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

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.

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).

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, P < 0.001), albumin level (OR = 0.87, P = 0.009), aspartate aminotransferase (OR = 1.01, P < 0.001), total bilirubin (OR = 1.04, P < 0.001), blood calcium (OR = 0.01, P = 0.003), and blood glucose (OR = 1.74, P < 0.001). The nomogram model demonstrated excellent discrimination with an area under the curve (AUC) of 0.926 (95% CI: 0.874-0.978) and good calibration (Brier score = 0.012). Internal validation confirmed robust performance with a corrected AUC of 0.923 (95% CI: 0.911-0.931). DCA indicated a positive net benefit across threshold probabilities of 0.02-0.72, suggesting that the model has strong potential for clinical application.

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.