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Timely Molecular Monitoring and Achievement of Major Molecular Response in Chronic Myeloid Leukemia

2016· article· en· W2980009311 on OpenAlexaffabout
Adi J. Klil‐Drori, Laurent Azoulay, Hui Yin, Alexa Del Corpo, Michaël Harnois, Michel‐Olivier Gratton, Inès Chamakhi, Robert Delage, Luigina Mollica, Pierre Laneuville, Harold J. Olney, Lambert Busque, Sarit Assouline

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsHôpital Notre-DameMcGill University Health CentreHôpital du Sacré-Cœur de MontréalHôpital Maisonneuve-RosemontHôpital de l'Enfant-JésusJewish General Hospital
Fundersnot available
KeywordsMedicineInternal medicineOdds ratioConfidence intervalNilotinibMyeloid leukemiaCohortGeneralized estimating equationOncologyPediatricsImatinib

Abstract

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Abstract Background: Timely molecular monitoring is the cornerstone of chronic myeloid leukemia (CML) treatment guidelines. These guidelines are based on the design of clinical trials, but none have been validated prospectively. We hypothesized that timely molecular monitoring in routine patient care increases the likelihood of achieving major molecular response (MMR) in CML. Methods: We conducted a prospective cohort study using the Québec CML Registry, which comprises 713 patients from 16 hospitals. Patients with newly-diagnosed CML (2009-2014) and measurable disease by quantitative PCR were followed from tyrosine kinase inhibitor (TKI) initiation. Timely PCR (tPCR) was defined as a PCR performed at 2-4, 11-13, and 17-19 months. (Figure). Study outcome was the achievement of MMR at 25 months, defined as international scale ratio (IS) <0.1% or a 3-log reduction in BCR-ABL1 copy number. Achievement of MMR was determined using any PCR during follow-up. Generalized estimating equations (GEE) using an exchangeable correlation structure, to account for patient clustering by center, were used to estimate odds ratios (ORs) with 95% confidence intervals (CIs) of achieving MMR comparing adherence and nonadherence to tPCR. The models were adjusted for age, sex, first-line TKI, year of study entry, and Charlson comorbidity index. Results: A total of 246 patients with 25 months of follow-up were included in the analysis (Table 1). Patients were excluded due to diagnoses before 2009 (350), insufficient follow-up (76), and other (41). The mean (standard deviation) age was 56.1 (15.5), 43.9% were female; 67.5% were started on imatinib, and 47.6% were treated in higher-volume (>50 CML patients) centers. Timely PCRs were performed in 76.3%, 69.5%, and 61.0% of patients at 2-4, 11-13, and 17-19 months, respectively. When compared with not performing tPCRs, performing one and two tPCRs were associated with achieving an MMR by 25 months (OR: 17.05, 95% CI: 5.18-56.09 and OR: 14.96, 95% CI 3.63-61.73, respectively, Table 2). The highest OR of achieving MMR was observed among those who underwent three tPCRs (OR: 24.02, 95% CI: 7.07-81.55). Conclusions: To our knowledge, this is the first study to assess clinical outcomes associated with timely molecular monitoring in early CML. While performing one and two tPCRs was associated with achieving MMR at 25 months, the point estimate for performing three tPCRs was the highest. These findings indicate that timely monitoring may allow for faster switching of TKI, which ultimately permits patients with early failure to "catch up." Alternatively, more regular testing may increase patient adherence to therapy. If replicated, these findings support routine and punctual monitoring of patients on TKI therapy. Disclosures Assouline: Pfizer: Speakers Bureau; BMS: Speakers Bureau.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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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Citations0
Published2016
Admission routes2
Has abstractyes

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