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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".