Relapse and transformation to myelodysplastic syndrome and acute myeloid leukemia following immunosuppressive therapy for aplastic anemia is more common as compared to allogeneic stem cell transplantation with a negative impact on survival.
Sohini ChattopadhyaySharon LionelSushil SelvarajanAnup Joseph DevasiaAnu KorulaKulkarni Uday PrakashFouzia N AboobackerKavitha M LakshmiAlok SrivastavaVikram MathewsAby AbrahamBiju GeorgePublished in: Annals of hematology (2024)
We studied the incidence of relapse, transformation to myelodysplastic syndrome/acute myeloid leukemia, and survival in patients with aplastic anemia (AA) surviving more than 1 year after ATG/ALG-based immunosuppressive therapy (IST) between 1985 and 2020. Four-hundred seventy patients (413 adults and 57 children) were studied, and data were compared with 223 patients who underwent matched sibling donor transplant (MSD HSCT). Median follow-up is 50 months (12-359). Relapse occurred in 21.9% at a median time of 33.5 months (5-228) post IST. Twenty-six (5.5%) patients progressed to PNH, while 20 (4.3%) evolved to MDS/AML. Ten-year estimated overall survival (OS) is 80.9 ± 3% and was significantly better in patients without an event (85.1 ± 4%) compared to relapse (74.6% ± 6.2%) or clonal evolution (12.8% ± 11.8%) (p = 0.024). While the severity of AA (p = 0.011) and type of ATG (p = 0.028) used predicted relapse, only age at IST administration influenced clonal evolution (p = 0.018). Among HSCT recipients, relapse rates were 4.9% with no clonal evolution, and the 10-year OS was 94.5 ± 2%. In patients who survived 1 year following IST, outcomes were good except with clonal evolution to MDS/AML. These outcomes, however, were still inferior compared to matched sibling donor HSCT.
Keyphrases
- acute myeloid leukemia
- end stage renal disease
- stem cell transplantation
- chronic kidney disease
- newly diagnosed
- ejection fraction
- free survival
- prognostic factors
- stem cells
- high dose
- young adults
- type diabetes
- acute lymphoblastic leukemia
- machine learning
- electronic health record
- big data
- insulin resistance
- artificial intelligence
- weight loss