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Record W2580021056 · doi:10.18553/jmcp.2017.23.2.214

Health Care Resource Utilization and Costs in Patients with Chronic Myeloid Leukemia with Better Adherence to Tyrosine Kinase Inhibitors and Increased Molecular Monitoring Frequency

2017· article· en· W2580021056 on OpenAlexaff
Dominick Latrémouille-Viau, Annie Guérin, Patrick Gagnon‐Sanschagrin, Katherine Dea, Benjamin G. Cohen, George J Joseph

Bibliographic record

VenueJournal of Managed Care & Specialty Pharmacy · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineMyeloid leukemiaEmergency medicineHealth careInternal medicineIncidence (geometry)Intensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Frequent molecular monitoring (qPCR tests), as recommended by evidence-based monitoring guidelines, is associated with higher adherence to tyrosine kinase inhibitors (TKIs) in the management of chronic myeloid leukemia (CML); both factors have been associated with better clinical and economic outcomes. OBJECTIVES: To (a) estimate the effect of more frequent qPCR tests on health care resource utilization (HRU) and associated costs, including direct (effect of qPCR test frequency on HRU) and indirect (through TKI adherence) effects, and (b) develop an economic model applicable to multiple clinical practice scenarios. METHODS: Adult patients newly diagnosed with CML who started TKI firstline therapy were identified from U.S. administrative claims data (2010-2015). TKI adherence (medication possession ratio [MPR]), number of inpatient days, emergency room (ER) visits, outpatient service days, and mean costs per HRU event were measured during the first year of CML treatment. Direct and indirect effects of qPCR test frequency were estimated using multivariate regression models. Subsequently, an economic model was developed to assess the overall effect of varying qPCR test frequency on HRU and associated costs during the first year of CML treatment under different clinical practice scenarios; the scenario reported is the increase from 1 to 2 qPCR tests. RESULTS: Of the 1,431 patients included, 36% had no qPCR tests, the average qPCR test frequency was 1.6, and the average MPR was 0.86 during the first year of CML treatment. The direct effect of increasing qPCR test frequency by 1 was associated with 13.0% fewer inpatient days (adjusted incidence rate ratio [adjusted IRR] = 0.87; P = 0.010); 8.3% fewer ER visits (adjusted IRR = 0.92; P = 0.043); and 3.0% more outpatient service days (adjusted IRR = 1.03; P = 0.002). Each increase of 1 test was associated with an increase in TKI adherence by 2.2 percentage points (adjusted MPR difference = 0.022; P < 0.001). When considering the indirect effect of qPCR test frequency through TKI adherence, an increase of 1 qPCR test combined with an increase in TKI adherence by 2.2 percentage points was associated with a greater reduction of inpatient days from 13.0% to 15.2%, ER visits from 8.3% to 8.6%, and a smaller increase of outpatient service days from 3.0% to 2.6%. Based on the economic model, an increase from 1 to 2 qPCR tests, considering the increase in TKI adherence, was associated with a reduction of 0.87 (95% CI = -1.49, -0.18) inpatient days and 0.06 (95% CI = -0.12, 0.05) ER visits, an increase of 0.98 (95% CI = 0.25, 1.60) outpatient service days and a cost savings of $2,918 (95% CI = -5,213, -349) per patient per year. CONCLUSIONS: Closer alignment with the monitoring guidelines' recommended qPCR test frequency and better adherence to TKIs were associated with lower HRU and medical service costs. Managed care initiatives to increase qPCR test frequency and TKI adherence might benefit from an enhanced reduction because of the interaction between both factors. DISCLOSURES: This study was funded by Novartis Pharmaceuticals, which was involved in all stages of the study and in the decision to submit the report for publication. Latremouille-Viau, Guerin, Gagnon-Sanschagrin, and Dea are employees of Analysis Group, which received consulting fees from Novartis Pharmaceuticals for work on this study. Joseph is an employee of Novartis Pharmaceuticals and owns stock in Amgen and Pfizer. Cohen was an employee of Novartis Pharmaceuticals at the time of this study. Portions of this study were presented online (beginning May 20, 2016) as part of the American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago, Illinois, on June 3-7, 2016, and as a poster at the American Society of Hematology (ASH) Annual Meeting in San Diego, California, on December 3-6, 2016. Study concept and design were contributed by Latremouille-Viau and Guerin, along with the other authors. Gagnon-Sanschagrin and Dea took the lead in data collection, assisted by the other authors, and data interpretation was performed by Cohen and Joseph, along with the other authors. The manuscript was written by Latremouille-Viau, along with the other authors, and revised by Joseph, along with the other authors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.298
Teacher spread0.283 · 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 teacher head, not a consensus.

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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Citations39
Published2017
Admission routes1
Has abstractyes

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