Approach to autologous stem cell transplantation in a patient with severe cold agglutinin disease, a case report
Bibliographic record
Abstract
BACKGROUND: Cold agglutinin disease (CAD) or syndrome (CAS) can be particularly challenging when autologous stem cell transplant (ASCT) is needed. Standard peripheral blood stem cell (PBSC) collection and manipulation involve ex vivo blood manipulations at lower than body temperature, predisposing to agglutination during graft collection, handling, processing, and infusion. STUDY DESIGN AND METHODS: We describe the first case of ASCT for relapsing lymphoma in a patient with high-titer CAD requiring anti-complement therapy and chronic transfusion. To prevent agglutination, five therapeutic plasma exchange sessions were performed prior to PBSC collection. Optimal thermal conditions were maintained using various approaches to increase the temperature of the room, venous return lines, and the apheresis device. Ex vivo graft manipulation was conducted under similar conditions. An infusion test was performed with a fraction of the graft a month prior to ASCT to confirm tolerability. RESULTS: The infusion test was performed without complications. A month later, the patient was admitted for ASCT. The aplasia phase was particularly challenging, as the patient experienced a rapid drop in hemoglobin levels due to hemolysis without compensatory reticulocytosis. Twelve months after ASCT, chronic hemolysis persists, but the patient is now transfusion-free, and the lymphoma remains in remission. DISCUSSION: Performing ASCT in patients with clinically significant CAS or CAD is challenging but can be done safely. Strong coordination between the apheresis team, cell therapy laboratory, and clinical unit is paramount to the success of this procedure. The experience gained from this case may also be applicable to other cell therapy procedures.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".