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Record W2987515732 · doi:10.1182/blood-2019-124375

Integrating Geriatric Assessment and Genetic Profiling to Personalize Therapy Selection in Older Adults with Acute Myeloid Leukemia (AML)

2019· article· en· W2987515732 on OpenAlexaboutno aff
Vijaya Raj Bhatt, Christopher Wichman, Zaid Al‐Kadhimi, Thuy T. Koll, Ann M. Berger, Jamés O. Armitage, Sarah A. Holstein, Krishna Gundabolu, Lori J. Maness

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineOncologyChemotherapy regimenMyeloid leukemiaPerformance statusIntensive care medicineChemotherapy

Abstract

fetched live from OpenAlex

Introduction: Geriatric assessment can predict the risk of toxicities of chemotherapy in older adults. Genetic risk categories correlate with survival following intensive chemotherapy in AML. Integrating geriatric assessment for patient profiling and genetic profiling of leukemic cells represents an innovative precision medicine approach to personalize therapy selection in older adults with AML. We report results of a pre-planned interim analysis of a pragmatic phase II trial using such strategy that has an overarching goal to reduce early mortality (NCT03226418). Methods: Patients ≥60 years with a new diagnosis of AML underwent geriatric assessment prior to initiation of treatment. Geriatric assessment of physical function, cognitive function and comorbidity burden were used to determine fitness for intensive chemotherapy (Table 1). Additional assessment included Karnofsky Performance Scale (KPS), Patient Health Questionnaire-9 (PHQ-9), and Mini Nutritional Assessment-Short Form (MNA). Genetic profiling for therapy selection relied on karyotyping and followed the 2017 European LeukemiaNet criteria. While available mutation test results were incorporated to risk stratify, the study did not require to wait for the results prior to therapy initiation given an anticipated turnaround time of 1-2 weeks for mutation test results. Therapy selection followed the algorithm demonstrated in Figure 1. Patients with good or intermediate-risk AML received intensive chemotherapy such as 7+3 +/- gemtuzumab or midostaurin if determined to be fit. Patients with high-risk AML received low-intensity chemotherapy such as a hypomethylating agent with or without novel drugs, or CPX 351 if they were fit and met the FDA-approved indications. Patients with organ dysfunction (e.g. creatinine ≥2 mg/dl) and those requiring chemotherapy for other malignancy received low-intensity chemotherapy. Chemotherapy at diagnosis or follow up could be administered in community oncology settings. Patients were followed for assessment of quality of life, functional and oncologic outcomes. Results: Between July 2017-June 2019, a total of 31 patients (including 1 MDS patient considered screen failure) were enrolled. The pre-planned interim analysis results are based on the first 27 AML patients. Baseline characteristics included a median age of 70 years (range 60-84 years), 56% female, 96% white, and a median KPS of 80% (range 60-100%). As presented in Table 1, over half of the patients had ≥3 comorbidities, impairment in objective physical function (short physical performance battery) and Montreal Cognitive Assessment. Additionally, 67% had poor nutritional status (MNA score of ≤11) and 26% had abnormal depression screen (PHQ-9 score of ≥10). Risk categories included adverse (64%), intermediate (16%), and good-risk AML (20%). Patients had one or more of the following mutations: FLT3 ITD (11%), NPM1 (22%), biallelic CEBPA (4%), IDH1 (15%), IDH2 (8%), RUNX1 (15%), ASXL1 (22%), and TP53 (19%). Three patients received intensive chemotherapy; CPX 351 (n=2) or 7+3+ gemtuzumab (n=1). Other patients received decitabine or azacitidine alone (n=16), azacitidine and venetoclax (n=5) or decitabine and midostaurin (n=3). The median time from diagnosis to therapy initiation was 7 days (0-20 days) whereas the median time from enrollment to therapy initiation was 2 days (range 0-9). Mortality at 30 days from diagnosis was 3.7% (95% confidence interval, CI 0.7-18.3%) and at 90 days was 29.6% (95% CI 15.9-48.5%). Mortality compared favorably to an unmatched historical cohort of patients ≥60 years treated at our center between 2011-2016, where 30-day mortality was 30% (95% CI 22-40%) and 90-day was 41% (95% CI 32-52%) (Future Oncol. 2019;15:1989-95). Conclusions: Our model to personalize AML therapy selection represents an innovative approach to precision medicine that incorporates both geriatric assessment for patient profiling and genetic profiling of leukemia cells. Geriatric assessment demonstrated high frequency of impairment in objective physical and cognitive function. Patients were able to start therapy within a median of 2 days following enrollment. Pragmatic aspects of the trial included broad eligibility criteria and co-management of patients with community oncologists. Our pre-planned interim analysis data appear promising with lower rates of early mortality compared to unmatched historical controls. Disclosures Bhatt: Pfizer: Consultancy; CSL Behring: Consultancy; Agios: Consultancy; Abbvie: Consultancy; Partner therapeutics: Consultancy; Incyte: Consultancy, Research Funding; Tolero Pharmaceuticals: Research Funding; National Marrow Donor Program: Consultancy. Al-Kadhimi:Celldex Biotech: Other: Stocks; Seattle Genetics: Other: Stocks. Armitage:Oncology Analytics: Consultancy; Partner Therapeutics: Consultancy; Samus Therapeutics: Consultancy; Ascentage: Consultancy; Tesaro bio: Membership on an entity's Board of Directors or advisory committees; Union Pacific: Consultancy. Holstein:GSK: Consultancy; Genentech: Membership on an entity's Board of Directors or advisory committees; Sorrento: Consultancy; Celgene: Consultancy; Takeda: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Gundabolu:Samus Therapeutics: Research Funding; Jazz pharmaceuticals: Consultancy; Pfizer: Consultancy; Novartis: Consultancy. OffLabel Disclosure: Single agent azacitidine and decitabine as well as decitabine in combination with midostaurin are not approved by the US FDA for acute myeloid leukemia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.262
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations7
Published2019
Admission routes1
Has abstractyes

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