LO31: Identification of high risk factors associated with 30 day serious adverse events among syncope patients transported to the emergency department by emergency medical services
Bibliographic record
Abstract
Introduction: The majority of syncope patients transported to the emergency department (ED) by emergency medical services (EMS) are low-risk with very few suffering serious adverse events (SAE) within 30-days and over 50% are diagnosed with vasovagal syncope. These patients can potentially be diverted by EMS to alternate pathways of care (primary care or syncope clinic) if appropriately identified. We sought to identify high-risk factors associated with SAE within 30-days of ED disposition as a step towards developing an EMS clinical decision tool. Methods: We prospectively enrolled adult syncope patients who were transported to 5 academic EDs by EMS. We collected standardized variables at EMS presentation from history, clinical examination and investigations including ECG and ED disposition. We also collected concerning symptoms identified and EMS interventions. Adjudicated SAE included death, myocardial infarction, arrhythmia, structural heart disease, pulmonary embolism, hemorrhage and procedural interventions. Multivariable logistic regression was used for analysis. Results: 990 adult syncope patients (mean age 58.9 years, 54.9% females and 16.8% hospitalized) were enrolled with 137 (14.6%) patients suffering SAE within 30-days of ED disposition. Of 42 candidate predictors, we identified 5 predictors that were significantly associated with SAE on multivariable analysis: ECG abnormalities [OR=1.77; 95%CI 1.36-2.48] (non-sinus rhythm, high degree atrioventricular block, left bundle branch block, ST-T wave changes or Q waves), cardiac history [OR=2.87; 95%CI 1.86-4.41] (valvular or coronary heart disease, cardiomyopathy, congestive heart failure, arrhythmias or device insertions), EMS interventions or concerning symptoms [OR=4.88; 95%CI 3.13- 7.62], age >50 years [OR=3.18; 95%CI 1.68-6.02], any abnormal vital signs [OR=1.58; 95%CI 1.03-2.42] (any EMS systolic blood pressure >180 or <100 mmHg, heart rate <50 or >100/minute, respiratory rate >25/minute, oxygen saturation <91%). [C-statistic: 0.81; Hosmer Lemeshow p=0.30]. Conclusion: We identified high-risk factors that are associated with 30-day SAE among syncope patients transported to the ED by EMS. This will aid in the development of a clinical decision tool to identify low-risk patients for diversion to alternate pathways of care.
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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 | 0.000 | 0.000 |
| 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".