Takotsubo Cardiomyopathy in Epilepsy: A Systematic insight into the Existing Literature from 2000-2023
Bibliographic record
Abstract
Background & Objective: Takotsubo cardiomyopathy (TCM), manifests as left ventricular dysfunction triggered by physical or emotional stress. It leads to higher morbidity in epileptic patients and can progress to complications. To find out the correlation between Takotsubo cardiomyopathy and epilepsy and to investigate pathophysiology and associated types of epilepsy. Methodology: This systematic review adhered to PRISMA guidelines and was sourced from the PubMed Central database. Search terms were pertinent to cardiomyopathy and epilepsy. Sixteen studies, comprising case reports and a case series were selected from 2000 to 2023 for data extraction. The quality evaluation was executed via the Joanna Briggs Institute Critical Appraisal checklist. Results: The review included 18 female patients with a mean age of 57.22 years. Predominant symptoms included tonic-clonic seizures (66.66%). Seizure-induced TCM pathophysiology implicates catecholamine surge, precipitating myocardial stunting and characteristic apical ballooning. Most patients had a history of epilepsy (38.88%). ECG findings showed tachycardia (38.88%) and ST-segment elevation (38.88%). Elevated troponin levels were noted in 83.33% of patients. Echocardiography showed reduced ejection fraction (72.22%), hypokinesia (38.88%) and akinesis (27.77%). Treatment involved Benzodiazepines (50%), Beta-blockers (61.11%) and Phenytoin (38.88%). The majority of patients showed improvement in echocardiography findings (55.55%) and ECG findings (11.11%). Conclusion: TCM in epilepsy patients evinces significant female predominance, with pathophysiology rooted in seizure-induced catecholamine surge. Early recognition in high-risk patients is essential in preventing complications. doi: https://doi.org/10.12669/pjms.40.12(PINS).11274 How to cite this: Qadri HM, Ismail F, Khawaja M, Sohail A, Bukhari S, Bashir A. Takotsubo Cardiomyopathy in Epilepsy: A Systematic insight into the Existing Literature from 2000-2023. Pak J Med Sci. 2024;40(12):S114-S125. doi: https://doi.org/10.12669/pjms.40.12(PINS).11274 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".