Incidence, Time to Diagnosis, and Therapeutic Landscape of Aplastic Anemia: Results from a Large United States Claims Database
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».