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Record W7117359556 · doi:10.31640/ls-2025-4-24

Management of chronic myeloid leukemia in children and adolescents: tyrosine kinase inhibitor efficacy, survivorship challenges, and long-term hemostatic safety

2025· article· W7117359556 on OpenAlexaboutno aff
Л. Я. Дубей, O. I. Дорош, Н. В. Дубей, Б. Р. Коцай, А. Є. Лісний, Н. В. Камуть, У. С. Шевців, Р. Я. Назар, O. O. Калмиков

Bibliographic record

VenueLikarska sprava · 2025
Typearticle
Language
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMyeloid leukemiaTyrosine-kinase inhibitorSurvivorship curveObservational studyDiseaseTyrosine kinaseBone marrow transplantationQuality of life (healthcare)Leukemia

Abstract

fetched live from OpenAlex

Background. The introduction of BCR::ABL1 tyrosine kinase inhibitors (TKIs) has transformed pediatric chronic myeloid leukemia (CML) from a life-threatening disease often requiring transplantation into a manageable chronic condition. However, this success necessitates a shift in clinical focus from acute survival to the multi-system, long-term toxicities incurred over a lifetime of therapy. Objective. The systematic scoping review synthesizes evidence from 2021 to the present, evaluating the comparative efficacy of TKI generations, long-term developmental and reproductive safety, and the complex cardiovascular and hemostatic risks in children and adolescents with CML. Methods. Following PRISMA-ScR guidelines, a systematic search of PubMed, Scopus, and Web of Science was conducted for studies published from January 2021. Key guidelines from the last decade were included. Observational studies were appraised using the Newcastle-Ottawa Quality Assessment Scale (NOQAS). Data were extracted and synthesized narratively across six key domains: 1) epidemiology, 2) comparative efficacy, 3) growth and endocrine safety, 4) fertility and reproductive safety, 5) cardiovascular and hemostatic safety, and 6) emergent therapies. Results. Pediatric CML presents with more aggressive features (e. g., higher leukocytosis, larger splenomegaly) than adult CML. While second-generation (2G) TKIs (dasatinib, nilotinib) demonstrate superior rates of deep and rapid molecular response (DMR) compared to imatinib, this has not translated into a proven overall survival (OS) benefit. However, achieving DMR is a prerequisite for treatment-free remission (TFR), a critical goal in pediatrics to mitigate long-term toxicities. Safety analysis revealed significant, TKI class-effect toxicities, including high rates of short stature (up to 26.5% in pre-pubertal patients) and low bone mineral density (35.2%). Growth impairment is comparable across imatinib and 2G-TKIs. A critical, sex-specific risk exists for fertility, with TKI teratogenicity confirmed in maternal exposure (5% anomaly rate) but not in paternal exposure, where large registry data show no increased risk. A critical analysis of “cardiovascular risk” reveals a divergent hemostatic risk spectrum: Nilotinib and ponatinib confer a pro-thrombotic (arterial, venous) risk, whereas dasatinib induces an anti-platelet (hemorrhagic) risk via SRC-kinase inhibition. Surveys confirm these specific risks are poorly recognized, with no standardized surveillance in pediatric practice. Conclusions. TKI selection in pediatric CML must evolve beyond efficacy alone to a multi-disciplinary, risk-stratified model. This includes balancing the TFR-enabling efficacy of 2G-TKIs against their specific toxicity profiles. This review proposes a “Hemostatic Risk Spectrum” as an essential framework, mandating that TKI selection be guided by the patient’s baseline hemostatic risk (e. g., avoiding nilotinib in thrombophilia, avoiding dasatinib in bleeding disorders). Standardized protocols for monitoring endocrine, metabolic, and hemostatic health are urgently required to ensure safe, long-term survivorship.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.257
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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