Impact of Thrombophilia On Arterial Ischemic Stroke or Cerebral Venous Sinus Thromboses in Children: A Systematic Review & Meta-Analysis of Observational Studies.
Bibliographic record
Abstract
Abstract Abstract 3993 Poster Board III-929 Background The incidence of stroke in children is estimated at about 2.6 per 100,000 per year. Risk factors include congenital heart malformations, trauma, hemolytic anemias, collagen tissue diseases, inborn metabolic disorders, and infectious diseases. Apart from acquired thrombophilic risk factors, such as the presence of antiphospholipid antibodies, inherited thrombophilias (IT) have been found to be associated with stroke in infants and children. However, results of single studies on the risk of stroke onset associated with IT have been contradictory or inconclusive, mainly due to lack of statistical power. The aim of this study was to estimate the impact of thrombophilia (IT) on risk of childhood stroke via meta-analysis of published observational studies. Methods and Results A systematic search of electronic databases (Medline, EMBASE, OVID, Web of Science, The Cochrane Library) for studies published from 1970 to 2009 was conducted using key words in combination both as MeSH terms and text words. Citations were independently screened by two authors and those meeting the a priori defined inclusion criteria were retained. Data on year of publication, study design, country of origin, number of patients/controls, ethnicity, stroke type (arterial ischemic stroke [AIS]; cerebral venous sinus thrombosis [CSVT]) were abstracted. Publication bias indicator and heterogeneity across studies were evaluated, and summary odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using both fixed-effects and random-effects models. Twenty-one of 185 references found met inclusion criteria. 1698 patients (AIS: 1291; CSVT: 407) and 2913 controls aged neonate to 18 years were enrolled. No significant heterogeneity was discerned across studies, and no publication bias was detected. A statistically significant association with stroke onset was demonstrated for each IT trait evaluated, with no difference found between AIS (table) and CSVT. Summary ORs/CIs (random-effects model) for AIS & CSVT cohorts were as follows: Protein C-deficiency (8.76/4.53-16.96), FV G1691A (3.34/2.66-4.26), FII G20210A (2.50/1.67-3.74), MTHFR T677T (1.61/1.21-2.14), antiphospholipid antibodies (5.84/3.06-11.18), elevated lipoprotein (a) (6.24/4.51-8.64), and combined ITs (8.85/3.32-23.57). Carrier rates reported for antithrombin- or protein S deficiency among patients were 1.5% and 1.6% as compared with 0.06% (p<0.001) and 0.4% (p=0.003) in healthy controls. Conclusions The present meta-analysis indicates that IT serve as risk factors for incident stroke. However, the impact of IT upon outcome and recurrence risk needs to be further investigated. Disclosures: Manco-Johnson: Baxter BioScience: Honoraria; Bayer HealthCare: Honoraria; CSL Behring: Honoraria; NovoNordisk: Honoraria; Octapharma: Honoraria. Off Label Use: Enoxaparin (LMWH) is used off-label in children to prevent symptomatic thromboembolism.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".