MétaCan
Menu
← Back to cohort

The influence of sex on the impact of age and frailty on acute care use among older adults receiving immune checkpoint inhibitor (ICI) treatment: A population-based study.

2023· article· en· W4379344533 on OpenAlexaffabout
Elizabeth Faour, Rinku Sutradhar, Yosuf Kaliwal, Yue Niu, Ning Liu, Melanie Powis, Geoffrey Liu, Jeffrey Peppercorn, Monika K. Krzyzanowska, Shabbir M.H. Alibhai, Lawson Eng

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of TorontoUniversity Health NetworkCancer Care OntarioPrincess Margaret Cancer Centre
FundersConquer Cancer Foundation
KeywordsMedicineBody mass indexComorbidityRetrospective cohort studyPopulationEmergency departmentAcute careIpilimumabEmergency medicineAdverse effectInternal medicineCancerHealth careImmunotherapy

Abstract

fetched live from OpenAlex

12053 Background: ICIs are commonly used across solid tumors and although better tolerated than chemotherapy, patients may develop immune related adverse events (irAEs) requiring hospitalization. Older adults were poorly represented in trials evaluating ICIs. We previously demonstrated that among older adults receiving ICIs, increasing age was associated with reduced risk of irAE hospitalizations, while frailty was associated with increased acute care use (ASCO 2022). However, sex may impact irAE rates. Here, we evaluated sex-specific differences based on age and frailty, on acute care use and irAEs among older adults receiving ICIs. Methods: We performed a retrospective, population-based study of a cohort of patients with cancer, age ≥ 65, receiving ICIs between June 2012 and October 2018 in Ontario, Canada using administrative data. Databases were deterministically linked to obtain socio-demographic and clinical covariates, and acute care outcomes. Acute care use was defined as emergency department visits or hospitalizations from the start of ICIs to 120 days following last dose; irAE specific hospitalizations were identified using ICD-10 codes. Frailty was assessed using the McIsaac Frailty Index. Using death as the competing risk, multivariable competing risk analyses with Fine Gray sub-distribution hazards evaluated the effect of age and frailty on both acute care use and irAE hospitalizations, adjusted for body mass index (BMI), history of autoimmune condition, comorbidity score, rurality, and hospitalization within 60 days prior to starting ICI, stratified by sex. Results: 2737 patients were identified; 60% male. Median age 73 (IQR 69-78); 43% received Nivolumab, 41% Pembrolizumab and 13% Ipilimumab; 53% had lung cancer, 34% melanoma. 70% were robust (R), 26% pre-frail (PF) and 4% frail (F). 72% of patients had an acute care episode and 8% had an irAE hospitalization, which did not differ by sex (72%/8% male; 71%/8% female). Increasing frailty was associated with greater acute care use in males (PF vs R aHR 1.20 [95% CI 1.02-1.40] p = 0.03, F vs R aHR 1.42 [1.05-1.91] p = 0.02) and females (PF vs R aHR 1.24 [1.03-1.49] p = 0.02, F vs R aHR 1.55 [0.99-2.40] p = 0.05) but was not associated with irAE hospitalization in either sex. Using age as a continuous variable, increasing age was associated with reduced irAE hospitalizations in males (aHR 0.97 per year [0.94-0.99] p = 0.04), but not in females (p = 0.18); no significant associations were identified modelling age as a categorical variable. Conclusions: Among older adults receiving ICIs, increasing age was associated with reduced rates of irAE related hospitalization in males but not in females, while increasing frailty was associated with increased acute care use among both sexes. Sex should be taken into consideration when evaluating potential toxicity among older adults receiving ICIs.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.220
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.082
GPT teacher head0.436
Teacher spread0.355 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicFrailty in Older Adults→French-language works237,207→