Epidemiology and prognostic nomogram for chronic eosinophilic leukemia: a population-based study using the SEER database

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Abstract

Chronic Eosinophilic Leukemia (CEL), a rare and intricate hematological disorder characterized by uncontrolled eosinophilic proliferation, presents clinical challenges owing to its infrequency. This study aimed to investigate epidemiology and develop a prognostic nomogram for CEL patients. Utilizing the Surveillance, Epidemiology and End Results database, CEL cases diagnosed between 2001 and 2020 were analyzed for incidence rates, clinical profiles, and survival outcomes. Patients were randomly divided into training and validation cohorts (7:3 ratio). LASSO regression analysis and Cox regression analysis were performed to screen the prognostic factors for overall survival. A nomogram was then constructed and validated to predict the 3- and 5-year overall survival probability of CEL patients by incorporating these factors. The incidence rate of CEL was very low, with an average of 0.033 per 100,000 person-years from 2001 to 2020. The incidence rate significantly increased with age and was higher in males than females. The mean age at diagnosis was 57 years. Prognostic analysis identified advanced age, specific marital statuses, and secondary CEL as independent and adverse predictors of overall survival. To facilitate personalized prognostication, a nomogram was developed incorporating these factors, demonstrating good calibration and discrimination. Risk stratification using the nomogram effectively differentiated patients into low- and high-risk groups. This study enhances our understanding of CEL, offering novel insights into its epidemiology, demographics, and prognostic determinants, while providing a possible prognostication tool for clinical use. However, further research is warranted to elucidate molecular mechanisms and optimize therapeutic strategies for CEL.

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Wang, J., Lin, M., & Wang, F. (2024). Epidemiology and prognostic nomogram for chronic eosinophilic leukemia: a population-based study using the SEER database. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-55432-8

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