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Record W4408136131 · doi:10.7759/cureus.80002

Efficacy of Thromboprophylaxis in Preventing Thrombotic Events in Pediatric Patients With COVID-19 or Multisystem Inflammatory Syndrome: A Systematic Review

2025· review· en· W4408136131 on OpenAlexaboutno aff
Jaqueline L Castillo, Á. Sánchez, Daniel Felipe Patiño-Lugo, Natalia Núñez Muratalla, Juan Manuel Rodríguez Carrillo, Gabriela Sánchez, M. Torres, K. Christopher García, Laura Alfaro, Mauricio Montelongo Quevedo, Jose R Flores Valdés

Bibliographic record

VenueCureus · 2025
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Intensive care medicinePandemicCoronavirus InfectionsInternal medicineVirologyOutbreakDisease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has been associated with a broad spectrum of clinical manifestations, including multisystem inflammatory syndrome in children (MIS-C), a rare but serious condition characterized by a proinflammatory and hypercoagulable state. MIS-C has been linked to an elevated risk of venous thromboembolism (VTE), necessitating a focus on thromboprophylaxis to prevent potentially fatal complications in pediatric patients. This systematic review aims to evaluate the association between COVID-19/MIS-C and thromboembolism and to assess the efficacy of thromboprophylaxis protocols in reducing thrombotic events and mortality in children. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Literature searches were performed in PubMed, Cochrane, and Science Direct databases. Randomized controlled trials, cohort studies, and case-control studies reporting on thromboprophylaxis, thrombotic events, and associated outcomes in pediatric patients (<21 years) with COVID-19 and/or MIS-C were included. The Newcastle-Ottawa Scale was used to assess the quality of included studies. Primary outcomes were the incidence of thrombotic events and mortality, while secondary outcomes included bleeding events, clinical recovery, and changes in coagulation markers. Of the 375 articles identified, three studies (n=771 patients) met the inclusion criteria. Thromboprophylaxis protocols primarily included low molecular weight heparin (LMWH) such as enoxaparin and antiplatelet agents such as aspirin, with varied doses and treatment durations. Thrombotic events were reported in 3.3% of patients, with a higher incidence in MIS-C cases compared to COVID-19 alone. Prophylactic anticoagulation was effective in preventing thrombotic events in high-risk patients without increasing the risk of major bleeding. The studies emphasized individualized treatment approaches based on risk factors such as elevated D-dimer levels, obesity, prolonged immobilization, and central venous catheter presence. All studies reported a low mortality rate, ranging from 0% to 2.2%, highlighting the potential benefit of thromboprophylaxis in this population. Pediatric patients with MIS-C or severe COVID-19 are at an increased risk of thrombotic complications due to their heightened proinflammatory and hypercoagulable states. Thromboprophylaxis using enoxaparin and aspirin appears effective in reducing thrombotic events and mortality in these patients. Individualized protocols based on clinical risk factors and D-dimer levels are critical to optimizing outcomes while minimizing bleeding risks. Standardized, evidence-based guidelines are needed to refine thromboprophylaxis strategies and determine the optimal duration of therapy in this vulnerable population. Further research is essential to better understand the role of coagulation markers in guiding treatment cessation and improving outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.436
Teacher spread0.377 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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