Abstract WP187: Takotsubo Cardiomyopathy And Subsequent Risk Of Major Adverse Cardiovascular Events: A Cluster Analysis
Bibliographic record
Abstract
Introduction: Takotsubo Cardiomyopathy (TCM) is a form of transient regional left ventricular dysfunction. The risk of MACE after TCM is poorly understood but appears to be higher. Predictors of cardiovascular (CV) events remain unclear. Methods: Retrospective study of patients admitted to London Health Sciences Centre hospitals with TCM between 2014 and 2021. Clinical data were collected. MACE during follow-up (FU) included stroke, acute coronary syndrome, admission for heart failure or CV death. Outcomes were assessed until the end of FU. A two-step cluster analysis was used to stratify patients' phenotypes. Variables for clustering included: TCM aetiology, age, sex, hypertension, dyslipidemia, smoking, previous stroke, diabetes, chronic kidney disease, ejection fraction, atrial fibrillation (AF), and antithrombotics. A Cox proportional-hazards model was used to determine the association between clusters and MACE. Results: We included 152 patients (88.8% females). Median age was 72.5 [65.5-80.0] years and aetiologies were emotional stress (31.6%), neurogenic (6.6%), acute medical conditions (23.7%), and unknown (38.2%). Overall, 80% received antithrombotic drugs post-TCM. At a median FU of 22.0 [8.2-44.5] months, 11.8% of the patients experienced a MACE. Cluster 1 (n=41) included more male patients (31.7%), and the highest rate of hypertension (100%), dyslipidemia (92.7%), smoking (31.7%), previous stroke (14.6%), and antiplatelet use (82.9%). Cluster 2 (n=21) included older patients (78 [73.5-84.5] years) with the highest prevalence of diabetes (23.8%), AF (95.2%) and anticoagulation (71.4%). Cluster 3 (n=90) included younger patients (69 [62.7-84.0] years) with the lowest burden of CV risk factors (CHA 2 DS 2 VASc 2.0 [1.0-2.2]) and antithrombotic therapy (68.9%). Cluster 1 was associated with increased risk of MACE (OR 2.9; 95%CI 1.1-7.3; p=0.02) and stroke (OR 4.9; 95%CI 1.1-20.6; p=0.02). Conclusion: CV events are relatively frequent after a diagnosis of TCM, affecting over one-tenth of patients. We identified a high-risk phenotype, predominantly characterized by male sex, a higher burden of CV comorbidities, and low anticoagulation rates, which was associated with an increased risk of MACE and stroke after TCM.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".