Evidence map›Paper›PMID 42239512›Full record

ArticleFrontiers in pharmacology2026

Nomogram-based prediction of asparaginase-associated pancreatitis in children with acute lymphoblastic leukemia: a retrospective study.

Xiangyu Ding, Ruihong Li, Chenhong Jia, Yile Zhao

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Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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4 authors.

Xiangyu Ding *Department of Pharmacy, Hebei Children's Hospital, Shijiazhuang, Hebei, China.
Ruihong Li *Department of Pharmacy, Hebei Children's Hospital, Shijiazhuang, Hebei, China.
Chenhong JiaDepartment of Pharmacy, Hebei Children's Hospital, Shijiazhuang, Hebei, China.
Yile ZhaoDepartment of Pharmacy, Hebei Children's Hospital, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Asparaginase is a crucial drug in acute lymphoblastic leukemia (ALL) treatment, but its use is frequently complicated by asparaginase-associated pancreatitis (AAP). Although several risk factors for AAP have been identified, no comprehensive predictive model is currently available to assess individual patient risk. We aimed to develop and validate a nomogram for AAP risk in children with ALL. Methods: We conducted a retrospective study of 352 children with ALL diagnosed at Hebei Children's Hospital from January 2020 to June 2025, all treated according to the Chinese Children's Leukemia Group Acute Lymphoblastic Leukemia 2018 (CCLG-ALL-2018) protocol. Patients were divided into AAP and non-AAP groups based on the 2012 Atlanta diagnostic criteria for pancreatitis. Clinical data and laboratory parameters were systematically collected. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by multivariate logistic regression to construct the prediction model. Model performance was assessed using the receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Results: Among the 352 patients (median age 5.0 years; 56.53% boys), 36 patients (10.23%) developed AAP. Six independent risk factors were identified: history of AAP (odds ratio [OR] = 13.20, Conclusion: A nomogram incorporating six available clinical and laboratory parameters was successfully developed and validated to predict AAP risk in children with ALL. The model demonstrated excellent predictive performance and clinical applicability, providing a valuable tool for early identification of high-risk AAP patients and guiding individualized preventive strategies to optimize treatment outcomes.

Indexed as

acute lymphoblastic leukemiaasparaginasechildrennomogram, prediction modelpancreatitisPEG-asparaginaserisk factors

Identifiers

PMID42239512
PMCPMC13226028

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