Paroxysmal Nocturnal Hemoglobinuria Testing in Patients with Myelodysplastic Syndrome in Clinical Practice—Frequency and Indications
Bibliographic record
Abstract
Background: Myelodysplastic syndrome (MDS) is characterized by peripheral blood cytopenias, with most patients developing significant anemia and dependence on red blood cell (RBC) transfusion. In paroxysmal nocturnal hemoglobinuria (PNH), mutations in the PIGA gene lead to lack of cell-surface glycosylphosphatidylinositol, allowing complement-mediated lysis to occur. Paroxysmal nocturnal hemoglobinuria results in direct antiglobulin test–negative hemolysis and cytopenias, and up to 50% of patients with MDS test positive for PNH cells. We wanted to determine whether PNH is considered to be a contributor to anemia in MDS. Methods: Patients with a diagnosis of MDS confirmed by bone-marrow biopsy since 2009 were reviewed. Highresolution PNH testing by flow cytometry examined FLAER (fluorescein-labeled proaerolysin) binding and expression of CD14, CD15, CD24, CD45, CD59, CD64, and CD235 on neutrophils, monocytes, and RBCS. Results: In 152 patients with MDS diagnosed in 2009 or later, the MDS diagnosis included subtypes associated with PNH positivity (refractory anemia, n = 7, and hypoplastic MDS, n = 4). Of 11 patients who underwent PNH testing, 1 was positive (9.0%). Reasons for PNH testing were anemia (n = 3), new MDS diagnosis (n = 2), hypoplastic MDS (n = 2), decreased haptoglobin (n = 1), increased RBC transfusion requirement (n = 1), and unexplained iron deficiency (n = 1). Conclusions: Testing for PNH was infrequent in MDS patients, and the criteria for testing were heterogeneous. Clinical indicators prompted PNH testing in 6 of 11 patients. Given that effective treatment is now available for PNH and that patients with PNH-positive MDS can respond to immunosuppressive therapy, PNH testing in MDS should be considered. Prospective analyses to clarify the clinical significance of PNH positivity in MDS are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".