Incidence and outcome of invasive fungal diseases after allogeneic hematopoietic stem cell transplantation: A Swiss transplant cohort study.
Sabine KusterSusanne StampfBernhard GerberVeronika BaettigMaja WeisserSabine GerullMichael MedingerJakob PasswegUrs SchanzChristian GarzoniChristoph BergerYves ChalandonNicolas J MuellerChristian van DeldenDionysios NeofytosNina Khannanull nullPublished in: Transplant infectious disease : an official journal of the Transplantation Society (2018)
Contemporary, comprehensive data on epidemiology and outcomes of invasive fungal disease (IFD) including breakthrough IFD among allogeneic hematopoietic stem cell transplantation (HSCT) recipients are scarce. We included 479 allogeneic HSCT recipients with 10 invasive candidiasis (IC) and 31 probable/proven invasive mold disease (IMD) from the Swiss Transplant Cohort Study from 01.2009 to 08.2013. Overall cumulative incidence was 2.3% for IC and 8.5% for probable/proven IMI: 6% for invasive aspergillosis (IA) and 2.5% for non-AspergillusIMI. Among 41 IFD, 46% IFD were breakthrough, with an overall incidence of 4.6%, more frequently caused by other-than-Aspergillus fumigatus molds than primary IFD (47.6% (10/21) vs 13% (3/23), P = 0.04). Twelve-week mortality among patients with IC was 20% and 58.6% for probable/proven IMD (60% IA and 54.6% non-Aspergillus). Our results reveal that breakthrough IFD represent a marked burden of probable/proven IFD postallogeneic HSCT and mortality remains above 50% in patients with probable/proven IMD, underscoring the ongoing challenges to prevent and treat IFD in these patients.
Keyphrases
- allogeneic hematopoietic stem cell transplantation
- risk factors
- acute myeloid leukemia
- acute lymphoblastic leukemia
- hematopoietic stem cell
- cardiovascular events
- end stage renal disease
- randomized controlled trial
- newly diagnosed
- ejection fraction
- bone marrow
- stem cell transplantation
- type diabetes
- cardiovascular disease
- metabolic syndrome
- prognostic factors
- kidney transplantation
- coronary artery disease
- machine learning
- insulin resistance
- low dose
- single cell
- dna methylation
- high dose
- deep learning