Outcome of allogeneic hematopoietic stem cell transplantation in adult patients with paroxysmal nocturnal hemoglobinuria.
Yukinori NakamuraKatsuto TakenakaHirohito YamazakiYasushi OnishiYukiyasu OzawaKazuhiro IkegameKen-Ichi MatsuokaTomomi ToubaiYasunori UedaYoshinobu KandaTatsuo IchinoheYoshiko AtsutaTakehiko MoriPublished in: International journal of hematology (2020)
The safety and efficacy of allogeneic hematopoietic stem cell transplantation (HSCT) for paroxysmal nocturnal hemoglobinuria (PNH) remain unclear. Therefore, we retrospectively analyzed the outcomes of 42 adult patients with PNH who underwent allogeneic HSCT using the registry database of the Japan Society for Hematopoietic Cell Transplantation. The median patient age was 32.5 years. The number of packed red cell (PRC) transfusions was < 20 times in 19 patients and ≥ 20 times in 16; 7 patients had missing data. Stem cell sources were bone marrow (N = 15) or peripheral blood (N = 13) from a related donor or bone marrow (N = 11) and cord blood (N = 3) from an unrelated donor. The cumulative incidence of neutrophil engraftment at day 40 was 81%. Six patients died before engraftment, and the 6-year overall survival (OS) was 74%. The OS of patients with < 20 pretransplant PRC transfusions was significantly higher than that of patients with ≥ 20 pretransplant PRC transfusions (95% vs. 63%; P < 0.05). Moreover, the OS of patients aged < 30 years was significantly higher than that of patients aged ≥ 30 years (90% vs. 59%; P < 0.05). Allogeneic HSCT for PNH could provide favorable survival; however, pretransplant transfusion burden and patient age should be considered when deciding the timing of allogeneic HSCT.
Keyphrases
- bone marrow
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- stem cells
- allogeneic hematopoietic stem cell transplantation
- peripheral blood
- prognostic factors
- mesenchymal stem cells
- blood pressure
- emergency department
- atrial fibrillation
- metabolic syndrome
- acute myeloid leukemia
- machine learning
- acute lymphoblastic leukemia
- low dose
- case report
- insulin resistance
- type diabetes
- skeletal muscle
- high dose
- artificial intelligence