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Record W4244305556 · doi:10.1182/blood.v128.22.692.692

Are We Choosing Wisely in Lymphoma? Excessive Use of Surveillance CT Imaging in Patients with Diffuse Large B-Cell Lymphoma (DLBCL) in Long-Term Remission

2016· article· en· W4244305556 on OpenAlexaffabout
Matthew C. Cheung, Nicole Mittmann, Craig C. Earle, Farah Rahman, Ning Liu, Simron Singh

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsHealth Sciences CentreUniversity of TorontoInstitute for Clinical Evaluative SciencesCancer Care OntarioSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineDiffuse large B-cell lymphomaPopulationCohortAsymptomaticDiagnosis codeIncidence (geometry)LymphomaCumulative incidencePediatricsInternal medicine

Abstract

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Abstract Introduction The use of surveillance CT imaging in patients with DLBCL in remission is neither effective to detect recurrence nor cost-effective. The ASH Choosing Wisely (CW) campaign, in particular, emphasizes the lack of benefit in imaging beyond 2-years of completion of therapy. We sought to describe the practice of surveillance imaging and predictors of this practice. Methods We used population-based health system administrative databases from Ontario, Canada. We studied a cohort of all adult patients ≥18 with diffuse large B-cell lymphoma who received R-CHOP therapy for curative intent between January 1, 2004 to June 30, 2011. Based on the CW campaign, we defined an index date of 2-years after the last dose of R-CHOP as the time-frame beyond which surveillance CT imaging would be inappropriate. The cumulative incidence of receiving CT scans within 3 years after the index date (i.e. from 2- to 5-years beyond the end of treatment) represented the primary outcome of interest (established within the Ontario Health Insurance Program database). To ensure that only surveillance scans in asymptomatic patients were captured, patients were censored 6 months prior to development of recurrent disease or a new cancer diagnosis, further chemotherapy/radiation, or if they died (censored at time of death). Predictor characteristics included baseline comorbidities (John Hopkins weighted Aggregated Diagnosis Groups (ADG) comorbidity score) and income quintile (linkage of the patient postal code to Statistics Canada Census data on average household income by postal code). Results The cohort consisted of 2,838 patients treated with R-CHOP during the study period. Median age at time of first R-CHOP dosing was 63 years (IQR 52-72) and the median number of cycles received was 6 (IQR 6-8). The cumulative incidence of receiving CT imaging from the index date (2-years from end of treatment) to 3-years beyond the end of treatment was 40.1% (95% CI 38.3%-41.9%). The cumulative incidence of imaging from the index date to 5-years beyond the end of treatment was 55.6 % (95% CI 53.7%-57.5%). During the follow-up period, patients ≥65 were more likely to receive imaging than those aged <65 (58.2% vs. 53.0%; p<0.01) as were those with more comorbidities (65.4% in ADG group ≥7 vs. 44.3% in ADG group 0-2; p<0.01). Income level (p=0.06) and rurality (p=0.52) did not predict for increased imaging. The cumulative incidence appeared to decrease over the study follow-up period (from 65.5% in 2006 to 47.4% in 2013; p<0.01). For the entire population, the number of CT scans completed was 0.45 per patient-year of follow-up. Conclusion During a time-frame in which surveillance imaging is deemed unnecessary by the CW campaign (2 to 5 years beyond therapy), the practice in a large population remains excessive (>50% cumulative incidence in scanning). Patients who are older and with more comorbidities are more likely to receive scans, and may represent target populations to ensure greater compliance with the CW campaign. This study represents a real-world baseline from which future efforts to reduce surveillance imaging can be benchmarked. Disclosures No relevant conflicts of interest to declare.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.233
Teacher spread0.222 · 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.

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
Published2016
Admission routes2
Has abstractyes

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