Invasive Fungal Infection in Childhood Embryonal Brain Tumour Treatment: A 10-year Review
Carter, S. M.; Chawla, A.; Campbell, M.; Eisenstat, D. D.; Weerdenburg, H.; Khuong-Quang, D.-A.; Haeusler, G. M.
Show abstract
Background: Invasive fungal infection (IFI) is well recognised in children with acute leukaemia and allogeneic haematopoietic stem-cell transplantation but is poorly characterised in children with brain tumours. Children receiving intensive therapy for embryonal brain tumours (EBTs) have multiple potential risk exposures including corticosteroids, central venous access, neurosurgical devices, mucosal injury and myelosuppressive chemotherapy with, in selected protocols, autologous stem-cell rescue. Methods: We performed a single-centre retrospective cohort study of children aged 0-18 years treated for EBTs between 2015-2025. IFIs were classified as proven, probable, possible, or modified possible using EORTC/MSGERC and TERIFIC criteria. Clinical characteristics, treatment exposures, timing, microbiology and outcomes were described. IFI prevalence was calculated using exact binomial confidence intervals. Exploratory Cox proportional hazards analyses assessed associations with clinical and treatment factors. Results: Seventy-seven patients were included. Fourteen patients experienced 15 IFI episodes, giving a patient-level IFI prevalence of 18.2% (95% CI, 10.3-28.6%). Proven or probable IFI occurred in seven patients (9.1%; 95% CI, 3.7-17.8%). Nine episodes had microbiological evidence. Non-mould pathogens predominated, accounting for six of nine identified pathogens. Treatment on ACNS0334/ACNS0333 was associated with a lower hazard of proven/probable IFI compared with SJMB12 (HR 0.062; 95% CI, 0.002-0.78; p=0.031). Two patients had chemotherapy delays exceeding one month, one had persistent infection at 12 months; no deaths were directly attributed to IFI. Three patients received antifungal prophylaxis. Conclusion: Rates of IFI following intensive embryonal brain tumour therapy were comparable to those in other high-risk oncology populations. Local consideration of antifungal prophylaxis is warranted.
Matching journals
The top 14 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- COVID symptoms, testing, shielding impact on patient reported outcomes and early vaccine responses in individuals with multiple myeloma 90%
- A validation study of the identification of haemophagocytic lymphohistiocytosis in England using population-based health data 89%
- Validation of the IMPEDE VTE Score for Prediction of Venous Thromboembolism in Multiple Myeloma: A Retrospective Cohort Study 89%
Similar papers in this journal
- Clinical and laboratory features of COVID-19 illness and outcomes in immunocompromised individuals during the first pandemic wave in Sydney, Australia 90%
- In-Hospital Survival of Adults with HIV-Associated Cryptococcal Meningitis in Tanzania: A Retrospective Comparison of Amphotericin B-based Regimen and Fluconazole Monotherapy 89%
- Impact of blood analysis and immune function on the prognosis of patients with COVID-19 88%
Similar papers in this journal
- Convalescent plasma improves overall survival in patients with B-cell lymphoid malignancies and COVID-19: a longitudinal cohort and propensity score analysis 91%
- Single-cell transcriptomics predicts relapse in MLL-rearranged acute lymphoblastic leukemia in infants 90%
- Clinical Impact of Panel Based Error Corrected Next Generation Sequencing versus Flow Cytometry to Detect Measurable Residual Disease (MRD) in Acute Myeloid Leukemia (AML) 89%
Similar papers in this journal
Similar papers in this journal
- Assessment of Prognostic Value of Cystic Features in Glioblastoma Relative to Sex and Treatment with Standard-of-Care 88%
- Factors affecting COVID-19 outcomes in cancer patients - A first report from Guys Cancer Centre in London 88%
- mRNA-COVID19 vaccination can be considered safe and tolerable for frail patients 87%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.