医学
美罗华
自身免疫性溶血性贫血
贫血
流行病学
病因学
疾病
重症监护医学
治疗方法
内科学
自身抗体
儿科
免疫学
淋巴瘤
抗体
作者
Jeremy W. Jacobs,Sheharyar Raza,Landon M. Clark,Laura D. Stephens,Elizabeth S. Allen,Jennifer S. Woo,Rachel Walden,Cristina A. Figueroa Villalba,Christopher A. Tormey,C Stanek,Brian D. Adkins,Evan M. Bloch,Garrett S. Booth
摘要
ABSTRACT Mixed autoimmune hemolytic anemia (AIHA) is a rare and clinically complex hematologic disorder defined by the simultaneous presence of both warm and cold autoantibodies, resulting in severe and often treatment‐resistant hemolysis. Due to variability in diagnostic criteria and limited data, a comprehensive understanding of its epidemiology, clinical characteristics, and management remains incomplete. To address these gaps, we performed a systematic literature review employing stringent diagnostic criteria to evaluate epidemiologic patterns, clinical features, and therapeutic outcomes. Our analysis included 81 patients identified across 35 studies, revealing a median age of 45 years and a notable female predominance (2.25:1). Autoimmune diseases constituted the most frequent underlying etiology, followed by hematologic malignancies and infections. Patients exhibited significant anemia, with median nadir hemoglobin levels reaching 5.6 g/dL. Corticosteroids represented the most common therapeutic intervention; however, only 43% of patients achieved remission, while 37% experienced chronic hemolysis, and mortality reached 11%. Many patients required multiple lines of therapy, including rituximab and cytotoxic agents, highlighting the disease's refractory nature and management complexity. The substantial variability in diagnostic and therapeutic approaches emphasizes an urgent need for standardized diagnostic criteria, earlier integration of combination therapies, and exploration of innovative treatment modalities. Future prospective, multicenter studies are essential to refine disease recognition, optimize therapeutic strategies, and ultimately improve patient outcomes in mixed AIHA.
科研通智能强力驱动
Strongly Powered by AbleSci AI