scholarly journals PCN285 USE OF REAL-WORLD DATA AND MACHINE LEARNING TO REDUCE TREATMENT-LIMITING ADVERSE EVENTS IN AN ACUTE MYELOID LEUKEMIA CLINICAL TRIAL POPULATION

2020 ◽  
Vol 23 ◽  
pp. S74
Author(s):  
R. Buderi ◽  
J. Ransom ◽  
A. Galaznik ◽  
M. Berger
2019 ◽  
Vol 19 ◽  
pp. S228
Author(s):  
Júlia Gaál-Weisinger ◽  
Alexandra Raska ◽  
Szilvia Krizsán ◽  
Ilona Tárkányi ◽  
Judit Demeter ◽  
...  

2021 ◽  
Vol 11 (10) ◽  
Author(s):  
Christina Rautenberg ◽  
Friedrich Stölzel ◽  
Christoph Röllig ◽  
Matthias Stelljes ◽  
Verena Gaidzik ◽  
...  

AbstractTo investigate the efficacy and toxicities of CPX-351 outside a clinical trial, we analyzed 188 patients (median age 65 years, range 26–80) treated for therapy-related acute myeloid leukemia (t-AML, 29%) or AML with myelodysplasia-related changes (AML-MRC, 70%). Eighty-six percent received one, 14% two induction cycles, and 10% received consolidation (representing 22% of patients with CR/CRi) with CPX-351. Following induction, CR/CRi rate was 47% including 64% of patients with available information achieving measurable residual disease (MRD) negativity (<10−3) as measured by flow cytometry. After a median follow-up of 9.3 months, median overall survival (OS) was 21 months and 1-year OS rate 64%. In multivariate analysis, complex karyotype predicted lower response (p = 0.0001), while pretreatment with hypomethylating agents (p = 0.02) and adverse European LeukemiaNet 2017 genetic risk (p < 0.0001) were associated with lower OS. Allogeneic hematopoietic cell transplantation (allo-HCT) was performed in 116 patients (62%) resulting in promising outcome (median survival not reached, 1-year OS 73%), especially in MRD-negative patients (p = 0.048). With 69% of patients developing grade III/IV non-hematologic toxicity following induction and a day 30-mortality of 8% the safety profile was consistent with previous findings. These real-world data confirm CPX-351 as efficient treatment for these high-risk AML patients facilitating allo-HCT in many patients with promising outcome after transplantation.


2021 ◽  
Author(s):  
Jie Xu ◽  
Hao Zhang ◽  
Hansi Zhang ◽  
Jiang Bian ◽  
Fei Wang

Restrictive eligibility criteria for clinical trials may limit the generalizability of treatment effectiveness and safety to real-world patients. In this paper, we propose a machine learning approach to derive patient subgroups from real-world data (RWD), such that the patients within the same subgroup share similar clinical characteristics and safety outcomes. The effectiveness of our approach was validated on two existing clinical trials with the electronic health records (EHRs) from a large clinical research network. One is the donepezil trial for Alzheimer's disease (AD), and the other is the Bevacizumab trial on colon cancer (CRC). The results show that our proposed algorithm can identify patient subgroups with coherent clinical manifestations and similar risk levels of encountering severe adverse events (SAEs). We further exemplify that potential rules for describing the patient subgroups with less SAEs can be derived to inform the design of clinical trial eligibility criteria.


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