How comparable are patient outcomes in the "real-world" with populations studied in pivotal AML trials?
Ing Soo TiongMeaghan WallAshish BajelAkash KalroShaun FlemingAndrew W RobertsNisha ThiagarajahChong Chyn ChuaMaya LatimerDavid YeungPaula MarltonAmanda JohnstonAnoop K EnjetiChun Yew FongGavin CullStephen LarsenGlen KennedyAnthony SchwarerDavid KippSundra RamanathanEmma VernerCampbell TileyEdward MorrisUwe HahnJohn MooreJohn TaperDuncan PurtillPauline WarburtonWilliam StevensonNicholas MurphyPeter TanAshanka BeligaswatteHoward MutsandoMark HertzbergJake ShorttFerenc SzaboKarin DunneAndrew H Weinull nullPublished in: Blood cancer journal (2024)
Despite an increasing desire to use historical cohorts as "synthetic" controls for new drug evaluation, limited data exist regarding the comparability of real-world outcomes to those in clinical trials. Governmental cancer data often lacks details on treatment, response, and molecular characterization of disease sub-groups. The Australasian Leukaemia and Lymphoma Group National Blood Cancer Registry (ALLG NBCR) includes source information on morphology, cytogenetics, flow cytometry, and molecular features linked to treatment received (including transplantation), response to treatment, relapse, and survival outcome. Using data from 942 AML patients enrolled between 2012-2018, we assessed age and disease-matched control and interventional populations from published randomized trials that led to the registration of midostaurin, gemtuzumab ozogamicin, CPX-351, oral azacitidine, and venetoclax. Our analyses highlight important differences in real-world outcomes compared to clinical trial populations, including variations in anthracycline type, cytarabine intensity and scheduling during consolidation, and the frequency of allogeneic hematopoietic cell transplantation in first remission. Although real-world outcomes were comparable to some published studies, notable differences were apparent in others. If historical datasets were used to assess the impact of novel therapies, this work underscores the need to assess diverse datasets to enable geographic differences in treatment outcomes to be accounted for.
Keyphrases
- clinical trial
- acute myeloid leukemia
- flow cytometry
- papillary thyroid
- electronic health record
- big data
- ejection fraction
- newly diagnosed
- acute lymphoblastic leukemia
- stem cell transplantation
- bone marrow
- high dose
- systemic lupus erythematosus
- stem cells
- genetic diversity
- mesenchymal stem cells
- diffuse large b cell lymphoma
- emergency department
- randomized controlled trial
- open label
- replacement therapy
- low dose
- metabolic syndrome
- allogeneic hematopoietic stem cell transplantation
- machine learning
- magnetic resonance imaging
- deep learning
- magnetic resonance
- rheumatoid arthritis
- prognostic factors
- skeletal muscle
- hematopoietic stem cell
- health information
- quality improvement
- smoking cessation
- patient reported outcomes
- patient reported
- contrast enhanced