Back

Comprehensive Analysis of Atypical chronic myeloid leukemia (aCML): Epidemiology, Clinical Features, and Survival Outcomes Based on SEER Database Insights

Wang, F.

2024-07-29 hematology
10.1101/2024.07.28.24311130 medRxiv
Show abstract

BackgroundAtypical Chronic Myeloid Leukemia (aCML) is a rare and aggressive myelodysplastic syndrome/myeloproliferative neoplasm. This study aimed to provide a comprehensive understanding of the epidemiology, clinical characteristics, and survival outcomes of aCML patients. MethodsThe study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database from 2001 to 2020. The age-adjusted incidence rate (AIR) of aCML was calculated, and survival outcomes were analyzed using the Kaplan-Meier method and accelerated failure time (AFT) regression analysis. ResultsThe AIR of aCML was found to be 0.024 per 100,000 person-years, with the highest rate observed in 2020. The incidence of aCML increased with age and was higher in males. The study cohort predominantly consisted of elderly White individuals, with an average age at diagnosis of 68.2 {+/-} 15.3 years. The median overall survival (OS) and disease-specific survival (DSS) were 1.4 years and 1.7 years, respectively. Older age was independently associated with worse survival outcomes. Notably, treatment delay and chemotherapy did not significantly impact OS or DSS. ConclusionsThis study provides comprehensive insights into the epidemiology, clinical characteristics, and survival outcomes of aCML, highlighting its rarity, aggressive nature, and poor prognosis. Further research is needed to validate these findings and explore novel therapeutic strategies for improving outcomes in this challenging hematologic malignancy.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.