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Enregistrement W4405043213 · doi:10.1182/blood-2024-207419

Pediatric Autoimmune Hemolytic Anemia Is Associated with a High Incidence of an Underlying Immune Disorder and High Mortality Rate

2024· article· en· W4405043213 sur OpenAlexaff
Rachael F. Grace, MacGregor Steele, Tatiana Kalashnikova, Ellis Bloom, Sherif M. Badawy, Hannah Elkus, Vicky R. Breakey, Sureka Pavalagantharajah, Stephanie A. Fritch Lilla, John Leister, Manpreet Kochhar, Kirsty Hillier, Lia Phillips, Jessica Chumsky, Robert J. Klaassen, Dana Ghanem, Danielle Charland, Taizo A. Nakano, Joshua A Kalter, Allison Remiker, Jennifer Rothman, Brian Guedes, Sanjay Shah, Cassandra J. Everly, Kristin A. Shimano, Alison Matsunaga, Gerald C Tiu, R. Valle, Shilpa Nataraj, Stacey Rifkin-Zenenberg, Erika Tavares, Caitlin Montcrieff, Emily Harris, Nan Chen, Wendy B. London, Michele P. Lambert

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueBlood groups and transfusion
Établissements canadiensChildren's Hospital of Eastern OntarioMcMaster UniversityMcMaster Children's HospitalAlberta Children's Hospital
Organismes subventionnairesnon disponible
Mots-clésAutoimmune hemolytic anemiaMedicineIncidence (geometry)Immune systemAnemiaMortality rateImmunologyPediatricsInternal medicine

Résumé

récupéré en direct d'OpenAlex

Background: Autoimmune hemolytic anemia (AIHA) is a rare pediatric autoimmune disease in which there is limited guidance for evaluation and treatment and a poor understanding of the characteristics that influence prognosis and clinical course. Aim: To characterize the clinical features, laboratory findings, and treatment outcomes of children with AIHA over a 10-year period. Methods: An IRB-approved multicenter observational cohort study included patients (pts) from 15 institutions from the ITP Consortium of North America (ICON) who were 3 mo to 21 y and had hematology consultation between Jan 2011 and Dec 2020 with a diagnosis of AIHA. Post-stem cell transplant AIHA was excluded. Demographic, medical history, laboratory, and treatment data were collected. Continuous variables are summarized as mean (SD) or median (range) and compared with a two-sided t-test. Categorical variables are summarized as counts or percentages and compared with a two-sided chi-squared test. Results: A total of 402 pts were included: median age at diagnosis 7 y (n=397, range: 0-22) with median length of follow up 2.7 years (n=390, range: 0.1-19.2). 45% (180/398) were female. Warm AIHA (wAIHA) was the predominant subtype in 64% (252/395). Most pts (58%, 217/374) had a single AIHA episode that remitted; others had two episodes that remitted (8%, 29/374), multiple relapsing episodes (7%, 26/374), chronic course that remitted (13%, 48/374), or chronic ongoing course (14%, 54/374). Cold agglutinin disease (CA) was present in 12% (47/395), paroxysmal cold hemoglobinuria (PCH) in 6% (24/395), and mixed/unspecified AIHA in 18% (72/395). Secondary AIHA was common with 15% (57/389) with an underlying primary immunodeficiency, most commonly CVID (49%, 28/57), ALPS (33%, 19/57), and 22q11.2DS (16%, 9/57); and 16% (61/391) with another autoimmune disorder, most commonly SLE (49%, 30/61), neurologic disease (36%, 22/61), or APLA (16%, 10/61). Immune testing was sent in 100% (402/402) and abnormal in 60% (240/402). An immunologist was consulted in 136/342 (40%). Infectious testing was sent in 71% (260/366). Family history (1st or 2nd degree) was positive for immune cytopenias, immunodeficiency, or autoimmunity in 19% (78/402). Genetic testing (sent in 30%, 106/349), was most often a gene panel (67%, 71/106) or WES (28%, 30/106) with pathogenic findings identified in 30% (30/100). Evans syndrome (ES) was common (37%, 142/385) including ITP (92%, 131/142) and/or immune neutropenia (43%, 57/133) and most likely in those with wAIHA (47%, 113/241) compared with PCH (0%, 0/23) or CA (4%, 2/45). Compared with pts without ES (n=243), those with ES (n=142) were more likely to be older at first episode (9 y vs 5 y, p=0.010), have genetic testing (54% vs 18%, p<0.001), pathogenic gene findings (13% vs 5%, p=0.002), and treatment with steroid-sparing agents at first episode (35% vs 19%, p<0.001). There was no difference in response to steroids at first episode (77% vs 75%, p=0.748) or incidence of another autoimmune disorder (18% vs 14%, p=0.306). Similarly, those with secondary AIHA were more likely to be older at first episode (8 y vs 6 y, p=0.007) and have wAIHA (77% vs 62%, p=0.035). Secondary AIHA did not impact steroid response (77% vs 74%, p=0.653) or treatment with steroid-sparing agents at first episode (26% vs 24%, p=0.756). Pts with multiple episodes of AIHA were also more likely to have wAIHA (82% vs 52%, p<0.001), genetic testing (50% vs 19%, p<0.001), and treatment with steroid-sparing agents at first episode (34% vs 18%, p<0.001) but did not have a different age at presentation of 1st episode [8 y vs 5 y, p=0.203] or proportion with pathogenic gene findings (10% vs 6%, p=0.268). Of those with ongoing follow up, 68/249 (27%) of pts with wAIHA had active disease on treatment, 7/249 (3%) had active disease and were being observed, and 174/249 (70%) were in remission. Overall, mortality was 9.4%. Mortality was not associated with a diagnosis of ES, immunodeficiency or autoimmunity, number of AIHA episodes, or type of AIHA. Conclusion: AIHA is associated with significant morbidity in children with a high rate of secondary AIHA (~30%), 37% with ES, and 34% with a chronic or relapsing course. In this recently diagnosed cohort, there was a 9.4% mortality rate. These high morbidity and mortality rates require expanded testing, monitoring, and additional treatment approaches in this pediatric population.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,103
Score d'incertitude au seuil0,606

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,257
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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