MétaCan
Menu
← Back to cohort

Real-world practice patterns, treatment-related toxicity, and survival outcomes in older patients with metastatic renal cell carcinoma: Results from the Canadian Kidney Cancer information system (CKCis).

2024· article· en· W4391303262 on OpenAlexaffabout
Lauren Curry, Sunita Ghosh, Erica Arenovich, Simon Tanguay, Aly‐Khan A. Lalani, Daniel Yick Chin Heng, Bimal Bhindi, Naveen S. Basappa, Jeffrey Graham, Georg A. Bjarnason, Rodney H. Breau, Vincent Castonguay, Denis Soulières, Frédéric Pouliot, Dominick Bossé, Christian Kollmannsberger, Antonio Finelli, Nazanin Fallah‐Rad, Maryam Soleimani

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité LavalPrincess Margaret Cancer CentreUniversity of CalgaryMcMaster UniversitySunnybrook Health Science CentreUniversity of ManitobaJuravinski Cancer CentreUniversity of OttawaMcGill University Health CentreUniversity of AlbertaQueen Elizabeth II Health Sciences CentreCentre hospitalier universitaire de QuébecUniversity Health NetworkOttawa HospitalBC Cancer Agency
Fundersnot available
KeywordsMedicineRenal cell carcinomaToxicityKidney cancerOncologyCancerInternal medicineKidney

Abstract

fetched live from OpenAlex

366 Background: There is a paucity of data with respect to optimal management of metastatic renal cell carcinoma (mRCC) in older adults. Real world data may help close this knowledge gap and improve care for older patients with mRCC. Methods: The Canadian Kidney Cancer information system (CKCis) was utilized to identify patients with mRCC, categorizing them as either older (defined as age ≥75 years) or younger (age <75 years). We compared first line (1L) mRCC management strategies and treatment-related toxicities. Secondary outcomes were overall survival (OS) and time to treatment discontinuation (TTD). Chi-Square and Fisher’s Exact tests were used to compare groups, and survival outcomes were measured by Kaplan-Meier method. Cox’s proportional hazard ratio (HR) were reported by age adjusting for IMDC risk groups, histology, and Charlson Comorbidity Index (CCI) for OS and TTD. Results: 2576 patients were included (n=2203 <75 years old; n=373 ≥75 years old). Baseline demographics were comparable between groups, though older patients had more comorbidities (5+, 95% vs. 67%, p<0.0001) and more frequently had Karnofsky Performance Status <70% (18% vs. 13%, p=0.01). Older patients underwent metastasectomy less frequently (15% vs. 25%, p=0.0001) and were less likely to be enrolled in clinical trials (10% vs. 24%, p<0.0001). Older patients received 1L tyrosine kinase inhibitor (TKI) monotherapy more frequently (79% vs. 69%, p<0.0001) than immune checkpoint inhibitor (ICI)-based treatment, even when adjusted by year to account for changes in practice patterns in the post-ICI era (65% vs. 44%, p<0.0001). Amongst all patients, the TKI monotherapy most frequently prescribed was sunitinib, though older patients were more likely to receive pazopanib than younger patients ( p<0.0001). Amongst all patients who received 1L ICI-based treatment, there was no difference in the type of ICI regimen (i.e. doublet ICI versus ICI plus TKI) prescribed when compared by age ( p=0.61). Older patients did not experience more frequent treatment-related toxicities with ICI-based treatment. They did however experience more grade 3+ toxicity with TKI monotherapy. Older patients had shorter OS even when controlling for IMDC score, CCI and histology (HR 1.21, 95% CI 1.03-1.43, p=0.02). There was no difference in TTD by groups. Conclusions: Patients ≥75 years of age received TKI monotherapy more frequently than those <75 years of age, though when they received ICI-based regimens, they did not experience more treatment-related toxicities nor more dose modifications. Clinicians should individualize treatments for older patients not solely based on age, but after discussion of all available options in a patient-centered manner, considering comorbidities, disease burden, and patient preferences.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.328
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.058
GPT teacher head0.382
Teacher spread0.324 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicRenal cell carcinoma treatment→French-language works237,207→