Novel predictors of major adverse cardiovascular events in patients with Takotsubo cardiomyopathy: a contemporary analysis
Bibliographic record
Abstract
BACKGROUND: Takotsubo cardiomyopathy (TCM) is considered a reversible condition; however, it can predispose patients to significant long-term morbidity and mortality. As there is a paucity of literature regarding the long-term clinical outcomes in patients with TCM, we performed a systematic review and meta-analysis to evaluate the clinical, biochemical and imaging factors that are associated with major adverse cardiovascular events (MACE) in these patients. METHOD: A systematic search of databases following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines was utilised to identify studies that reported MACE and mortality in patients with TCM (PROSPERO CRD42025648547). Variable data were extracted and meta-analysed using random-effect modelling. Direct parameter comparisons were conducted using odds ratios (ORs) and standardised mean differences, while a pooled hazard ratio (HR) was meta-analysed for mortality. A two-tailed P-value of <0.05 was deemed significant. RESULTS: Of 320 studies screened, 19 met our inclusion criteria, comprising a total of 6443 patients. Predictors of MACE included a presentation with dyspnoea (OR, 1.41 (95% confidence interval (CI), 1.037-1.907), P = 0.028), Killip-Kimball classification of heart failure >1 (OR, 2.216 (95% CI, 1.673-2.936), P < 0.001) and secondary TCM (OR, 1.538 (95% CI, 1.051-2.251), P = 0.027). Right ventricular involvement (HR, 1.90 (95% CI, 1.14-3.16), P = 0.01) was identified as a predictor of mortality. Physical stressors and diabetes were predictors of both MACE and mortality (P < 0.05). Killip-Kimball class >1 was significantly stronger than dyspnoea (P 0.033 for interaction), while other novel predictors including secondary TCM, diabetes, physical stressors and right ventricular involvement demonstrated comparable effect sizes. CONCLUSION: This study outlines several novel risk factors for poor long-term outcomes in patients with TCM. Identification of these high-risk subpopulations early may allow for more intensive therapy and follow-up.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".