Association between rheumatoid arthritis, frailty status, mortality and anti-cancer therapy: a retrospective SEER-Medicare analysis in patients with non-metastatic renal cell carcinoma

Introduction

Kidney cancers account for 4.1% of new cases of cancer and 2.4% of cancer-related deaths in the United States (1). Renal cell carcinoma (RCC), particularly clear cell renal cell carcinoma (ccRCC) accounts for most primary kidney neoplasms. The median age at diagnosis is approximately 65 years, with men having nearly double the risk of this diagnosis (1,2). Other known risk factors include advanced age, hypertension, tobacco use, and obesity, among others. Pathologic stage is the most robust predictor of outcomes, but patient-related prognostic factors such as performance status and comorbidities including frailty are increasingly recognized. Among various comorbidities, the role of autoimmune disease, such as rheumatoid arthritis (RA) is poorly explored. Recent data suggest an increased risk of RCC in these patients, similar to what is observed in a variety of other malignancies (3-6). Prognostically however, the role of RA on mortality in this population is not well-understood. From the oncology perspective, immune checkpoint inhibitors (ICIs) have transformed RCC treatment but present unique challenges in patients with pre-existing autoimmunity given the noteworthy potential for immune-related adverse events (irAEs). Up to half of patients with RA exposed to such therapies experience a flare in RA-related symptoms (7,8). As such, the intersection of RCC and RA represents a complex clinical scenario requiring thoughtful consideration of multiple interacting variables. We are focusing on RCC considering its incidence, responsiveness to ICI-based therapy and particular immunological features.

Frailty is a crucial and often overlooked third dimension in the cancer-RA relationship. It is described as a state of vulnerability to physiologic stressors resulting from the decline in multiple biological systems (9). Both RA and RCC disproportionately affect more senior adults, who are independently at risk for frailty. Nearly half of more senior patients with cancer are categorized as frail (10). In oncology, frailty is strongly associated with poor treatment response and outcomes, including disability and treatment-related complications, which frequently leads to difficult treatment decisions (11-16). Frailty is a clinically important consideration in patients with RA, particularly given that approximately one-third of this population are frail, underscoring the need to incorporate frailty assessment into routine management decisions (17). Importantly, RA and frailty share a common inflammatory substrate that likely amplifies their combined impact on outcomes. RA is characterized by persistent synovial inflammation driven by pro-inflammatory cytokines—including tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β)—that exert systemic effects well beyond the joints, including acceleration of cardiovascular disease, bone loss, and muscle catabolism. The frailty phenotype is increasingly understood through an inflammatory lens: elevated circulating levels of IL-6, C-reactive protein, and TNF-α have been consistently identified in frail older adults, suggesting that frailty itself may represent a state of chronic, low-grade systemic inflammation—or ‘inflammaging’. In patients with RA, this baseline inflammatory burden is further compounded by disease-related immune dysregulation, creating a bidirectional relationship in which chronic inflammation promotes frailty, and frailty in turn may worsen inflammatory dysregulation. This convergence on shared pathways of cytokine excess, immune senescence, and sarcopenia provides a compelling biological rationale for examining RA and frailty jointly, and for expecting that their co-occurrence may produce outcomes that are not simply additive. Patients with RA are consequently at an increased risk of frailty-associated outcomes such as osteoporotic fractures, infections, medication side effects, decreased physical functioning, treatment-associated adverse events, hospital admissions and readmissions, and mortality, making patients with both RA and cancer particularly high-risk populations (18,19).

While frailty has been explored separately in patients with RA and in patients with RCC, it is understudied in the vulnerable population of patients suffering from both conditions (11,20-22). Furthermore, given that uncontrolled or longstanding RA can itself contribute significantly to frailty, we sought to better understand the interaction between RA, RCC, and frailty—relationships that remain poorly characterized and leave significant gaps in knowledge regarding how best to manage this complex patient population. We hypothesized that the presence of RA would be associated with an increased risk of all-cause and cancer-specific mortality, as well as a decrease in immunotherapy and surgical management for patients with non-metastatic ccRCC. We also hypothesized that the contribution of frailty, by various adverse physiological mechanisms, would intensify these associations. We present this article in accordance with the STROBE reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0074/rc).

Methods

Study population and exposures of interest

This observational, retrospective cohort utilized the linked Surveillance Epidemiology and End Results and Medicare (SEER-Medicare) database to bring together data from the National Cancer Institute’s (NCI) SEER registry with claims-related data from Medicare (23). Using ICD-9 and ICD-10 codes, we included patients over the age of 65 years with non-metastatic ccRCC, the most common subtype of RCC, diagnosed from 2004–2017. Patients with Medicare part A and B coverage were eligible. We excluded Medicare Advantage beneficiaries who enrolled at any point prior to or after cancer diagnosis to ensure complete claim availability. Patients with metastatic ccRCC at diagnosis were excluded from this study, as the inherently poor prognosis associated with metastatic disease was felt to diminish the distinguishable prognostic contribution of frailty and RA on mortality outcomes. The index date for all survival analyses was the SEER-recorded date of ccRCC diagnosis. Patients were followed from the index date until death, loss of follow-up, or the administrative end of follow up (December 31, 2018), corresponding to the availability of SEER vital status data. Loss to follow-up was defined as disenrollment from continuous Medicare parts A and B coverage before December 31, 2018, in the absence of a recorded death. Individuals remaining alive at the end of available follow-up were administratively censored on December 31, 2018. All-cause mortality and cancer-specific mortality were ascertained using SEER vital status and cause-specific death classification variables, rather than Medicare claims. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was exempted from the requirement of Institutional Review Board (IRB) approval by the Ethics Committee of the University of Washington (No. STUDY00014227), and individual consent for this retrospective analysis was waived.

To identify pre-existing RA, we required patients to have at least two separate claims carrying RA diagnostic codes (ICD-9 or ICD-10) within the two years prior to their cancer diagnosis, with those claims separated by a minimum of 30 days and a maximum of 365 days. This algorithm has been shown to have moderate to high positive predictive values between 60–73% in identifying patients with RA (24).

Frailty was defined using a validated claims-based frailty index using claims in the 12 months prior to ccRCC diagnosis (25). This index, validated for use in Medicare data, utilizes a variety of ICD codes, current procedural terminology (CPT) variables, and healthcare common procedure coding system (HCPS) variables that relate to medical conditions, surgical history, functional status, and the use of relevant durable medical equipment, such as hospital beds and wheelchairs. Individuals with a score ≥0.25 were classified as frail.

Covariates and cancer treatments

Demographic information including age, sex, marital status and race was captured from the cancer registry. The YOST index, a score-based census tract-level attributes American Community Survey (ACS) 5-year estimates, was used as a composite socioeconomic status (SES) measure (26,27). The first YOST quintile (the group with the lowest SES) is the 20th percentile or less, and the fifth quintile (the group with the highest SES) corresponds to the 80th percentile or higher. American Joint Committee on Cancer (AJCC) summary cancer stage was measured at the time of diagnosis in the registry. If a patient had bilateral RCC, the more aggressive stage was used to classify the patient. Claims in the prior 12 months to diagnosis were used to determine the Comorbidity Index independent from the contribution of RA (28). To avoid collinearity between our primary exposure (RA) and the comorbidity adjustment variable, the NCI Comorbidity Index was calculated with the rheumatologic disease domain excluded, such that the score reflects non-RA comorbidity burden. We additionally examined correlations between RA status, frailty, and the comorbidity index and found no evidence of high collinearity that could distort regression estimates. Procedure ICD-9/10 and CPT codes in claims following cancer diagnosis were used to determine both surgical and immunotherapy management (Table S1). The identified surgical procedures included renal tumor ablation, radical nephrectomy, and partial nephrectomy. Immunotherapy exposure, defined as receipt of any systemic immunotherapy during follow-up, was also included as an adjustment variable to account for treatment differences between groups. Immunotherapies identified were pembrolizumab, nivolumab, avelumab, ipilimumab, atezolizumab, aldesleukin, and interferon alfa (2a and 2b).

Statistical analysis

Chi-square and t-tests were used to describe the baseline characteristics of individuals stratified by RA status. Cox proportional hazards regression models evaluated the association between RA and mortality (all-cause and cancer-specific). All adjusted models included age (continuous), sex, race, marital status, SES (Yost index), AJCC stage, the modified NCI comorbidity index (excluding the rheumatologic disease component), receipt of surgery, and receipt of immunotherapy as covariates. The same covariate set was applied to both the primary models and the four-level joint-effect models evaluating additive interaction. For cancer-specific mortality, competing-risk analyses were performed using Fine and Gray subdistribution hazard models, with death from causes other than kidney cancer treated as the competing event, and subdistribution hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated. Within these models, we assessed the interaction between RA status and frailty with mortality outcomes and stratified results presented if evidence of interaction existed at P<0.05. In addition to assessing multiplicative interaction through inclusion of an RA × frailty interaction term in the Cox proportional hazards models, we evaluated additive interaction for all-cause mortality. Patients were classified into four mutually exclusive exposure groups (no RA/non-frail, RA only, frailty only, and RA plus frailty), with the no RA/non-frail group serving as the reference. Measures of additive interaction, including the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (S), were calculated, and 95% CIs were estimated using bootstrap resampling.

All analyses were performed with SAS, version 9.4 [Copyright (c) 2016 by SAS Institute Inc., Cary, NC, USA].

Results

We identified 31,989 patients with non-metastatic ccRCC, of whom 802 (2.5%) had pre-existing RA. Approximately 60% of patients were male and 80% were non-Hispanic White. Most patients had stage I–II disease (80.7%). There was a higher prevalence of female sex, black ethnicity, unmarried status, lower SES, and lower AJCC stage in patients with RA. Our population included 3,918 (12.3%) patients categorized as frail. Those with RA were more frequently frail (20.5%) compared to those without RA (12.0%, P<0.0001). Further demographics details are listed in Table 1.

All-cause mortality

The median follow-up time was 65 [interquartile range (IQR): 35–107] months. The 5-year survival was 89% among patients with and without RA. Regarding all-cause mortality, among non-frail patients, 7,875 (28.7%) of 27,433 without RA and 214 (33.5%) of 638 with RA died during the study period. Among frail patients, 1,803 (48.0%) of 3,754 without RA and 81 (49.4%) of 164 with RA died during the study period. In the adjusted multivariate Cox Proportionate Hazards model examining all-cause mortality risk, a statistically significant interaction was observed between frailty and RA (P=0.01), thus only stratified mortality estimates are presented. In frail patients, RA was not an independent factor associated with all-cause mortality (aHR 0.94, 95% CI: 0.79–1.14; P=0.55). In non-frail patients, however, the presence of RA was associated with a significantly higher risk of all-cause mortality (aHR 1.15, 95% CI: 1.03–1.29; P=0.02). See Table 2 for further details.

Cancer specific mortality

Cancer-specific mortality was similar between groups when RA status was considered in isolation (16.8% in patients with RA vs. 16.4% in patients without RA). Regarding cancer-specific mortality, among non-frail patients, 4,250 (15.5%) of 27,433 without RA and 98 (15.4%) of 638 with RA died of renal cancer during the study period. Among frail patients, 871 (23.2%) of 3,754 without RA and 37 (22.6%) of 164 with RA died of renal cancer during the study period. We did not observe significant interaction between RA and frailty for cancer-specific mortality. The presence of frailty was independently associated with a higher risk of cancer-specific mortality (HR 1.37, 95% CI: 1.26–1.50; P≤0.001) while the presence of RA was not an independent risk factor (HR 1.07, 95% CI: 0.90–1.28; P=0.38). See Table 3 for further details.

Additive interaction analyses were consistent with the multiplicative models. Compared with patients who were neither frail nor had RA, adjusted hazard ratios for all-cause mortality were 1.15 (95% CI: 1.03–1.29) for RA only, 1.78 (95% CI: 1.69–1.86) for frailty only, and 1.66 (95% CI: 1.65–1.99) for both RA and frailty. Measures of additive interaction demonstrated no evidence that the combined effect of RA and frailty exceeded the sum of their independent effects (RERI =−0.275, 95% CI: −0.54 to 0.02; AP =−0.166, 95% CI: −0.29 to 0.01; S =0.704, 95% CI: 0.44–1.02). In competing risks analyses, the interaction between RA and frailty was not statistically significant (P=0.86). Likewise, the four-group additive model did not demonstrate evidence of excess joint effects on cancer-specific mortality.

Treatment receipt

Surgical management was performed in 25,953 patients (81.1%) within our population. Undergoing surgery was associated with lower all-cause and cancer-specific mortality (Tables 2,3). Rates of undergoing surgery were relatively similar between patients with and without RA (81.8% vs. 81.1% respectively). Immunotherapy was administered to 2,150 patients (6.7%). The presence of RA was not significantly associated with a change in receipt of immunotherapy [odds ratio (OR) 0.81, 95% CI: 0.59–1.12] vs. other variables, such as advanced-stage cancer (OR 2.38, 95% CI: 2.16–2.61) and prior cancer-related surgery (OR 1.20, 95% CI: 1.14–1.49) (Figure 1).

Figure 1 Odds of receiving immunotherapy prescription by category. N, node; T, tumor.

Among frail patients, immunotherapy was associated with lower all-cause mortality, whereas for non-frail patients it was associated with higher all-cause mortality (Table 2). In all patients, receipt of immunotherapy was associated with higher cancer-specific mortality.

Discussion

Our results highlight the significant impact of frailty on mortality among patients with localized ccRCC, both with and without RA. Frail patients faced substantially higher risks of all-cause and cancer-specific mortality compared to their non-frail counterparts, and this association remained consistent regardless of RA status. Among non-frail patients, however, RA emerged as an independent risk factor for all-cause mortality. This is consistent with prior cohort studies and registry analyses, which have similarly demonstrated an association between RA and increased all-cause mortality, attributed to cardiovascular disease, respiratory conditions such as interstitial lung disease and pneumonia, infectious complications, and malignancy (29-32). Our findings underscore that frailty not only may independently drives mortality but appears to interact with and overshadow RA as a contributing risk factor for all-cause mortality. In frail patients, the burden of physiologic vulnerability and multimorbidity may be so pronounced that it effectively eclipses the incremental mortality contribution of RA. This may be mediated through multiple proposed mechanisms such as sarcopenia, immune senescence, infection risk, cardiovascular burden, declining organ function, and others. While less likely, we also consider medication-related toxicity, as non-frail patients are more likely to be on active, aggressive RA therapy, including disease-modifying antirheumatic drugs (DMARDs) and biologics, which paradoxically carry their own mortality-relevant risks such as infection susceptibility, hepatotoxicity, cardiovascular morbidity, and potential malignancy association.

For cancer-specific mortality, it appeared that RA interestingly was not associated with a higher risk, regardless of frailty status. This is in opposition to what has been suggested in other cancers, such as melanoma, breast cancer, lymphoma, lung cancer, and bladder cancer (33-35). While the reason for this finding is unclear, this discrepancy may potentially relate to different population and study characteristics, power calculations, and/or the interactions of pathophysiologic mechanisms between unique cancer types and RA, which introduce an interesting avenue of future study. For instance, it has been shown that a positive rheumatoid factor, rather than a positive anti-citrullinated protein antibody, is associated with increased cancer-specific death by mechanisms yet to be elucidated (36-38).

An important distinction must be made regarding the relationship between the receipt of immunotherapy and mortality. Our results suggest that receipt of immunotherapy was associated with increased cancer-specific mortality. Presumably, this is related to the selection of patient candidates for immunotherapy, who tend to have higher cancer stage rather than small, localized masses. Therefore, this finding may be reflective of their tendency to have more aggressive disease, e.g., selection bias.

The presence of RA may possibly influence surgical and systemic treatment outcomes in oncology. Prior data have shown that patients with RA may have an increased risk of irAEs, often in the form of mild-moderate RA flares that are treatable with glucocorticoids and/or DMARDs (39,40). Severe irAEs, however, do not appear to be increased compared to individuals without RA (41,42). Patients with RA may also face increased surgical risk with a higher risk of postoperative infection and delayed wound healing, especially if on biologic DMARDs or glucocorticoids (43,44). Such factors require careful consideration and often require adjustments to the rheumatologist’s treatment plan. Frequently this may include changing or discontinuing DMARDs to optimize oncologic outcomes, when possible. Our results show that when it comes to immunotherapy administration and surgical management, the presence of RA was not associated with a significant difference in receipt of those treatments. Our available data does not capture how immunotherapy treatment decisions were made. One possibility for this outcome could simply be related to the low proportion of patients with RA in our study, but we are hopeful that this could also reflect interdisciplinary collaboration between rheumatology and oncology aiming at optimizing cancer outcomes.

Limitations

Our study has several important limitations. Its retrospective design carries risks of confounding and lacks the rigor of randomization. As a US-based study, findings may have limited generalizability to healthcare systems outside the United States and inappropriate translation of results.

While immunotherapy was coded as a fixed covariate, which may introduce immortal time bias, we believe this is unlikely to have meaningfully influenced our primary findings. Our cohort was limited to patients diagnosed with localized ccRCC between 2004 and 2017. Although nivolumab received its first approval for previously treated metastatic RCC in 2015, ICI use during this period remained largely restricted to clinical trials in the setting of advanced, unresectable, or adjuvant disease, and was not yet standard treatment for the localized disease population captured in our study window. As such, our analysis of immunotherapy administration may not be representative of current clinical practice.

Patient identification based solely on ICD diagnostic codes introduces additional limitations. This approach may introduce selection bias, as DMARD prescription records, which could more reliably identify RA and stratify disease severity, were not utilized (24). Additionally, serologic status and other measures of disease severity were not accessible using this identification method and could act as unmeasured confounders. In this light, approximately 2.5% of our sample of senior adults with ccRCC carried the diagnosis of RA. While this is somewhat higher than the reported prevalence in the United States, it is similar to reported figures for individuals ≥65 years (45,46). By nature of our study design, we may have also captured more patients with RA given their elevated risk for urological cancers, such as RCC (3).

Additionally, exclusion of Medicare Advantage beneficiaries was necessary to ensure complete claims data, but this limits the representativeness of our sample within the broader Medicare population, though we do not anticipate this introduced significant bias.

Our focus on non-metastatic RCC was intentional, aimed at better isolating the contributions of frailty and RA to mortality outcomes. As a result, we did not evaluate the population most likely to be indicated for receipt of systemic immunotherapy. Additionally, our method of determining the cause of death was derived from coding in SEER, which may be subject to some misclassification, potentially affecting cancer-specific mortality estimates. Furthermore, given the relatively recent regulatory approval of adjuvant pembrolizumab for resected RCC, the current analysis may not reflect evolving contemporary practice. Finally, while there are several confounding factors between immunotherapy management and RA disease activity, we excluded non-immunotherapy systemic therapy options for ccRCC. which may have interacted with RA status and treatment.

While this analysis was limited to ccRCC, a similar relationship between RA, frailty, and mortality may possibly exist across other cancer types and warrants further investigation.

Conclusions

In patients with non-metastatic ccRCC, frailty significantly modified the relationship between RA and all-cause mortality. Among non-frail patients, RA was an independent risk factor for all-cause mortality. However, this association appears to possibly be overshadowed in patients with frailty. Cancer-specific mortality was not affected by the presence of RA in this population regardless of frailty status. Frailty is significantly associated with higher all-cause mortality and cancer-specific mortality, consistent with prior investigations. Regarding oncology treatment planning, the presence of RA did not appear to be associated with the likelihood of undergoing cancer-related surgery or receipt of immunotherapy for ccRCC. These findings underscore the importance of assessing and further understanding the impact of frailty in the vulnerable population with pre-existing RA and cancer. While this study specifically focuses on RCC, it represents an example of how to study the complex interactions between RA and frailty in the context of other cancers. Furthermore, it is becoming increasingly clear that modern cancer care, specifically involving immunotherapy in frail patients, is immunologically and physiologically complex. Inter-disciplinary care and communication between rheumatologists, urologic oncologists, and medical oncologists is essential to promptly identifying and managing disabling rheumatic complications of therapy, while minimizing unnecessary treatment interruptions and preserving optimal cancer care outcomes.

Acknowledgments

The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s (CDC) National Program of Cancer Registries, under cooperative agreement 1NU58DP007156; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the authors and do not necessarily reflect the opinions of the State of California, Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0074/rc

Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0074/dss

Peer Review File: Available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0074/prf

Funding: This work was supported by the NIAMS (award No. K23AR079588); NIA (award No. R03AG082857); the NIHR Manchester Biomedical Research Centre (No. NIHR203308 to K.H. and S.S.Z.); a University of Manchester Dean’s Prize and work in centres supported by Versus Arthritis (grant Nos. 21173, 21754 and 21755 to S.S.Z.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0074/coif). N.S. serves as an unpaid editorial board member of Annals of Translational Medicine from February 2026 to December 2027. K.H. has received honoraria from AbbVie and grants from Pfizer and BMS for research areas unrelated to this work. J.S. has received research support from Boehringer Ingelheim, Bristol Myers Squibb, Johnson & Johnson, and Sonoma Biotherapeutics unrelated to this work. He has performed consultancy for AbbVie, Amgen, Anaptys, AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Fresenius Kabi, Gilead, GSK, Inova Diagnostics, Invivyd, Johnson & Johnson, Merck, MustangBio, Novartis, Optum, Pfizer, ReCor, Sana, Sobi, and UCB unrelated to this work. M.S.A. has received funding from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01 AR078484 and a consulting fee from Syneos Health. P.G. reports the consulting fee from MSD, Bristol Myers Squibb, AstraZeneca, EMD Serono, Pfizer, Janssen, Roche, Astellas Pharma, Gilead Sciences, Strata Oncology, AbbVie, Bicycle Therapeutics, Replimune, Daiichi Sankyo, Foundation Medicine, Eli Lilly, Urogen, Tyra Biosciences, Natera; and research funding from MSD, EMD Serono, Gilead Sciences, Acrivon Therapeutics, ALX Oncology, Genentech (paid to institution). S.P. reports consulting fees from Janssen/Johnson & Johnson, CG Oncology, Merck, ImmunityBio; Research Funding: Bladder Cancer Advocacy Network; National Institute on Aging; Janssen (Global PI SunRise-4 Trial); Steba Biotech (Site PI ENLIGHTED Trial). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was exempted from the requirement of IRB approval by the Ethics Committee of the University of Washington (No. STUDY00014227), and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Cancer Stat Facts: Kidney and Renal Pelvis Cancer. NIH Surveillance, Epidemiology, and End Results Program. 2022. Available online: https://seer.cancer.gov/statfacts/html/kidrp.html
2. Scelo G, Li P, Chanudet E, et al. Variability of Sex Disparities in Cancer Incidence over 30 Years: The Striking Case of Kidney Cancer. Eur Urol Focus 2018;4:586-90. [Crossref] [PubMed]
3. Lee D, Jeon KH, Jung J, et al. Urological cancer risk in patients with rheumatoid arthritis compared to matched controls: a nationwide cohort study. Rheumatology (Oxford) 2025;64:3458-67. [Crossref] [PubMed]
4. Beydon M, Pinto S, De Rycke Y, et al. Risk of cancer for patients with rheumatoid arthritis versus general population: a national claims database cohort study. Lancet Reg Health Eur 2023;35:100768. [Crossref] [PubMed]
5. Smitten AL, Simon TA, Hochberg MC, et al. A meta-analysis of the incidence of malignancy in adult patients with rheumatoid arthritis. Arthritis Res Ther 2008;10:R45. [Crossref] [PubMed]
6. Simon TA, Thompson A, Gandhi KK, et al. Incidence of malignancy in adult patients with rheumatoid arthritis: a meta-analysis. Arthritis Res Ther 2015;17:212. [Crossref] [PubMed]
7. Bernabela L, Bermas B. Immune Checkpoint Inhibitor Associated Rheumatoid Arthritis. Curr Rheumatol Rep 2024;27:3. [Crossref] [PubMed]
8. McCarter KR, Arabelovic S, Wang X, et al. Immunomodulator use, risk factors and management of flares, and mortality for patients with pre-existing rheumatoid arthritis after immune checkpoint inhibitors for cancer. Semin Arthritis Rheum 2024;64:152335. [Crossref] [PubMed]
9. Clegg A, Young J, Iliffe S, et al. Frailty in elderly people. Lancet 2013;381:752-62. [Crossref] [PubMed]
10. Handforth C, Clegg A, Young C, et al. The prevalence and outcomes of frailty in older cancer patients: a systematic review. Ann Oncol 2015;26:1091-101. [Crossref] [PubMed]
11. Walach MT, Wunderle MF, Haertel N, et al. Frailty predicts outcome of partial nephrectomy and guides treatment decision towards active surveillance and tumor ablation. World J Urol 2021;39:2843-51. [Crossref] [PubMed]
12. Campi R, Berni A, Amparore D, et al. Impact of frailty on perioperative and oncologic outcomes in patients undergoing surgery or ablation for renal cancer: a systematic review. Minerva Urol Nephrol 2022;74:146-60. [Crossref] [PubMed]
13. Miller C, Raza SJ, Davaro F, et al. Trends in the treatment of clinical T1 renal cell carcinoma for octogenarians: Analysis of the National Cancer Database. J Geriatr Oncol 2019;10:285-91. [Crossref] [PubMed]
14. Pierantoni F, Basso U, Maruzzo M, et al. Comprehensive geriatric assessment is an independent prognostic factor in older patients with metastatic renal cell cancer treated with first-line Sunitinib or Pazopanib: a single center experience. J Geriatr Oncol 2021;12:290-7. [Crossref] [PubMed]
15. Gontero P, Mari A, Marra G, et al. Is partial nephrectomy safe and effective in the setting of frail comorbid patients affected by renal cell carcinoma? Insights from the RECORD 2 multicentre prospective study. Urol Oncol 2021;39:78.e17-26.
16. Iqbal M. Renal Cell Carcinoma: A Complex Therapeutic Challenge in the Elderly. Cureus 2022;14:e26346. [Crossref] [PubMed]
17. Gao RC, Wu ZG, Wu ZZ, et al. Frailty in rheumatoid arthritis: A systematic review and meta-analysis. Joint Bone Spine 2022;89:105343. [Crossref] [PubMed]
18. Singh N, Gold LS, Lee J, et al. Frailty and Risk of Serious Infections in Patients With Rheumatoid Arthritis Treated With Biologic or Targeted-Synthetic Disease-Modifying Antirheumatic Drugs. Arthritis Care Res (Hoboken) 2024;76:627-35. [Crossref] [PubMed]
19. Tahir MW, Rosli Y, Leung C, et al. Frailty and its association with readmissions in patients with rheumatoid arthritis: A national readmissions database study. Clin Rheumatol 2024;43:3639-46. [Crossref] [PubMed]
20. Germonpré S, Mulier S, Falzon L, et al. Prevalence of frailty and pre-frailty in patients with rheumatoid arthritis: a systematic literature review and meta-analysis. Clin Exp Rheumatol 2023;41:1443-50. [Crossref] [PubMed]
21. Tang SC, Jin L, Hu YQ, et al. The distribution of the frailty index among different arthritis patients and the association between the frailty index and mortality in rheumatoid arthritis patients among U.S. adults (results from NHANES 1999-2018). Aging Clin Exp Res 2025;37:186.
22. Rosiello G, Larcher A, Fallara G, et al. A comprehensive assessment of frailty status on surgical, functional and oncologic outcomes in patients treated with partial nephrectomy-A large, retrospective, single-center study. Urol Oncol 2023;41:149.e17-25.
23. SEER-Medicare: Brief Description of the SEER-Medicare Database. National Cancer Institute Division of Cancer Control & Population Sciences. 2019. Available online: https://healthcaredelivery.cancer.gov/seermedicare/overview/
24. Kim SY, Servi A, Polinski JM, et al. Validation of rheumatoid arthritis diagnoses in health care utilization data. Arthritis Res Ther 2011;13:R32. [Crossref] [PubMed]
25. Kim DH, Schneeweiss S, Glynn RJ, et al. Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index. J Gerontol A Biol Sci Med Sci 2018;73:980-7. [Crossref] [PubMed]
26. Yu M, Tatalovich Z, Gibson JT, et al. Using a composite index of socioeconomic status to investigate health disparities while protecting the confidentiality of cancer registry data. Cancer Causes Control 2014;25:81-92. [Crossref] [PubMed]
27. Yost K, Perkins C, Cohen R, et al. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control 2001;12:703-11. [Crossref] [PubMed]
28. Klabunde CN, Legler JM, Warren JL, et al. A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients. Ann Epidemiol 2007;17:584-90. [Crossref] [PubMed]
29. Ometto F, Fedeli U, Schievano E, et al. Cause-specific mortality in a large population-based cohort of patients with rheumatoid arthritis in Italy. Clin Exp Rheumatol 2018;36:636-42.
30. Misra DP. Clinical manifestations of rheumatoid arthritis, including comorbidities, complications, and long-term follow-up. Best Pract Res Clin Rheumatol 2025;39:102020. [Crossref] [PubMed]
31. Widdifield J, Paterson JM, Huang A, et al. Causes of Death in Rheumatoid Arthritis: How Do They Compare to the General Population? Arthritis Care Res (Hoboken) 2018;70:1748-55. [Crossref] [PubMed]
32. Ji J, Liu X, Sundquist K, et al. Survival of cancer in patients with rheumatoid arthritis: a follow-up study in Sweden of patients hospitalized with rheumatoid arthritis 1 year before diagnosis of cancer. Rheumatology (Oxford) 2011;50:1513-8. [Crossref] [PubMed]
33. Wang JH, Derkach A, Pfeiffer RM, et al. Immune-related conditions and cancer-specific mortality among older adults with cancer in the United States. Int J Cancer 2022;151:1216-27. [Crossref] [PubMed]
34. Ugolini A, Nuti M. Rheumatoid Factor: A Novel Determiner in Cancer History. Cancers (Basel) 2021;13:591. [Crossref] [PubMed]
35. Swaminathan M, Holt SK, Gore JL, et al. Association Between Rheumatoid Arthritis, Frailty Status, and Mortality in Older Adults with Bladder Cancer. Clin Genitourin Cancer 2025;23:102369. [Crossref] [PubMed]
36. Ahn JK, Hwang J, Chang Y, et al. Rheumatoid factor positivity increases all-cause and cancer mortality: a cohort study. Rheumatol Int 2017;37:1135-43. [Crossref] [PubMed]
37. Ajeganova S, Humphreys JH, Verheul MK, et al. Anticitrullinated protein antibodies and rheumatoid factor are associated with increased mortality but with different causes of death in patients with rheumatoid arthritis: a longitudinal study in three European cohorts. Ann Rheum Dis 2016;75:1924-32. [Crossref] [PubMed]
38. Efuni E, Cytryn S, Boland P, et al. Risk of Toxicity After Initiating Immune Checkpoint Inhibitor Treatment in Patients With Rheumatoid Arthritis. J Clin Rheumatol 2021;27:267-71. [Crossref] [PubMed]
39. Rajak P. Immune checkpoint inhibitors: From friend to foe. Toxicol Rep 2025;14:102033. [Crossref] [PubMed]
40. McCarter KR, Wolfgang T, Arabelovic S, et al. Mortality and immune-related adverse events after immune checkpoint inhibitor initiation for cancer among patients with pre-existing rheumatoid arthritis: a retrospective, comparative, cohort study. Lancet Rheumatol 2023;5:e274-83. [Crossref] [PubMed]
41. Isaacs DJ, Kathuria-Prakash N, Hilder R, et al. Risk of severe immune-related adverse events in cancer patients with pre-existing autoimmunity receiving immune checkpoint inhibitor therapy. Curr Cancer Rep 2024;5:168-80. [Crossref] [PubMed]
42. George MD, Baker JF. Perioperative management of immunosuppression in patients with rheumatoid arthritis. Curr Opin Rheumatol 2019;31:300-6. [Crossref] [PubMed]
43. Sigmund A, Russell LA. Optimizing Rheumatoid Arthritis Patients for Surgery. Curr Rheumatol Rep 2018;20:48. [Crossref] [PubMed]
44. Ito H, Murata K, Sobue Y, et al. Comprehensive risk analysis of postoperative complications in patients with rheumatoid arthritis for the 2020 update of the Japan College of Rheumatology clinical practice guidelines for the management of rheumatoid arthritis. Mod Rheumatol 2022;32:296-306. [Crossref] [PubMed]
45. Hunter TM, Boytsov NN, Zhang X, et al. Prevalence of rheumatoid arthritis in the United States adult population in healthcare claims databases, 2004-2014. Rheumatol Int 2017;37:1551-7. [Crossref] [PubMed]
46. Laiho K, Tuomilehto J, Tilvis R. Prevalence of rheumatoid arthritis and musculoskeletal diseases in the elderly population. Rheumatol Int 2001;20:85-7. [Crossref] [PubMed]