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Use of Anticoagulation in Patients with Immune Thrombocytopenia

2017· article· en· W2771490898 on OpenAlexaffabout
Amaris Balitsky, John G. Kelton, Ishac Nazy, Brandon Aubie, Rumi Clare, Donald M. Arnold

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineImmune thrombocytopeniaInternal medicineHematologyThrombosisProspective cohort studyPlateletPediatrics

Abstract

fetched live from OpenAlex

Introduction : There is little evidence to guide practice on the use of anticoagulation (AC) for patients with immune thrombocytopenia (ITP). In this study, we describe management of AC and clinical outcomes in patients with ITP who had a platelet count 9 /L while receiving AC. Methods : Patients were identified from the McMaster ITP Registry, a prospective registry of adult patients with thrombocytopenia ( 9 /L) referred to a tertiary hematology clinic in Canada. Patients who had a platelet count 9 /L and who were receiving AC at the same time were selected for this study. A detailed chart review was done to augment the clinical data from the registry. For every patient, we defined 9 at-risk encounters 9 as any occurrence of platelets 9 /L while simultaneously on AC. For each encounter, we described management decisions to either stop or continue AC; assessed bleeding events using a validated ITP bleeding tool, which graded the severity of bleeding from 0 (none) to 2 (severe); and recorded thrombotic events. This study was approved by the Hamilton Integrated Research Ethics Board. Results : Of 615 patients from the registry, 44 received AC at some point. Of those, 13 patients with ITP had platelets 9 /L while simultaneously on AC (n=44 encounters). Indications for AC were atrial fibrillation (n=6) or venous thrombosis (n=7). Median age was 74 years and 53.8% were female. Median follow up was 9 months (IQR 22 months). Of all encounters with AC management data available (n=35 encounters), 7 (20.0%) were associated with grade 2 bleeding, 6 (17.1%) were associated with grade 1 bleeding, and 20 (57.1%) were not associated with bleeding (2 were unknown). AC was stopped during 26 (74.3%) encounters; median platelet count was 15 x10 9 /L. Of those encounters where AC was stopped, 20 (76.9%) resulted in administration of ITP therapies to raise the platelet count; 7 (26.9%) were associated with a subsequent thrombotic event; and none were associated with subsequent grade 2 bleeds. There were 2 deaths among patients who stopped AC: 1) A 74 year-old female who had been receiving AC for atrial fibrillation (CHADS2 = 2) presented with a platelet count of 1 x10 9 /L and a grade 2 bleed. Ten days after stopping AC and receiving ITP treatments, repeat platelet count was 55 x10 9 /L and she developed a massive ischemic stroke resulting in death. 2) A 49 year-old male who had been receiving AC for recurrent deep vein thromboses had 3 encounters with platelets 9 /L and a grade 2 bleed. Two years after stopping AC and receiving multiple ITP treatments (repeat platelet count = 167 x10 9 /L), he developed sepsis and ultimately died of presumed pulmonary embolism. AC was continued during 9 (25.7%) encounters (median platelet count, 38 x10 9 /L). Of those, 5 (55.6%) resulted in the use of ITP therapies. There were no subsequent grade 2 bleeds. For encounters with platelets 9 /L (n=12), 11 stopped AC and there were 2 thrombotic events; AC was continued for 1 patient without any subsequent grade 2 bleeds. Conclusions : For patients with ITP who had platelets 9 /L and who were on AC, grade 2 bleeding events occurred during 20% of encounters. When AC was held, 27% of encounters resulted in thrombotic events; when AC was continued, no subsequent grade 2 bleeds were reported. These results suggest that for some patients with ITP, stopping AC can result in more harmful outcomes than continuing AC. Further studies are needed to define safe platelet count thresholds for AC in patients with ITP. Disclosures Arnold: Novartis: Consultancy, Research Funding; UCB: Consultancy; Dova: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers Squibb: Research Funding; Rigel: Consultancy.

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)0.004
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.260
Teacher spread0.235 · 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".

Quick stats

Citations2
Published2017
Admission routes2
Has abstractyes

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