Mixed Autoimmune Hemolytic Anemia: A Systematic Review of Epidemiology, Clinical Characteristics, Therapies, and Outcomes
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".