Prior History of Severe Infection in Children, Adolescents and Young Adults with Lymphoma As Proxy for Inborn Errors of Immunity: Prevalence and Impact on Post-Lymphoma Outcomes
Notice bibliographique
Résumé
Introduction: Children, adolescents and young adults (CAYA) diagnosed with lymphoma may have an underlying, undiagnosed inborn error of immunity (IEI). The prevalence and outcomes of CAYA with undiagnosed IEI who are diagnosed with lymphoma is largely unknown. The primary aim of this study was to assess the prevalence of a prior history of severe infections in such CAYA as a marker of potential underlying IEI compared to matched population controls without lymphoma. The secondary aim was, amongst patients diagnosed with lymphoma, to evaluate the association of a preceding history of serious infection with the incidence of serious infection and mortality following lymphoma diagnosis. Methods: We identified all CAYA in Ontario, Canada ages 0-21 years diagnosed 1992-2022 with Hodgkin (HL) or non-Hodgkin Lymphoma (NHL), excluding those with known IEI, prior cancer diagnosis or with post-transplant lymphoproliferative disease using pediatric and adult population-based cancer registries. Each lymphoma case was age-, sex- and geographic region- randomly matched to 5 population controls. First, using linkage to provincial population-based healthcare data, infection-related healthcare encounters (outpatient, emergency room, hospitalizations, ICU) were identified from birth through 6 months prior to lymphoma diagnosis, and compared between cases and controls with evaluation for impacts of age, sex and lymphoma subtype. Other potential proxies for IEI, including prior diagnosis of autoimmune disease and prior hospitalization for failure to thrive (FTT) were also compared. Second, amongst CAYA with lymphoma, the incidence of infection-related ICU admission and mortality post lymphoma-diagnosis was compared between those with and without a pre-lymphoma diagnosis history of infection-related ICU admission. Results: A total of 2950 CAYA with lymphoma and without known IEI and 14,750 matched controls were included. Among cases, mean age at diagnosis was 15.5 years (SD 4.76) and 58% (1708/2950) were males. Compared to controls, cases had a statistically significantly higher prior incidence of all types of infection-related healthcare encounters, autoimmune disease, and FTT. Most striking were infection-related ICU admissions, a history of which was nearly 9-fold more common among cases vs. controls [4.8% (143/2950) vs. 0.6% (87/14750); OR 8.9 (95CI: 6.7-11.7); P<0.0001]. Stratification by age group and lymphoma subtype yielded similar results. Among CAYA with lymphoma, those with a pre-lymphoma diagnosis history of infection-related ICU admission were 9-times more likely to have post-diagnosis infection-related ICU admission compared to lymphoma patients without such a history [6-month cumulative incidence 38.5% vs. 6.6%; OR 8.9 [95CI: 6.2-12.9; P<0.0001). Similarly, the risk of death was substantially higher. One- and five-year overall survival were 87.3% and 66.7% vs. 97.2% and 93.5%; OR 6.6 (95CI: 5.0-8.6); P<0.0001). The impact of preceding history of infection on risks of subsequent ICU admission and mortality persisted when analyzed by age group and lymphoma subtype. Interpretation and conclusion: In this population-based matched cohort study, our findings suggest that a subset of CAYA diagnosed with lymphoma likely have IEI at rates higher than previously suggested, given their substantially increased odds of a history of previous infection-related ICU admission. Notably, the subset of patients with lymphoma with this preceding history had a substantially and clinically important increased risk of serious infection and mortality following lymphoma diagnosis. Systematic evaluations for IEI in children and AYA diagnosed with lymphoma should be considered, particularly in those with a history of prior serious infection, regardless of lymphoma subtype or age at diagnosis.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».