Is the incidence of cerebral embolic events underestimated in Takotsubo cardiomyopathy?
Bibliographic record
Abstract
Abstract Introduction Although initially considered a benign cause of transient left ventricular dysfunction, Takotsubo cardiomyopathy (TCM or stress cardiomyopathy) has become an increasingly recognized cause of cerebral embolic events. Prior case series have suggested a 0.9% to 3% risk of ischemic stroke following the diagnosis of TCM. With the growing number of cases of stress cardiomyopathy reported since the COVID pandemic, we want to evaluate if the risk of stroke in the literature has been underestimated. Purpose In our study, we want to evaluate the risk of ischemic stroke one year following the diagnosis of Takotsubo cardiomyopathy in our university health centre. We also want to evaluate the time interval between the incidence of stroke and the initial diagnosis of TCM. Methods In this retrospective single-centre observational study, we collected data from 267 consecutive patients hospitalized for a diagnosis of Takotsubo cardiomyopathy (confirmed by echocardiography and cardiac catheterization) between May 2008 and December 2017 at our medical centre in Sherbrooke. All patients were followed for a minimum of one year. We excluded 14 patients with a prior history of stroke or a diagnosis of atrial fibrillation in the year following the diagnosis of TCM. We used a descriptive statistical analysis to evaluate the rate and time to event up to 1 year after the diagnosis. Results 253 patients were included in the study. The cohort was composed of 220 females (88% of the population), and the mean age was 69 years (SD: 11.8 years). Among this population, we identified 15 patients presenting with a stroke within a year after the diagnosis of stress cardiomyopathy, with a crude incidence of 5.9%. The median time interval between the incidence of stroke and the initial diagnosis of TCM was 17 days (2 to 298 days; an inter-quartile range of 3 to 105 days). The proportion of patients that reached the primary endpoint in the first 30 days was 67%. Conclusion According to our study, the risk of stroke in the year following the diagnosis of TCM is significantly higher than what is currently described in the literature. The fact that 10 of the 15 events occurred in the first 30 days may suggest a therappy design to lower the risk during that critical period could be explored. However, further studies are needed to address this issue.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".