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Record W2906488389 · doi:10.1182/blood-2018-99-110650

Prospective Diagnosis of VWD in a Large Cohort of Patients with Bleeding Symptoms through the Zimmerman Program

2018· article· en· W2906488389 on OpenAlexaff
Veronica H. Flood, Pamela A. Christopherson, Joan Cox Gill, Kenneth D. Friedman, Sandra L. Haberichter, Jorge Di Paola, Paula D. James, Janna M. Journeycake, Steven R. Lentz, Cindy Leissinger, Margaret V. Ragni, Madhvi Rajpurkar, Jonathan Roberts, Amy D. Shapiro, Robert F. Sidonio, Robert R. Montgomery, Thomas C. Abshire

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsVon Willebrand diseaseMedicineProspective cohort studyPopulationInternal medicineCohortBleeding diathesisVon Willebrand factorPediatricsGastroenterologyPlatelet

Abstract

fetched live from OpenAlex

Abstract Background: Diagnosis of von Willebrand disease (VWD) is challenging in clinical practice due to variability in laboratory testing and clinical bleeding history. We investigated the prospective diagnosis of VWD in academic hematology clinics across the U.S. and Kingston, ON and report on the final cohort here. Methods: Subjects were enrolled as new consults to their hematologist for evaluation of a bleeding disorder from 11 centers. Laboratory results including VWF antigen (VWF:Ag) and VWF platelet binding activity were determined both locally (VWF ristocetin cofactor activity [VWF:RCo] or VWF:GPIbM) and centrally (VWF:GPIbM) to determine the comparative effectiveness in VWD diagnosis. Some centers defined type 1 VWD with levels <30 and low VWF as VWF:Ag and/or activity ≥30 but less than lower limit of normal while others used only type 1 VWD. Bleeding scores were obtained using the ISTH bleeding assessment tool. DNA sequencing of all exons including intron/exon boundaries was performed and variants classified as presumably pathogenic if present in <1% of the general population using human genetic variation databases. Results: A total of 1826 subjects undergoing evaluation for VWD or a suspected bleeding disorder were prospectively enrolled. Median age, demographics, and bleeding score did not differ between the VWD and non VWD groups. Since pediatric subjects comprised a large segment of our cohort, bleeding scores were examined for subjects ≥18 years old and showed no difference with a median of 7 for those with and without VWD (p=NS). Subjects with VWD had no significant difference in VWF:Ag between local and central lab testing. Differences in VWF platelet binding was likely due to most local labs using VWF:RCo vs central lab testing with VWF:GPIbM. The diagnosis of VWD was determined locally. A total of 36% of subjects received a diagnosis of VWD. Of those, 63% received a diagnosis of type 1 VWD, 27% low VWF and 10% other (type 2A, 2B, 2M, 2N, 3 or unclassified). The percent of subjects with VWD was consistent across centers. One subject was diagnosed as type 3 VWD. For type 2 VWD, 4% were 2A, 1% 2B, 2% 2M, and <1% 2N. Pathogenic genetic variants in VWF were most common (71%) in type 2 and 3 VWD subjects as compared to type 1 (45%) or low VWF (25%) cohorts. We then separated these subjects into 3 groups by diagnostic testing: 1) type 1 VWD (using criteria of VWF:Ag or activity <30 by either local or central lab), 2) low VWF or 3) non VWD. Type 2 VWD required VWF activity/VWF:Ag ratio <0.6, loss of high molecular weight multimers for types 2A and 2B and increased VWF platelet binding for type 2B. When both local and central lab results were taken into account and study definitions applied, only 29% of the enrolled subjects had abnormal VWF levels. Of these, 78% had low VWF, 15% type 1, and 7% type 2 VWD. Median bleeding score for both the low VWF and type 1 cohorts was 4 (p=NS). Type 2 subjects with confirmed phenotypic diagnoses had a median bleeding score of 5 (p=NS compared to the type 1 or low VWF cohorts). Discussion: This unique large prospective study of bleeding disorder subjects, designed as an inception cohort, highlights the diagnosis of VWD but presents several clinical challenges: 1) The lack of difference in bleeding scores between VWD and non VWD suggests that bleeding symptoms are common and possibly due to other diagnoses or not necessarily related to VWF levels. 2) The variability in VWD diagnosis with borderline levels is common when local and central lab testing are applied, potentially due to varying use of the VWF:RCo vs VWF:GPIbM to assess platelet binding activity. The comparative effectiveness of methods of diagnosis will be highlighted by followup testing on these subjects. 3) As only 1/3 of subjects merited a VWD or low VWF diagnosis, mild bleeding may be the result of other conditions; platelet defects, vascular disorders, collagen defects, other unidentified mechanisms or potentially non-pathogenic. We hypothesize that the presence of bleeding symptoms increases the likelihood that a diagnosis of VWD will be made, regardless of the level. This cohort will continue to be followed longitudinally which may help elucidate the importance of serial VWF measurement. However, the stability of VWD diagnosis across centers in 1/3 of subjects suggests a broader application. Disclosures Friedman: Shire: Membership on an entity's Board of Directors or advisory committees; CSL Behring: Membership on an entity's Board of Directors or advisory committees. James:Bayer: Research Funding; Shire: Research Funding; CSL Behring: Research Funding. Ragni:CSL Behring: Research Funding; Bioverativ: Consultancy, Research Funding; Shire: Research Funding; Biomarin: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sangamo: Research Funding; MOGAM: Membership on an entity's Board of Directors or advisory committees; Alnylam: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novo Nordisk: Research Funding; SPARK: Consultancy, Research Funding. Rajpurkar:HEMA biologics: Honoraria; Bristol Myers Squibb: Research Funding; Shire: Honoraria; Pfizer: Honoraria, Research Funding; Novonordisk: Honoraria. Shapiro:Shire: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Octapharma: Research Funding; Sangamo Biosciences: Consultancy; Bioverativ, a Sanofi Company: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genetech: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; OPKO: Research Funding; Bio Products Laboratory: Consultancy; Novo Nordisk: Membership on an entity's Board of Directors or advisory committees, Research Funding; BioMarin: Research Funding; Daiichi Sankyo: Research Funding; Bayer Healthcare: Other: International Network of Pediatric Hemophilia; Kedrion Biopharma: Consultancy, Research Funding; Prometic Life Sciences: Consultancy, Research Funding. Sidonio:genentech: Membership on an entity's Board of Directors or advisory committees, Research Funding; shire: Membership on an entity's Board of Directors or advisory committees, Research Funding; bioverativ: Membership on an entity's Board of Directors or advisory committees, Research Funding; octapharma: Membership on an entity's Board of Directors or advisory committees; grifols: Membership on an entity's Board of Directors or advisory committees, Research Funding; biomarin: Membership on an entity's Board of Directors or advisory committees; uniqure: Membership on an entity's Board of Directors or advisory committees; csl behring: Membership on an entity's Board of Directors or advisory committees; novo nordisk: Membership on an entity's Board of Directors or advisory committees. Montgomery:BCW: Patents & Royalties: GPIbM assay patent to the BloodCenter of Wisconsin. Abshire:CSL: Consultancy; Shire: Consultancy; Novo Nordisk: Other: DSMB.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.255
Teacher spread0.249 · 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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Citations1
Published2018
Admission routes1
Has abstractyes

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