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Background People with chronic kidney disease (CKD) have increased incidence and mortality of most cancer types. We hypothesized that the odds of presenting with advanced cancer may vary according to differences in estimated glomerular filtration rate (eGFR), that this could contribute to increased all-cause mortality and that sex differences may exist. Methods Data were from Secure Anonymised Information Linkage Databank, including people with de novo cancer diagnosis (2011–17) and two kidney function tests within 2 years prior to diagnosis to determine baseline eGFR (mL/min/1.73 m2). Logistic regression models determined the odds of presenting with advanced cancer by baseline eGFR. Cox proportional hazards models tested associations between baseline eGFRCr and all-cause mortality. Results eGFR <30 was associated with higher odds of presenting with advanced cancer of prostate, breast and female genital organs, but not other cancer sites. Compared with eGFR >75–90, eGFR <30 was associated with greater hazards of all-cause mortality in both sexes, but the association was stronger in females [female: hazard ratio (HR) 1.71, 95% confidence interval (CI) 1.56–1.88; male versus female comparison: HR 0.88, 95% CI 0.78–0.99]. Conclusions Lower or higher eGFR was not associated with substantially higher odds of presenting with advanced cancer across most cancer sites, but was associated with reduced survival. A stronger association with all-cause mortality in females compared with males with eGFR <30 is concerning and warrants further scrutiny. [ABSTRACT FROM AUTHOR]

Introduction: In trials, subgroup analyses are used to examine whether treatment effects differ by important patient characteristics. However, which subgroups are most commonly reported has not been comprehensively described., Design and Settings: Using a set of trials identified from the US clinical trials register (ClinicalTrials.gov), we describe every reported subgroup for a range of conditions and drug classes., Methods: We obtained trial characteristics from ClinicalTrials.gov via the Aggregate Analysis of ClinicalTrials.gov database. We subsequently obtained all corresponding PubMed-indexed papers and screened these for subgroup reporting. Tables and text for reported subgroups were extracted and standardised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Via logistic and Poisson regression models we identified independent predictors of result reporting (any vs none) and subgroup reporting (any vs none and counts). We then summarised subgroup reporting by index condition and presented all subgroups for all trials via a web-based interactive heatmap (https://ihwph-hehta.shinyapps.io/subgroup&#95;reporting&#95;app/)., Results: Among 2235 eligible trials, 23% (524 trials) reported subgroups. Follow-up time (OR, 95%CI: 1.13, 1.04-1.24), enrolment (per 10-fold increment, 3.48, 2.25-5.47), trial starting year (1.07, 1.03-1.11) and specific index conditions (eg, hypercholesterolaemia, hypertension, taking asthma as the reference, OR ranged from 0.15 to 10.44), predicted reporting, sponsoring source and number of arms did not. Results were similar on modelling any result reporting (except number of arms, 1.42, 1.15-1.74) and the total number of subgroups. Age (51%), gender (45%), racial group (28%) were the most frequently reported subgroups. Characteristics related to the index condition (severity/duration/types etc) were frequently reported (eg, 69% of myocardial infarction trials reported on its severity/duration/types). However, reporting on comorbidity/frailty (five trials) and mental health (four trials) was rare., Conclusion: Other than age, sex, race ethnicity or geographic location and characteristics related to the index condition, information on variation in treatment effects is sparse., Prospero Registration Number: CRD42018048202., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)