A precision medicine classification for treatment of acute myeloid leukemia in older patients.
Alice S MimsJessica KohlschmidtUma BorateJames S BlachlyShelley OrwickAnn-Kathrin EisfeldDimitrios PapaioannouDeedra NicoletKrzysztof MrόzekEytan SteinBhavana BhatnagarRichard M StoneJonathan E KolitzEunice S WangBayard L PowellAmy BurdRoss L LevineBrian J DrukerClara D BloomfieldJohn C ByrdPublished in: Journal of hematology & oncology (2021)
By classifying patients through this genomic algorithm, outcomes were poor and not unexpected from a non-algorithmic, non-dominant VAF approach. The exception is 30-day ED rate typically is not available for intensive induction for individual genomic groups and therefore difficult to compare outcomes with targeted therapeutics. This Alliance data supported the use of this algorithm for patient assignment at the initiation of the Beat AML study. This outcome data was also used for statistical design for Beat AML substudies for individual genomic groups to determine goals for improvement from intensive induction and hopefully lead to more rapid approval of new therapies. Trial registration ClinicalTrials.gov Identifiers: NCT00048958 (CALGB 8461), NCT00900224 (CALGB 20202), NCT00003190 (CALGB 9720), NCT00085124 (CALGB 10201), NCT00742625 (CALGB 10502), NCT01420926 (CALGB 11002), NCT00039377 (CALGB 10801), and NCT01253070 (CALGB 11001).
Keyphrases
- acute myeloid leukemia
- machine learning
- deep learning
- end stage renal disease
- emergency department
- big data
- copy number
- ejection fraction
- clinical trial
- allogeneic hematopoietic stem cell transplantation
- electronic health record
- chronic kidney disease
- newly diagnosed
- randomized controlled trial
- metabolic syndrome
- small molecule
- prognostic factors
- type diabetes
- study protocol
- peritoneal dialysis
- blood pressure
- acute lymphoblastic leukemia
- case report
- neural network
- phase iii
- loop mediated isothermal amplification