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Record W4406396310 · doi:10.1161/svin.04.suppl_1.364

Abstract 364: Pediatric Case of COVID‐Related Serial Large Vessel Occlusions Treated with Repeated Mechanical Thrombectomy

2024· article· en· W4406396310 on OpenAlexaboutno aff
Ruchi Shah, M. Sheikh, R. Bartolina, Hugo Cuellar, H. Chokhawala, Aamir Siddiqui, Jamil A Ansari

Bibliographic record

VenueStroke Vascular and Interventional Neurology · 2024
Typearticle
Languageen
FieldMedicine
TopicVascular Procedures and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Physical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

Introduction Infection with the SARS‐CoV‐2 virus, leading to the disease entity Coronavirus disease 2019 (COVID‐19), may be associated with several systemic sequelae, including COVID‐19‐induced hypercoagulability. Such a state may induce thrombus formation and ensuing acute ischemic stroke (AIS) from a large vessel occlusion (LVO). Herein, mechanical thrombectomy (MT) has been shown to be a highly effective therapeutic option for eligible adults with LVO strokes. However, given the relative rarity of AIS due to LVO in pediatric patients, MT as a therapeutic modality is not well defined in this population. Materials/Methods Case report Results The present case is that of an 11‐year‐old male with an extensive contributory medical history presenting twice over a period of 5 months for multiple LVOs. The patient was born with congenital complete heart block, given maternal Sjogren's syndrome, and underwent epicardial pacemaker implantation at 1 month of age, with replacement thereof in 2016. Serial cardiological follow up in the history revealed possible pacemaker‐induced cardiomyopathy with resultant dilated cardiomyopathy necessitating medical management. The patient's first presentation for stroke was in 2/2023 with a classic left middle cerebral artery (L MCA) syndrome constituting a National Institute of Health Stroke Scale (NIHSS) of 16; computed tomography of the head (CTH) findings of L MCA infarction and Alberta Stroke Program Early CT Score (ASPECTS) of 9; and computed tomography angiography (CTA) findings of L M1 occlusion. Intravenous thrombolysis was withheld at this encounter and the patient underwent MT with Thrombolysis in Cerebral Infarction (TICI) 2C reperfusion. Further history revealed COVID‐19 infection one week prior and echocardiogram during admission revealed features of acute systolic heart failure, likely due to COVID‐19 induced myocarditis. The patient experienced significant symptomatic recovery over this hospital stay, being discharged home on warfarin 5mg daily and outpatient follow up. The patient presented again in 7/2023 with initial cortical symptoms and dilated right pupil quickly evolving to unresponsiveness, constituting an NIHSS of 23. Work up revealed an unremarkable CTH but with basilar artery occlusion on CTA. The patient received intravenous thrombolysis with alteplase and was subsequently taken for MT, with TICI 3 reperfusion. Echocardiogram at this admission revealed decreased ejection fraction and a lesion in the anterior free wall of the left ventricle suspicious for thrombus. Evaluation by physical/occupational therapy deemed the patient appropriate for inpatient physical rehabilitation, to where he was subsequently discharged with medical recommendations of aspirin and apixaban, the latter replacing his previous warfarin. Conclusion This is an extremely rare case of a pediatric patient developing recurrent LVO's, likely secondary to COVID infection, undergoing successful MT on both occasions. This case illustrates the potential for MT as a valuable treatment option in pediatric AIS. Despite the controversial aspect of pursuing multiple thrombectomies for treatment, especially given the rarity of AIS due to LVO in the pediatric population, this case provides an example of MT producing a favorable outcome, illustrating potential therapeutic viability.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.278
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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