Predictors of breakthrough invasive fungal infections (BIFI) in pediatric acute leukemia: a retrospective analysis and predictive model development - Li, Yan; Qu, Lijun; Wang, Jian; Chen, Pingtian; Jiang, Aoshuang; Liu, Hongjun (Frontiers in Medicine, 2024)
Objective: This study aims to identify key risk factors associated with the development of breakthrough invasive fungal infections (BIFI) in pediatric acute leukemia patients to improve early detection and intervention strategies. Method: A retrospective analysis was conducted on 160 pediatric patients with acute leukemia admitted to Anhui Provincial Children's Hospital between October 2018 and June 2022. The study evaluated the impact of various clinical parameters on BIFI risk using univariate and multivariable analyses, with data including patient demographics, treatment regimens, and infection outcomes. The predictive model was assessed using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).Result: Among the 160 pediatric acute leukemia patients, 34 (22.22%) developed BIFI. Univariate analysis identified longer durations of neutrophil deficiency (P < 0.001), broad-spectrum antibiotic use (P < 0.001), higher volumes of red blood cell transfusions (P = 0.001), and elevated C-reactive protein (CRP) levels (P < 0.001) as significant factors associated with BIFI. Multivariable analysis confirmed these as significant predictors, with odds ratios for neutrophil deficiency (OR = 1.38, 95% CI [1.15, 1.69]), antibiotic use (OR = 1.41, 95% CI [1.10, 1.84]), transfusions (OR = 2.54, 95% CI [1.39, 5.13]), and CRP levels (OR = 1.10, 95% CI [1.04, 1.17]). The model validation showed strong predictive performance with an AUC of 0.890 (95% CI: 0.828 -0.952), good calibration (Brier score = 0.099), and demonstrated clinical utility across a range of risk thresholds. Conclusion: The study highlights the importance of considering these key predictors in the management of pediatric acute leukemia patients to mitigate the risk of BIFI. Incorporating these factors into personalized treatment strategies could enhance early intervention, reduce infection rates, and improve overall patient outcomes.