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Comparison of Bleeding Tools in a Cohort of Pediatric Patients with ITP: Data from the Pediatric ITP Consortium of North America ICON1 Study

2016· article· en· W2605986624 on OpenAlexaff
Breakey R. Vicky, Rachael F. Grace, Carolyn M. Bennett, Jenny M. Despotovic, Jennifer Rothman, Yves Pastore, Ellis J. Neufeld, Robert J. Klaassen, Michele P. Lambert, James B. Bussel, George R. Buchanan, Melissa J. Rose, Amy E. Geddis, Kristin A. Shimano, Kerry Hege, Kristina M. Haley, Adonis Lorenzana, Alexis A. Thompson, Michael Jeng, Shelley E. Crary, Michelle Neier, Travis Brown, Peter Forbes, Cindy Neunert

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsChildren's Hospital of Eastern OntarioCentre Hospitalier Universitaire Sainte-JustineMcMaster University
Fundersnot available
KeywordsMedicineCohortImmune thrombocytopeniaPediatricsCohort studyObservational studyPlateletInternal medicine

Abstract

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Abstract Background: The indications for treating children with immune thrombocytopenia (ITP) remain controversial. A valid and reliable bleeding assessment tool could assist in objectively quantifying bleeding and influence treatment decisions. Both the ITP Bleeding Score (IBLS) and the ITP-Bleeding Assessment Tool (BAT) have been developed to assess bleeding severity in ITP. The IBLS scores bleeding severity using an 11-item tool with grading from 0 to 2 and the 18-item BAT grades from 0 to 3 or 4. The BAT includes some types of bleeding not represented on the IBLS, such has intramuscular hematomas. To date, no data describe how these two measures compare when measuring bleeding associated with ITP. Objective: To describe and compare bleeding as assessed by both the IBLS and BAT and to correlate bleeding severity with platelet counts in a cohort of children with ITP. Methods: A longitudinal observational cohort of children ages >1 and < 18 years with ITP, were enrolled from 2013-2015 in the Pediatric ITP Consortium of North America ICON1 trial. All children were enrolled prior to starting a new second line monotherapy (not IVIG, steroids or anti-D). At enrollment, bleeding was assessed using the IBLS in all children. A subset of children also underwent a BAT assessment. Grades of bleeding were described and compared between tools and agreement in grading was assessed. Severity was correlated with platelet count using Spearman's correlation calculation. Results: 118 children were enrolled from 21 ICON centers. 54% had chronic ITP and the median age was 11.4y (range 1.2-17.8). The mean platelet count was 28 x 109/l (SD 57) and 88% had a baseline platelet count of <30 x 109/l. The burden of skin and oral bleeding was high. Table 1 compares bleeding scores for the 78 patients with both measures. Agreement for grades 0, 1, and 2 between the measures was highest for urinary bleeding (97%), gastrointestinal (95%), subconjunctival (95%), and epistaxis (91%). Agreement between IBLS oral bleeding by historyand BAT gum bleeding was 78% and with BAT oral cavity bleeding was 79%. IBLS oral bleeding by physical examination and BAT gum bleeding was 73% and BAT oral cavity bleeding was 79%. The lowest agreement was seen for skin manifestations. IBLS skin bleeding by history showed only 54% and 62% agreement with the BAT ecchymoses and petechiae items respectively. IBLS skin bleeding by physical examination showed 59% agreement with both the BAT ecchymoses and petechiae items. Grades 3 and 4 scores from the BAT did not provide additional information beyond the IBLS for most sites of bleeding. For sites included on the BAT but not represented on the IBLS, only 1 child had an intramuscular hematoma, 1 suffered an ocular bleed, and none experienced hemarthrosis. Bleeding from minor wounds and bleeding with tooth loss captured additional bleeding symptoms in 9 and 4 children, respectively. There were no episodes of pulmonary or intracranial hemorrhage in the cohort. Table 2 shows the correlation between bleeding severity and platelet count for all items on each measure. Conclusion: The IBLS and BAT were similarly effective at identifying bleeding symptoms, although neither tool showed strong correlation with the platelet count. There were moderate correlations noted between skin bleeding scores and platelet counts for both tools. A major limitation of this comparison is the different definitions of bleeding severity between the two measures. For sites where the items were more detailed, agreement declined (i.e. adding specificity reduced generalizability). While no patients in our cohort exhibited significant grade 3 or 4 bleeding outside of skin findings, we conclude that in the setting of clinical trials, the ability to capture very severe bleeding might be an important distinction that supports using the more complicated BAT, but for clinical practice, a simplified assessment such as the IBLS may suffice. Disclosures Grace: Agios Pharmaceuticals: Other: Scientific Advisor, Research Funding. Neufeld:Novartis: Consultancy. Bussel:Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; UpToDate: Patents & Royalties; Protalex: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genzyme: Research Funding; Ligand: Membership on an entity's Board of Directors or advisory committees, Research Funding; Symphogen: Membership on an entity's Board of Directors or advisory committees; Rigel Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Shionogi: Membership on an entity's Board of Directors or advisory committees; Sysmex: Research Funding; Momenta Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Immunomedics: Research Funding; Eisai: Membership on an entity's Board of Directors or advisory committees, Research Funding; Boehringer Ingelheim: Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cangene: Research Funding; Prophylix Pharma: Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Physicians Education Resource: Speakers Bureau; BiologicTx: Research Funding. Haley:CSL Behring: Honoraria; Baxalta: Membership on an entity's Board of Directors or advisory committees. Thompson:Celgene: Research Funding; ApoPharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mast: Research Funding; Amgen: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Amgen: Research Funding; Baxalta (now part of Shire): Research Funding; bluebird bio: Consultancy, Research Funding; Baxalta (now part of Shire): Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; bluebird bio: Consultancy, Research Funding; Mast: Research Funding; ApoPharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Eli Lily: Research Funding; Eli Lily: Research Funding.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.039
GPT teacher head0.294
Teacher spread0.254 · 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 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
Published2016
Admission routes1
Has abstractyes

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