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Record W4310119424 · doi:10.1182/blood-2022-170814

Efficacy and Safety of Fostamatinib in Elderly Patients with Immune Thrombocytopenia: A Single-Center, Real-World Case Series

2022· article· en· W4310119424 on OpenAlexaff
Jessica Chang Liu, Cyrus C. Hsia

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineSykImmune thrombocytopeniaInternal medicinePlateletTyrosine kinase

Abstract

fetched live from OpenAlex

Background: Fostamatinib is an oral small molecule spleen tyrosine kinase (Syk) inhibitor that was approved for treatment of adult patients with immune thrombocytopenia (ITP) in second-line therapy [1]. Syk inhibition prevents cytoskeletal rearrangements during phagocytosis allowing platelet survival in ITP. The efficacy of fostamatinib in adults with chronic ITP has been established in phase 3 clinical trials [2], and further studies have established fostamatinib as an effective second-line treatment [3]. However, fostamatinib treatment in elderly patients with ITP has not been well established. These individuals pose a particular challenge with a higher risk of bleeding [4], having multiple comorbidities, and may be at increased risk of toxicity from treatments such as fostamatinib. Methods: We performed a retrospective review of all elderly patients (age greater than or equal to 65 years) who had started on fostamatinib between June 1, 2021 and May 31, 2022 for the treatment of ITP at a single tertiary care centre to evaluate its efficacy and safety. Results: Seven patients, median age 80 years (range 78 - 94), four women and three men, all Caucasian background, with various comorbidities started fostamatinib 100mg oral twice daily as second or subsequent line therapy. Patients had a diagnosis of ITP for a median 6 years (range approximately 6 months - 30 years), had six comorbidities (range 2 - 14), and experienced 2 unique prior lines of ITP therapy (range 1 to 6). Over 1290 days of fostamatinib exposure, two patients required dose escalation to 150mg oral twice daily while five patients remained on initial starting dose of 100mg twice daily in partial or complete response. The median platelet count at time of initiating fostamatinib was 25 x 109/L (range less than 10 - 193). Median time to response (defined any first platelet count greater than or equal to 30 x 109/L) was 19 days (range 0 - 181 days) with two patients responding rapidly (5 days and 19 days). Two patients required dose escalation, rescue therapy, and these same two patients discontinued fostamatinib after 175 days and 216 days of treatment. Treatment was tolerated in all patients with no thromboembolic events observed. One death was noted and unrelated to treatment. In keeping with the current recommendations, dose reduction was not required in any of the patients despite their advanced age. Conclusions: Fostamatinib was effective and safe for the majority of our very elderly patients with ITP in second and subsequent line therapies over a cumulative 1290 days of exposure. References: 1. Neunert, C. et al. American Society of Hematology 2019 guidelines for immune thrombocytopenia. Blood Adv 3, 3829-3866 (2019). 2. Bussel, J. et al. Fostamatinib for the treatment of adult persistent and chronic immune thrombocytopenia: Results of two phase 3, randomized, placebo-controlled trials. Am J Hematol 93, 921-930 (2018). 3. Boccia, R. et al. Fostamatinib is an effective second-line therapy in patients with immune thrombocytopenia. Br J Haematol 190, 933-938 (2020). 4. Cortelazzo, S. et al. High Risk of Severe Bleeding in Aged Patients With Chronic Idiopathic Thrombocytopenic Purpura. Blood 77, 31-33 (1991). Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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 designCase report
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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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