伯基特淋巴瘤患者预后不良危险因素分析及预测模型

doi:10.3969/j.issn.1006-6233.2025.03.025

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Abstract Objective: To analyze the risk factors for poor prognosis in patients with Burkitt lymphoma (BL) and establish a predictive model. Methods: A total of 51 BL patients admitted to our hospital from February 2018 to January 2024 were enrolled and followed up regularly until August 2024. Clinical data were collected, and serum lactate dehydrogenase (LDH) levels were measured. Patients were divided into a survival group (n=43) and a death group (n=8) based on outcomes. Cox regression analysis was used to identify risk factors for poor prognosis, and Kaplan-Meier survival curves were plotted, with survival rates compared using the Log-rank test. Results: Among the 51 patients, 34 achieved complete remission (CR), 12 had partial remission (PR), 3 had stable disease (SD), and 2 had progressive disease (PD) after chemotherapy. The overall response rate (ORR) was 90.20%, and the disease control rate (DCR) was 96.08%. By August 2024, the median follow-up time was 30 months, with no loss to follow-up. Six patients experienced recurrence, and eight died. The 3-year overall survival (OS) rate and event-free survival (EFS) rate were 84.31% and 81.40%, respectively. Univariate Cox regression analysis showed that maxillofacial and central nervous system involvement, LDH >1000 U/L, chemotherapy alone, bone marrow tumor cell proportion >25%, involvement of >4 organ sites, St. Jude stage III+IV, early chemotherapy insensitivity, and visible tumor lesions at mid-term evaluation were associated with poor prognosis (P<0.05). Multivariate Cox regression identified maxillofacial and central nervous system involvement, LDH >1000 U/L, and bone marrow tumor cell proportion >25% as independent risk factors for poor prognosis (P<0.05). Conclusion: Maxillofacial and central nervous system involvement, LDH >1000 U/L, and bone marrow tumor cell proportion >25% are significant risk factors for poor prognosis in BL patients. Tailored treatment strategies targeting these factors may improve patient outcomes.

Key words： Burkitt lymphoma Poor prognosis Risk factors Predictive model

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	WANG Xiaorong
	BAI Yanni

Cite this article:

WANG Xiaorong,BAI Yanni. Risk Factor Analysis and Prediction Model for Poor Prognosis in Patients with Burkitt Lymphoma[J]. HeBei Med, 2025, 31(3): 490-494.

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http://www.hbyxzzs.cn/EN/10.3969/j.issn.1006-6233.2025.03.025 OR http://www.hbyxzzs.cn/EN/Y2025/V31/I3/490

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