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Record W4389229290 · doi:10.1182/blood-2023-185370

Prospective Assessment of Co-Morbidities and Framingham Risk Score in Newly Diagnosed Chronic Myeloid Leukemia (CML) Patients and Its Impact on Clinical Outcomes Following Frontline TKI Therapy: Toronto CML Genomic Alliance in Greater Toronto Area & Ontario (TCGA-GTA)

2023· article· en· W4389229290 on OpenAlexaffabout
May Chiu, Gopila Gupta, María Agustina Perusini, Eshrak Al‐Shaibani, Jenny Ho, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsLondon Health Sciences CentrePrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineComorbidityInternal medicineFramingham Risk ScoreFamily medicineDisease

Abstract

fetched live from OpenAlex

Introduction Front-line (1L) tyrosine kinase inhibitor (TKI) drug selection is a pearl of clinical practice in CML. It requires consideration of multiple aspects including the goal of therapy, potency of the drug as well as comorbidity of the patient (pt). Contemporary CML practice emphasizes the importance of comorbidity for TKI selection. However, such data is scarce particularly the prevalence of comorbidity and its impact on 1L drug selection. We are developing a shared care model with community hematologists in Greater Toronto Area (TCGA-GTA). Newly diagnosed CML pts in the community will be referred to the study core center for prospective enrolment into the CML registry, informed consenting for genetic testing/future research, and evaluation of baseline comorbidity. Pt will return to referring hematologists for management of CML, including 1L TKI treatment as per standard of care, and is scheduled to visit the study center at 6 and 12 months, then annually for 10 years. It gives us a unique opportunity to evaluate the impact of comorbidity on 1L TKI drug selection. Patients and method Adult pts with newly diagnosed CML of any phase within 2 months of diagnosis are eligible for the study unless they are not able to visit study core center. Comorbidities and Framingham risk score (FRS) were assessed at the time of initial evaluation. Mutation profile testing is done by a bar-coded error-corrected sequencing platform for 40 genes developed at the Ontario Institute for Cancer Research. Cumulative incidence of molecular response with 2 log (MR2), 3 log (MR3), 4 log reduction or deeper (MR4), and treatment failure (TF) were calculated using cumulative incidence method considering competing risk. Event-free survival (EFS) was defined as time from start of TKI until TKI stop/switch, TF or death. Failure-free survival (FFS) was defined as time from start of TKI to TF [primary resistance, loss of complete cytogenetic response, new additional cytogenetic abnormalities (ACAs), and progression to accelerated or blasts phase (AP/BP)] or death. Results From November 2020 until June 31, 2023 (data cutoff), a total of 77 pts were enrolled in the study. The median follow-up time was 12 months. The frequency of 1L TKI selection was as follows: IMATINIB (IM) (n=23, 30%), DASATINIB (n=25, 32%), NILOTINIB (n=22, 29%), BOSUTINIB (n=1, 1%), ASCIMINIB (n=6, 8%). Majority of the pts were male (n=51, 66%). Ninety-three percent of pts were in chronic phase at diagnosis, while 4% and 3% were in AP and BP, respectively. Presence of ACAs was detected in 18 pts (23%), 10 of which were high risk, while 6% (n=5) were not evaluable. The baseline clinical and disease characteristics of pts on 1L IM vs other TKIs (2G-TKI) are summarised in Table 1. Of note, pts on IM were significantly older with higher FRS and comorbidities, including coronary artery disease, diabetes mellitus, and hypertension. The OS of all pts at 1 year was 98.5% (95% confidence interval 90%-99.8%). At 1 year, pts with intermediate to high risk FRS had lower FFS (73.2% vs 86.5%, p=0.037) while history of hyperlipidemia was associated with lower EFS (59.3% vs 81.5%, p=0.023) and 6 times more likely to stop or switch therapy due to intolerance or resistance (HR 5.9, p=0.002). Baseline comorbidities did not demonstrate signifincant impact on MR2 and MR3 at 1 year but pts with low FRS were more likely to achieve MR4 within 1 year (22.3% vs 0%, p=0.023). Age did not affect any of the clinical and molecular endpoints. Compared to 2G-TKI, IM had worse EFS (HR 3.93, p=0.003), FFS (HR 5.66, p=0.011), and MR2 (47.3% vs 84.4%, p=0.003) at 1 year. Further, IM pts experienced higher rate of any side effects compared to those on 2G-TKI (69.6% vs 31.5%, p=0.003). Conclusion The TCGA-GTA study provides valuable insights into the impact of comorbidity on selection of 1L TKI and its outcome in a real-world setting. Pts who are older and with higher FRS and comorbidities tend to receive 1L IM. However, pts on 1L 2G TKIs are more likely to reach treatment goals faster with lower probability of TF. The present study did not reach a clear conclusion whether the worse outcomes in the pts receiving IM are from IM treatment or concurrent risk factors such as age and comorbidities, thus further study with an expanded cohort and longer follow-up is required. Elderly pts or those with comorbidities would need alternative treatment with better efficacy and tolerability, such as Asciminib, that can overcome comorbidity issues in the future.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.773
Threshold uncertainty score0.450

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.348
Teacher spread0.312 · 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
Published2023
Admission routes2
Has abstractyes

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