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2302 Novel Data Analytics Identify Predictors of Quality-of-Life Trajectories in Patients with AML or High-Risk Myeloid Neoplasms

Program: Oral and Poster Abstracts
Session: 906. Outcomes Research—Myeloid Malignancies: Poster I
Hematology Disease Topics & Pathways:
Research, Acute Myeloid Malignancies, AML, MDS, adult, Clinical Practice (Health Services and Quality), MPN, elderly, Clinical Research, health outcomes research, Chronic Myeloid Malignancies, patient-reported outcomes, Diseases, Myeloid Malignancies, Study Population, Human
Saturday, December 10, 2022, 5:30 PM-7:30 PM

Jordan Gauthier, MD1,2, Bianca Furtuna3*, Jacopo Mangiavacchi3*, Shahrzad Gholami3*, Juan Lavista Ferres3*, Rahul Dodhia3*, Amir T. Fathi, MD4,5, Andrew M. Brunner, MD6, Aaron T. Gerds, MD, MS7, Mikkael A. Sekeres, MD8, Bruno C. Medeiros, MD9, Eunice S. Wang, MD10, Paul J Shami, MD11, Kehinde Adekola, MBBS, MS12, Selina M. Luger, MD, FRCPC13, Maria R. Baer, MD14, David A Rizzieri, MD15, Tanya Wildes, MD, MSc16, Jamie L. Koprivnikar, MD17, Julie Smith, MD18*, Mitchell A. Garrison, MD19*, Kiarash Kojouri, MD20*, Frederick R. Appelbaum, MD2,21, Mary-Elizabeth M. Percival, MD22,23, Stephanie J. Lee, MD, MPH24,25 and Mohamed L. Sorror, MD, MSc26,27

1Fred Hutchinson Cancer Research Center, Seattle, WA, USA, Seattle, WA
2Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA
3Microsoft, Seattle
4Director of Leukemia Program, Massachusetts General Hospital, Boston, MA
5Associate Professor of Medicine, Harvard Medical School, Boston, MA
6Dana-Farber Cancer Institute, Boston, MA
7Department of Hematology and Medical Oncology,, Cleveland Clinic Taussig Cancer Institute, Cleveland, OH
8University of Miami, Sylvester Comprehensive Cancer Center, Miami, FL
9Department of Medicine, Division of Hematology, Stanford University School Medicine, Stanford, CA
10Leukemia Service, Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY
11Division of Hematology and Hematologic Malignancies, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
12Department of Medicine, Division of Hematology/Oncology and the Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Northwestern University Feinberg School of Medicine, Chicago, IL
13Cellular Therapy and Transplantation, Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA
14University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore, MD
15Dept. of Medicine, Division of Hematologic Malignancies and Cellular Therapy, Duke University Medical Center, Durham, NC
16University of Nebraska Medical Center, Omaha, NE
17Georgetown University Hospital, Hackensack, NJ
18Confluence Health/Wenatchee Valley Hospital, Wenatchee, WA
19Confluence Health/Wenatchee Valley Hospital and Clinic, Wenatchee, WA
20Skagit Valley Hospital Regional Cancer Care Center, Mount Vernon, WA
21Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA
22Department of Medicine, University of Washington, Seattle, WA
23Fred Hutchinson Cancer Center, Seattle, WA
24Department of Medicine, Division of Medical Oncology, University of Washington, Seattle, WA
25Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA
26Clinical Research Division, Fred Hutchinson Cancer Research Ctr., Seattle, WA
27Division of Medical Oncology, Department of Medicine, University of Washington School of Medicine, Seattle, WA

Background:

Acute myeloid leukemia (AML) remains fatal in most patients (pts) with a 5-year survival probability of approximately 30% (less than 10% in pts aged 65 or older). Beyond survival, quality of life (QOL) can be significantly impaired by both disease and treatment-related factors. There is an urgent need to both characterize and identify factors predictive of QOL trajectories. Leveraging prospective data from 503 pts enrolled on an observational clinical trial, we implemented a novel statistical approach using non-supervised longitudinal clustering and ordinal logistic regression. We successfully identified: i) distinct QOL trajectories, ii) baseline factors independently associated with QOL trajectories.

Methods:

We analyzed data from pts with AML (90%) or high-risk myelodysplastic, myeloproliferative syndrome, or myelofibrosis (10%) enrolled on an observational clinical trial (NCT01929408) between 2013 and 2017 at 13 centers. QOL questionnaires were collected at the time of enrollment and approximately at months 1, 3, 6, 9, 12, 18, and 24 after study enrollment. Pts with a least six longitudinal data points available for QOL were included (n=503). Missing QOL data were imputed using linear interpolation. QOL was evaluated using the visual analog scale of the EQ5D (0, worst imaginable health state; 100, best imaginable health state). Deceased pts were considered to have a QOL of 0. Disease risk was assessed using the ELN 2017 criteria. Comorbidities and frailty were measured using the hematopoietic cell transplant comorbidity index (HCT-CI) and the NIH Toolbox 4-Meter Walk Gait Speed Test, respectively. QOL trajectories were clustered using non-supervised k-means based on Euclidian distances (TimeSeriesKMeans function of the tslearn library). Optimal cluster number was determined using the silhouette plot approach. Multivariable ordinal logistic regression was applied to predict QOL trajectories as a function of baseline variables using the rms R package. All analyses were done in Python (3.5) or R (4.0.2).

Results:

Six distinct QOL trajectories could be clustered and ordered from very good (n=109, 22%), good (n=76, 15%), intermediate 1 (n=49, 10%), intermediate 2 (n=86, 17%), poor (n=88, 17%), and very poor (n=95, 19%), as shown in the Figure. Comparisons of extreme clusters showed that pts in the very good QOL trajectory group were at baseline significantly younger (median age, 60 versus 66, respectively, p<0.001), had fewer comorbidities (median HCT-CI, 2 versus 4, respectively, p<0.001), less adverse AML cytogenetic/molecular risk (26% versus 48%, respectively, p=0.004), and were less frail (median walking speed, 0.89 versus 0.69m/s, p=0.009) compared to those in the very poor QOL trajectory group. A higher proportion of very good QOL pts received intensive chemotherapy (91% versus 62%, p<0.001) followed by consolidative allo-HCT (68% versus 11%, p<0.001) after achieving complete response/complete response with incomplete hematologic recovery (94% versus 40%, p<0.001).

Multivariable modeling including baseline variables showed that older age (OR per 1-year increase, 1.34; 95%CI, 1.09-1.66; p=0.006), higher HCT-CI (OR per 1-unit increase, 1.66; 95%CI, 1.27-2.16; p<0.001), and frailty (OR per m/s increase, 0.8; 95%CI, 1.09-1.66; p=0.06) were associated with worse QOL trajectories. Favorable disease risk (favorable versus adverse: OR, 0.45; 95%CI, 0.28-0.71; p<0.001) and intensive therapy (OR, 0.54; 95%CI, 0.36-0.83; p=0.004) were independently associated with more favorable QOL trajectories.

Conclusion:

Unsupervised k-means clustering identified groups of pts with distinct QOL trajectories. Multivariable ordinal regression identified age, HCT-CI, disease risk and treatment intensity as independently associated with QOL trajectories. Favorable QOL trajectories were more likely to be achieved in younger, non-comorbid pts with favorable disease risk and receiving intensive therapy. Intensive therapy was independently associated with more favorable QOL trajectories even in older pts and in those with higher comorbidity burden. Probabilities of achieving the most favorable QOL trajectory in pts aged ≥60 after intensive therapy were <40% (<25% in case of adverse disease risk), indicating a critically unmet need to improve outcomes in older pts with high-risk myeloid neoplasms.

Disclosures: Gauthier: Celgene (A BMS Company): Research Funding; Juno Therapeutics - A BMS Company: Research Funding; Multerra Bio: Consultancy; Sobi: Research Funding; Legend Biotech: Consultancy; Kite Pharma: Consultancy. Furtuna: Microsoft: Current Employment. Mangiavacchi: Microsoft: Current Employment. Gholami: Microsoft: Current Employment. Lavista Ferres: Microsoft: Current Employment. Dodhia: Microsoft: Current Employment. Fathi: Ipsen: Consultancy; Foghorn: Consultancy; Genentech: Consultancy; Immunogen: Consultancy; Orum: Consultancy; Mablytics: Consultancy; Astellas: Consultancy; Novartis: Consultancy; EnClear: Consultancy; Abbvie/Servier: Consultancy, Other: Clinical Trial Support; MorphoSys, Novartis, Pfizer, Seattle Genetics, Takeda, Trillium Therapeutics, and Trovagene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Forma: Consultancy; PureTech: Consultancy; Amgen: Consultancy; Morphosys: Consultancy; AbbVie, Agios, Bristol Myers Squibb, Servier, and Takeda: Research Funding; Celgene/BMS: Consultancy, Other: Clinical Trial Support; AbbVie, Agios, Amgen, Astellas Pharma, Blueprint Medicines, Bristol Myers Squibb, Daiichi Sankyo, Foghorn Therapeutics, Forty Seven, Inc., Genentech, Ipsen, Kite Pharma, Kura Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy; Kite: Consultancy. Brunner: AstraZeneca: Research Funding; Agios: Honoraria; Acceleron: Honoraria; Takeda: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding; Taiho: Consultancy; Novartis: Consultancy, Research Funding; Keros Therapeutics: Consultancy; Janssen: Research Funding; GSK: Research Funding; Aprea: Research Funding. Gerds: Incyte Corporation: Research Funding; Imago BioSciences: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb/Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; CTI BioPharma: Membership on an entity's Board of Directors or advisory committees, Research Funding; Accurate Pharmaceuticals: Research Funding; Kratos Pharmaceuticals: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Morphosys/Constellation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; PharmaEssentia: Consultancy, Membership on an entity's Board of Directors or advisory committees. Sekeres: Takeda/Millenium: Membership on an entity's Board of Directors or advisory committees; Bristol Myers-Squibb: Membership on an entity's Board of Directors or advisory committees; Kurome: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Medeiros: Genentech: Ended employment in the past 24 months, Patents & Royalties. Wang: Macrogenics: Consultancy; Pfizer: Consultancy, Honoraria, Other: Advisory Board, Speakers Bureau; Astellas: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria, Other: member of data monitoring committee ; Kite Pharmaceuticals: Consultancy, Honoraria, Other: Advisory Board; GlaxoSmithKline: Consultancy, Honoraria, Other: Advisory Board; Daiichi Sankyo: Consultancy, Honoraria, Other: Advisory Board; Takeda: Consultancy, Honoraria, Other: Advisory Board; Jazz Pharmaceuticals: Consultancy, Honoraria, Other: Advisory Board; Mana Therapeutics: Consultancy, Honoraria; Kura Oncology: Consultancy, Honoraria, Other: Advisory Board, Steering Committee, Speakers Bureau; Gilead: Consultancy, Honoraria, Other: Advisory Board; BMS/Celgene: Membership on an entity's Board of Directors or advisory committees; Stemline Therapeutics: Consultancy, Honoraria, Other: Advisory Board, Speakers Bureau; Novartis: Consultancy, Honoraria, Other: Advisory Board; PTC Therapeutics: Consultancy, Honoraria, Other: Advisory Board; Genentech: Consultancy; Dava Oncology: Consultancy, Speakers Bureau; Rafael Pharmaceuticals: Other: Data Safety Monitoring Committee. Luger: Onconova, Celgene, Biosight, Hoffman LaRoche, and Kura: Research Funding; Syros, Agios, Daiichi Sankyo, Jazz Pharmaceuticals, Brystol Myers Squibb, Acceleron, Astellas, and Pfizer: Honoraria. Baer: Ascentage: Research Funding; Forma: Research Funding; AbbVie: Research Funding; Kura Oncology: Research Funding; Takeda: Research Funding; Kite: Research Funding. Rizzieri: Stemline: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celltron: Consultancy, Honoraria; Mustang: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; Celgene, Jazz, Gilead, Incyte, Amgen, Kite, AROG, Pharmacyclics, Seattle Genetics.: Honoraria. Wildes: Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Carevive, Sanofi: Consultancy. Appelbaum: Jasper Biotherapy: Membership on an entity's Board of Directors or advisory committees. Percival: Ascentage: Research Funding; Abbvie: Research Funding; Celgene/BMS: Research Funding; Biosight: Research Funding; Glycomimetics: Research Funding; Cardiff Oncology: Research Funding; Oscotec: Research Funding; Trillium: Research Funding; Pfizer: Research Funding; Telios: Research Funding. Lee: Incyte: Research Funding; Mallinckrodt: Consultancy, Honoraria; Kadmon: Consultancy, Honoraria, Research Funding; Pfizer: Research Funding; Syndax: Research Funding; AstraZeneca: Research Funding; Amgen: Research Funding; Equillium: Consultancy, Honoraria; Novartis: Other: Steering Committee; National Marrow Donor Program: Membership on an entity's Board of Directors or advisory committees.

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