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Impact on costs and outcomes of multi-gene panel testing for advanced solid malignancies: A cost consequence analysis using linked administrative data.

2023· article· en· W4379281572 on OpenAlexaffabout
Alberto Hernando‐Calvo, Paul Nguyen, Philippe L. Bédard, Kelvin Chan, Ramy Saleh, Deirdre Weymann, Celeste Yu, Eitan Amir, Dean A. Regier, Bishal Gyawali, Danielle Kain, Brooke E. Wilson, Craig C. Earle, Nicole Mittmann, Albiruni Ryan Abdul Razak, Trevor J. Pugh, Christine Williams, Lillian L. Siu, Timothy P. Hanna

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsOntario Institute for Cancer ResearchQueen's UniversityHealth Sciences CentreUniversity of TorontoUniversity Health NetworkMcGill University Health CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesBC Cancer AgencyPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineCohortPropensity score matchingClinical endpointClinical trialInternal medicineReimbursementOncologyHealth careGerontologyDemography

Abstract

fetched live from OpenAlex

6650 Background: To date, economic analyses of tissue-based next generation sequencing genomic profiling (NGS) have required models with multiple assumptions, with little real-world evidence on overall survival (OS), clinical trial enrollment or end-of-life (EOL) quality of care. The OCTANE clinical trial (NCT02906943) is a prospective study evaluating the role of NGS for advanced solid tumors in Ontario, Canada. We performed a cost consequence analysis of OCTANE. Methods: We undertook a longitudinal, propensity score-matched retrospective cohort study using linked administrative data. OCTANE patients (pts) at Princess Margaret Cancer Centre from August 2016 until March 2019 undergoing NGS panel testing (555 or 163-gene panels) were matched with contemporary controls from across Ontario not enrolled in OCTANE. Patients were matched according to 19 variables including age, sex, place of residence, tumor site, symptom burden, income quintile, comorbidities and prior lines of systemic therapy. Primary outcomes were mean per capita health care costs (2019 Canadian dollars [CAD]) from the public payer’s perspective, OS, clinical trial enrollment and EOL quality metrics. Full 2-year follow-up data was available. Sensitivity analyses considered alternative matched cohort specifications. Results: There were 782 OCTANE pts with 782 matched controls. Variables were balanced after matching (standardized difference [std. diff.]<0.10). Most common tumor sites were: Ovary (30.4%), endometrium (15.0%) breast (12.3%) and colon (8.6%). OCTANE pts had higher mean healthcare costs than controls ($79,702 vs. $59,550), mainly due to costs of oncology visits ($33,165 vs. $26,197), outpatient clinic visits ($8,696 vs. $5,114) and emergency visits ($1,723 vs. $1,373) (all p<0.05). Publicly funded drug costs were less for OCTANE pts ($20,015 vs. $24,465). Overall, OCTANE enrollment was not associated with improved OS (restricted mean survival time (RMST) [standard error]: 1.50 (±0.03) vs. 1.44 (±0.03) years, log-rank p=0.153), but OCTANE was associated with longer OS in ovarian cancer (RMST: 1.69 (±0.05) vs. 1.45 (±0.06) years, p=0.011) and biliary tract tumors (RMST: 1.16 (±0.13) vs. 0.80 (±0.11) years, p=0.02). Importantly, OCTANE correlated with increased clinical trial enrollment (25.5% vs. 9.5%, p<0.001) and better EOL quality due to fewer deaths in hospital (10.2% vs 16.4%, p=0.003). Results were robust in sensitivity analysis. Conclusions: There was an increase in healthcare costs associated with NGS testing for advanced cancers. The impact on OS was not significant in the overall population, but varied across tumor types. OCTANE was associated with greater trial enrollment, lower publicly funded drug costs and fewer in hospital deaths suggesting important considerations in determining the value of NGS for advanced cancers. Clinical trial information: NCT02906943 .

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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)0.011
metaresearch head score (Gemma)0.027
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 score0.116
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.887
GPT teacher head0.666
Teacher spread0.221 · 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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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