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Impact Of Dedicated CML Clinic On The Outcome Of Patients With Newly Diagnosed CML: Experience Of a Single Canadian Center

2013· article· en· W2527307458 on OpenAlexaffabout
Moussab Damlaj, Yury Monczak, Sarit Assouline

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsMedicineImatinib mesylateImatinibInternal medicineAnagrelideSingle CenterPediatricsOncologySurgeryMyeloid leukemia

Abstract

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Abstract Background The outcome of patients with CML has improved significantly since the introduction of imatinib (O’Brien et al 2003). In the landmark IRIS trial, the rate of complete cytogenetic response (CCyR) was 69% by 12 months and 87% by 60 months and 7% of patients progressed to accelerated or blast crisis (Druker et al 2006). Multiple studies show that bcr-abl transcript level at 3 months can predict outcome and can be used to guide therapeutic strategies (Marin et al 2012). Additionally, patients achieving CCyR within 2 years on imatinib were projected to have a normal life expectancy (Gambacorti-Passerini et al 2011). Surprisingly, a survey of the CML World Registry involving over 1800 patients revealed that only 10% and 15% of patients had cytogenetic and molecular evaluation at 3 months, respectively. At our institution, we have a dedicated, multidisciplinary CML clinic with RT-PCR assay in house. We sought to determine how the outcome of our CP-CML patients, followed every 3 months and according to the ELN (European Leukemia Network) guidelines compares to clinical trial results. Methods Patients with CP-CML treated with imatinib first line between January 2004 and December 2012 were included in this retrospective chart review. Diagnosis was based on bone marrow aspirate with blast count and cytogenetic and/or FISH, and bcr/abl transcript RT-PCR using the IS method. Patients presenting in AP or BP, or those having previous lines of therapy except hydroxyurea or anagrelide were excluded. Baseline demographic, medical history, Sokal score at presentation and monitoring frequency for hematologic, cytogenetic and molecular responses were extracted. Definitions of cytogenetic and molecular responses were in accordance with the ELN guidelines. Cumulative rates of cytogenetic and molecular responses were estimated using the Kaplan–Meier method. Data for patients not achieving an adequate response were censored at the last follow up visit. Differences between groups were calculated by log rank test. The study received IRB approval at our institution. Results 55 eligible patients were identified. Median age was 53 years (17-92) and 33 (60%) were male. Distribution of Sokal score was low in 28 (50.9%), intermediate in 22 (40%) and high in 5 (9%). Median follow up duration was 53 months (range 3-106). Seven patients (12.7%) required switching to second line TKI, six for inadequate response, one for intolerance and one patient required switch to third line TKI for inadequate response. Molecular testing at 3 and 18 months post start of imatinib was performed in 48 pts (87.3%) and 53 (96.4%), respectively. At 12 months, 49 (89.1%) had cytogenetic evaluation. Estimated rates of CCyR at 6 months, 12 months, 18 months and 24 months were 46.6%, 69.2%, 78.7% and 88.2%, respectively. Estimated rates of MMR at 6 months, 12 months, 18 months and 24 months were 23.9%, 41.2%, 58.8% and 70.6%, respectively. When stratified by Sokal score, rate of CCyR at 12 months was 82.1%, 72.7% and 60% (p 0.8840) for low, intermediate and high scores, respectively. Conclusions CP-CML patients followed in a dedicated CML clinic receive more rigorous follow-up and monitoring and achieve response rates similar to that of clinical trials. Additionally, in this small cohort of patients, discontinuation rate of imatinib was substantially lower than previously reported. Disclosures: No relevant conflicts of interest to declare.

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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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.277
Teacher spread0.254 · 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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Citations1
Published2013
Admission routes2
Has abstractyes

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