The value of electrocardiography in prognosticating clinical deterioration and mortality in acute pulmonary embolism: A systematic review and meta‐analysis
Bibliographic record
Abstract
The role of electrocardiography (ECG) in prognosticating pulmonary embolism (PE) is increasingly recognized. ECG is quickly interpretable, noninvasive, inexpensive, and available in remote areas. We hypothesized that ECG can provide useful information about PE prognostication. We searched MEDLINE, EMBASE, Google Scholar, Web of Science, abstracts, conference proceedings, and reference lists through February 2017. Eligible studies used ECG to prognosticate for the main outcomes of death and clinical deterioration or escalation of therapy. Two authors independently selected studies; disagreement was resolved by consensus. Ad hoc piloted forms were used to extract data and assess risk of bias. We used a random‐effects model to pool relevant data in meta‐analysis with odds ratios (ORs) and 95% confidence intervals (CIs); all other data were synthesized qualitatively. Statistical heterogeneity was assessed using the I 2 value. We included 39 studies (9198 patients) in the systematic review. There was agreement in study selection (κ: 0.91, 95% CI: 0.86‐0.96). Most studies were retrospective; some did not appropriately control for confounders. ECG signs that were good predictors of a negative outcome included S1Q3T3 (OR: 3.38, 95% CI: 2.46‐4.66, P < 0.001), complete right bundle branch block (OR: 3.90, 95% CI: 2.46‐6.20, P < 0.001), T‐wave inversion (OR: 1.62, 95% CI: 1.19‐2.21, P = 0.002), right axis deviation (OR: 3.24, 95% CI: 1.86‐5.64, P < 0.001), and atrial fibrillation (OR: 1.96, 95% CI: 1.45‐2.67, P < 0.001) for in‐hospital mortality. Several ischemic patterns also were significantly predictive. Our conclusion is that ECG is potentially valuable in prognostication of acute PE.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".