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Record W4405045474 · doi:10.1182/blood-2024-211916

Simplifying Eligibility Criteria for Clinical Trials in Immune Thrombocytopenia Using the Platelet Count Variability Index

2024· article· en· W4405045474 on OpenAlexaff
Donald M. Arnold, Yang Liu, Na Li, Madison Cranstone, Syed Mahamad, Ishac Nazy

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversity of CalgaryMcMaster University
Fundersnot available
KeywordsMedicineImmune thrombocytopeniaPlateletClinical trialInternal medicine

Abstract

fetched live from OpenAlex

Introduction Up to 15% of immune thrombocytopenia (ITP) patients are misdiagnosed because there is no unique ITP-specific biomarker and many syndromes can present with thrombocytopenia (Arnold et al, Blood Advances 2017). Errors in diagnosis can result in case mix and diluted treatment effects in clinical trials if non-ITP patients are permitted to enter. Clinical trial eligibility criteria that rely solely on the presence of severe thrombocytopenia may not adequately identify non-ITP patients. Conversely, the useful criterion of a previous response to ITP treatment is either too subjective and prone to recall bias, or too cumbersome to be feasible. The platelet count variability index (PVI) is a simple metric that quantifies platelet count fluctuations over time from 3 or more platelet count values. A high PVI can distinguish ITP patients from non-ITP patients independent of treatment. In this study, we modeled the performance of PVI as an eligibility criterion for ITP clinical trials. Methods Using consecutive patients with thrombocytopenia from the McMaster ITP registry, we determined the number of patients that were correctly identified as ITP based on the presence of severe thrombocytopenia plus a high PVI index, compared with patients with severe thrombocytopenia alone (worst-case scenario) or patients with severe thrombocytopenia plus a documented response to prior ITP treatment (best-case scenario). Patients were classified as ITP or non-ITP based on the clinical assessment at the last follow up in the registry. Severe thrombocytopenia was defined as an average platelet count <30 x109/L from 3 platelet counts in a 3-month period and no platelet count >35 x109/L. Previous response to treatment was defined as a platelet count >50 x 109/L within 4 weeks of starting corticosteroids, IVIG or Rh-immune globulin with a baseline platelet count <30 x109/L. We previously reported that the median (IQR) PVI for patients with definite ITP was 11.1 (9.7, 12.7) (Li et al, Blood Advances, 2021). In the context of improving clinical trial eligibility criteria, we prioritized a high positive predictive value (PPV) in our evaluation of PVI to minimize the risk of admitting non-ITP patients into an ITP trial. Results We identified 862 thrombocytopenic patients, of whom 501 (58.1%) had ITP and 361 (41.8%) had non-ITP. In the worst-case scenario, eligibility criteria that used severe thrombocytopenia alone identified 129 eligible patients, of whom 31 (24.0%) did not have ITP (PPV = 76.0%). In the best-case scenario, eligibility criteria that used severe thrombocytopenia plus a documented response to previous ITP treatment identified 42 eligible patients, of whom 2 (4.8%) did not have ITP (PPV = 95.2%). In comparison, our new strategy that used severe thrombocytopenia plus PVI >9.7 (25th quartile for definite ITP) identified 70 eligible patients, of whom 6 (8.6%) did not have ITP (PPV = 91.4%). Conclusion PVI is an objective metric that can be easily derived from a patient's previous platelet count values. A high PVI index was comparable to a previous response to ITP treatment in its ability to identify ITP patients and exclude non-ITP patients. PVI may be a useful eligibility criterion for ITP clinical trials.

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)0.158
metaresearch head score (Gemma)0.401
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.842
Threshold uncertainty score0.838

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1580.401
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0040.004
Science and technology studies0.0010.003
Scholarly communication0.0060.003
Open science0.0030.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.188
GPT teacher head0.501
Teacher spread0.313 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations0
Published2024
Admission routes1
Has abstractyes

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