Consensus definitions of 14 severe acute toxic effects for childhood lymphoblastic leukaemia treatment: a Delphi consensus

Although there are high survival rates for children with acute lymphoblastic leukaemia, their outcome is often counterbalanced by the burden of toxic effects. This is because reported frequencies vary widely across studies, partly because of diverse definitions of toxic effects. Using the Delphi method, 15 international childhood acute lymphoblastic leukaemia study groups assessed acute lymphoblastic leukaemia protocols to address toxic effects that were to be considered by the Ponte di Legno working group. 14 acute toxic effects (hypersensitivity to asparaginase, hyperlipidaemia, osteonecrosis, asparaginase-associated pancreatitis, arterial hypertension, posterior reversible encephalopathy syndrome, seizures, depressed level of consciousness, methotrexate-related stroke-like syndrome, peripheral neuropathy, high-dose methotrexate-related nephrotoxicity, sinusoidal obstructive syndrome, thromboembolism, and Pneumocystis jirovecii pneumonia) that are serious but too rare to be addressed comprehensively within any single group, or are deemed to need consensus definitions for reliable incidence comparisons, were selected for assessment. Our results showed that none of the protocols addressed all 14 toxic effects, that no two protocols shared identical definitions of all toxic effects, and that no toxic effect definition was shared by all protocols. Using the Delphi method over three face-to-face plenary meetings, consensus definitions were obtained for all 14 toxic effects. In the overall assessment of outcome of acute lymphoblastic leukaemia treatment, these expert opinion-based definitions will allow reliable comparisons of frequencies and severities of acute toxic effects across treatment protocols, and facilitate international research on cause, guidelines for treatment adaptation, preventive strategies, and development of consensus algorithms for reporting on acute lymphoblastic leukaemia treatment

Invasive fungal diseases impact on outcome of childhood ALL - an analysis of the international trial AIEOP-BFM ALL 2009

In children with acute lymphoblastic leukemia (ALL), risk groups for invasive fungal disease (IFD) with need for antifungal prophylaxis are not well characterized, and with the advent of new antifungal compounds, current data on outcome are scarce. Prospectively captured serious adverse event reports of children enrolled in the international, multi-center clinical trial AIEOP-BFM ALL2009 were screened for proven/probable IFD, defined according to the updated EORTC/MSG consensus definitions. In a total of 6136 children (median age 5.2 years), 224 proven/probable IFDs (65 yeast and 159 mold) were reported. By logistic regression, the risk for proven/probable IFDs was significantly increased in children ≥12 years and those with a blast count ≥10% in the bone marrow on day 15 (P < 0.0001 each). Proven/probable IFDs had a 6-week and 12-week mortality of 10.7% and 11.2%, respectively. In the multivariate analysis, the hazard ratio for event-free and overall survival was significantly increased for proven/probable IFD, age ≥12 years, and insufficient response to therapy (P < 0.001, each). Our data define older children with ALL and those with insufficient treatment-response at high risk for IFD. As we show that IFD is an independent risk factor for event-free and overall survival, these patients may benefit from targeted antifungal prophylaxis

Dilemmas on emicizumab in children with haemophilia A: A survey of strategies from PedNet centres.

INTRODUCTION Haemophilia A care has changed with the introduction of emicizumab. Experience on the youngest children is still scarce and clinical practice varies between haemophilia treatment centres. AIM We aimed to assess the current clinical practice on emicizumab prophylaxis within PedNet, a collaborative research platform for paediatricians treating children with haemophilia. METHODS An electronic survey was sent to all PedNet members (n = 32) between October 2022 and February 2023. The survey included questions on the availability of emicizumab, on the practice of initiating prophylaxis in previously untreated or minimally treated patients (PUPs or MTPs) and emicizumab use in patients with or without inhibitors. RESULTS All but four centres (28/32; 88%) responded. Emicizumab was available in clinical practice in 25/28 centres (89%), and in 3/28 for selected patients only (e.g. with inhibitors). Emicizumab was the preferred choice for prophylaxis in PUPs or MTPs in 20/25 centres; most (85%) started emicizumab prophylaxis before 1 year of age (30% before 6 months of age) and without concomitant FVIII (16/20; 80%). After the loading dose, 13/28 centres administered the recommended dosing, while the others adjusted the interval of injections to give whole vials. In inhibitor patients, the use of emicizumab during ITI was common, with low-dose ITI being the preferred protocol. CONCLUSION Most centres choose to initiate prophylaxis with emicizumab before 12 months of age and without concomitant FVIII. In inhibitor patients, ITI is mostly given in addition to emicizumab, but there was no common practice on how to proceed after successful ITI

Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia

Publisher Copyright: © 2021 Lippincott Williams and Wilkins. All rights reserved.Asparaginase-associated pancreatitis (AAP) frequently affects children treated for acute lymphoblastic leukemia (ALL) causing severe acute and persisting complications. Known risk factors such as asparaginase dosing, older age and single nucleotide polymorphisms (SNPs) have insufficient odds ratios to allow personalized asparaginase therapy. In this study, we explored machine learning strategies for prediction of individual AAP risk. We integrated information on age, sex, and SNPs based on Illumina Omni2.5exome-8 arrays of patients with childhood ALL (N=1564, 244 with AAP aged 1.0 to 17.9 y) from 10 international ALL consortia into machine learning models including regression, random forest, AdaBoost and artificial neural networks. A model with only age and sex had area under the receiver operating characteristic curve (ROC-AUC) of 0.62. Inclusion of 6 pancreatitis candidate gene SNPs or 4 validated pancreatitis SNPs boosted ROC-AUC somewhat (0.67) while 30 SNPs, identified through our AAP genome-wide association study cohort, boosted performance (0.80). Most predictive features included rs10273639 (PRSS1-PRSS2), rs10436957 (CTRC), rs13228878 (PRSS1/PRSS2), rs1505495 (GALNTL6), rs4655107 (EPHB2) and age (1 to 7 y). Second AAP following asparaginase re-exposure was predicted with ROC-AUC: 0.65. The machine learning models assist individual-level risk assessment of AAP for future prevention trials, and may legitimize asparaginase re-exposure when AAP risk is predicted to be low.Peer reviewe

First experience of a hemophilia monitoring platform: florio HAEMO

Background: florio HAEMO is a new hemophilia treatment monitoring application consisting of a patient smartphone application (app) and a web-based dashboard for healthcare professionals, providing several novel features, including activity tracking, wearable connectivity, kids and caregiver mode, and real-time pharmacokinetic factor level estimation. ----- Objectives: To assess intuitiveness, ease-of-use, and patient preference of florio HAEMO in Central Europe using a cross-sectional survey. ----- Methods: This survey was conducted in six Central European countries between 9 December 2020 and 24 May 2021. The online questionnaire included 17 questions about overall satisfaction, ease-of-use, intuitiveness, and patient preference. Adults or children with hemophilia on regular prophylaxis and using the florio HAEMO app for a minimum of 1 week were invited to complete the online questionnaire by their treating physician. ----- Results: Sixty-six participants took part in the survey. The median duration for all respondents using the florio HAEMO app was 3 to 4 weeks. Overall, 89.4% of users reported being very satisfied or rather satisfied after using florio HAEMO. Of the 23 respondents who had switched from another hemophilia app, 87.0% indicated that they strongly preferred or preferred using florio HAEMO. Most florio HAEMO users reported that the app was very easy or rather easy to use (97.0%) and intuitive (94.0%). florio HAEMO had a positive impact on daily living, with 78.8% of users reporting that the app was very important or rather important to them. ----- Conclusions: This survey suggests that florio HAEMO is an easy-to-use and intuitive app to assist self-management of home prophylaxis

Can machine learning models predict asparaginase-associated pancreatitis in childhood acute lymphoblastic leukemia

Abstract Asparaginase-associated pancreatitis (AAP) frequently affects children treated for acute lymphoblastic leukemia (ALL) causing severe acute and persisting complications. Known risk factors such as asparaginase dosing, older age and single nucleotide polymorphisms (SNPs) have insufficient odds ratios to allow personalized asparaginase therapy. In this study, we explored machine learning strategies for prediction of individual AAP risk. We integrated information on age, sex, and SNPs based on Illumina Omni2.5exome-8 arrays of patients with childhood ALL (N=1564, 244 with AAP 1.0 to 17.9 yo) from 10 international ALL consortia into machine learning models including regression, random forest, AdaBoost and artificial neural networks. A model with only age and sex had area under the receiver operating characteristic curve (ROC-AUC) of 0.62. Inclusion of 6 pancreatitis candidate gene SNPs or 4 validated pancreatitis SNPs boosted ROC-AUC somewhat (0.67) while 30 SNPs, identified through our AAP genome-wide association study cohort, boosted performance (0.80). Most predictive features included rs10273639 (PRSS1-PRSS2), rs10436957 (CTRC), rs13228878 (PRSS1/PRSS2), rs1505495 (GALNTL6), rs4655107 (EPHB2) and age (1 to 7 y). Second AAP following asparaginase re-exposure was predicted with ROC-AUC: 0.65. The machine learning models assist individual-level risk assessment of AAP for future prevention trials, and may legitimize asparaginase re-exposure when AAP risk is predicted to be low