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Record W4405041602 · doi:10.1182/blood-2024-204908

Incidence, Time to Diagnosis, and Therapeutic Landscape of Aplastic Anemia: Results from a Large United States Claims Database

2024· article· en· W4405041602 on OpenAlexaff
Phillip Scheinberg, Tim H. Brümmendorf, Régis Peffault de Latour, Carlo Dufour, Raj Gokani, Morag Griffin, J. Hey‐Hadavi, Yoshinobu Kanda, Barbara Możejko-Pastewka, Andres Quintero, Meltem Kurt Yüksel, Alicia Rovó

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsPfizer (Canada)Merck Canada Inc. (Canada)
Fundersnot available
KeywordsAplastic anemiaIncidence (geometry)MedicineDatabaseAnemiaInternal medicineComputer scienceBone marrow

Abstract

fetched live from OpenAlex

Introduction: Aplastic anemia (AA) is a rare and serious blood disorder of bone marrow failure characterized by hypocellularity and peripheral cytopenias. Without appropriate therapy, AA is often fatal. Patients (pts) with AA have a better prognosis when they initiate appropriate treatment early. Delays in diagnosing AA may be a barrier to timely initiation of therapy. This analysis of real-world data provides key insights into the current management of AA in the US that may also be applicable globally. Aims: To investigate the epidemiologic and therapeutic landscape of AA using a US claims database, to identify opportunities for improvement in the management of AA. Methods: Anonymous patient longitudinal claims data from the Veeva Compass Patient database were analyzed, covering prescriptions, procedures, and diagnoses for >300 million pts (Jan 2017-Sept 2023). Pts with AA were identified using ICD-10 diagnosis codes. The population at risk (PAR) included pts with ≥2 claims for anemia, thrombocytopenia, and leukocytopenia, or claims for pancytopenia, within 365 days before and 15 days after the first AA claim. Analyses were of incident cases of AA, defined as pts meeting the following criteria: bone marrow biopsy within 120 days before and 15 days after the first AA claim; no prior AA treatment; claims for ≥4 quarters before the first AA claim; aged ≥2 years; and AA diagnosis per claims volume within 15 days before and 100 days after initial diagnosis. Time to diagnosis (T0) was defined as the date a clinician might suspect AA and start diagnostic workup, determined by comparing claims volume peaks to patient level baselines before an AA diagnosis. The diagnosis time (TDx) was marked by the first AA claim. Results: Approximately 128,000 pts with suspected AA were identified based on ICD-10 diagnosis code, of whom, 44,661 pts were included in the PAR, 16,561 pts met criteria for bone marrow biopsy, and 15,534 pts remained when use of prior specific AA therapies was excluded. There was a total of 1,937 final incident pts with AA. Median time from T0 to TDx was 95 days; mean (standard deviation) was 108 (76) days. Of incident AA pts, 55% (n=1,056) were female. Population breakdown by age at diagnosis was: 2-14 y, 10% (n=190); 15-35 y, 15% (n=286); 36-49 y, 9% (n=170); 50-64 y, 23% (n=451); and ≥65 y, 43% (n=840). By year, the incidence of AA per million population was 1.8 (2018), 1.6 (2019), 1.5 (2020), 1.5 (2021), and 1.4 (2022). Overall survival from diagnosis to final claim was 94.2% (n=179/190) for pts aged 2-14 y, 93.4% (n=267/286) for pts aged 15-35 y, 91.8% (n=156/170) for pts aged 36-49 y, 82.5% (n=372/451) for pts aged 50-64 y, and 68.8% (n=578/840) for pts aged ≥65 y. Median time from diagnosis to treatment was 43 days. Most commonly reported treatments for AA were blood transfusion (64.3%), cyclosporine (33.5%), and filgrastim (13.0%). Stem cell transplant (6.5%), horse antithymocyte globulin (hATG; 1.4%), and eltrombopag (0.3%) were recorded less frequently. AA pts had a higher claims volume relative to controls prior to diagnosis. Median claims volume for AA pts surpassed the 90th percentile for controls at approximately 1 y before the first AA claim and peaked beginning at 3 months before the first AA claim. Conclusions: These analyses of real-world data from a large US claims database show that the incidence of AA is similar to what has been reported previously. However, these data also indicated that slightly more pts are female, rather than a slight male predominance reported in most trials, and most pts with AA in the US are aged ≥50 y. Relative to the literature, incidence of AA appears to be higher than previously reported in pts aged ≥65 y; some reports may have other referral biases due to the types of centers involved. This might suggest a potential care gap in older pts with AA, necessitating earlier referral and diagnosis, followed by appropriate treatment. Analyses also indicated that healthcare utilization increases at about 1 y prior to diagnosis of AA and peaks beginning approximately 3 months prior to diagnosis of AA relative to the control population. Treatment patterns suggest infrequent use of direct therapy within 6 months of diagnosis, which may reflect the overall management of pts with AA in the US. These findings require further evaluation, but could present opportunities for earlier intervention, diagnosis, and treatment.

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)0.011
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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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
Published2024
Admission routes1
Has abstractyes

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