic accuracy and should guide clinical decision-making in ACS patients with LBBB.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".