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J. Fletcher1-3, N. Reid3, R.E. Hubbard1-3, R. Berry1, M. Weston1, E. Walpole1,2, R. Kimberley1, D.A. Thaker2,4,
R. Ladwa1,2


1. Princess Alexandra Hospital, 199 Ipswich Road, Woolloongabba, Queensland 4102, Australia; 2. Faculty of Medicine, The University of Queensland. 199 Ipswich Road, Woolloongabba, Queensland 4102, Australia; 3. Centre for Health Services Research, Faculty of Medicine, The University of Queensland, 199 Ipswich Road, Woolloongabba, QLD 4102, Australia; 4. Metro North Hospital and Health Service, Queensland, Australia.

Corresponding Author: Dr James Fletcher, Postal address: Division of Cancer Services, Princess Alexandra Hospital. 199 Ipswich Road, Woolloongabba, QLD 4102, Australia. Email address: james.fletcher@uq.edu.au, Contact number: +617 3176 5159

J Frailty Aging 2024;in press
Published online March 6, 2024, http://dx.doi.org/10.14283/jfa.2024.22



BACKGROUND: Frailty is an indicator of individual vulnerability and differentiates health status among people of the same chronological age.
OBJECTIVES: This study aimed to determine whether baseline frailty index (FI) was associated with systemic anticancer therapy treatment outcomes in older adults with solid cancers.
DESIGN: Retrospective cohort study.
SETTING: Major metropolitan outpatient oncology service.
PARTICIPANTS: Adults aged over 65 years with a solid malignancy who had been referred for consideration of systemic therapy, and had completed a baseline frailty assessment between January 2019 and July 2021.
MEASUREMENTS: Frailty had been prospectively assessed with a 58-item FI derived from a geriatric oncology nurse assessment prior to initial oncologist appointments. Primary outcome was treatment completion, and secondary outcomes included incidence of high-grade treatment-related toxicity or unplanned hospital admissions, and survival outcomes. Univariate and multivariable regression analyses were conducted to test the association between treatment outcomes and baseline FI. Co-variates included age, sex, performance status, treatment intent, and stage. Kaplan-Meier and cox proportional hazard analysis were conducted for survival analysis.
RESULTS: The median FI (IQR) was 0.24 (0.15-0.31) and 43% were frail (FI>0.25). FI was positively correlated with ECOG, however 28% of ECOG 0-1 were frail. In multivariable regression analyses, each 0.10 increase in FI was associated with an increased likelihood of not completing or not receiving treatment (OR 1.37, 95% CI 1.02-1.84; p=.04), treatment-related toxicity (OR 1.60, 95% CI 1.14-2.23; p<.01) and unplanned hospital admissions (OR 1.61; 95% CI 1.16-2.25; p<.01). Frail patients had increased mortality (adjusted HR 2.81, 95% CI 1.42-5.56; p<.01). Age did not predict treatment completion, toxicities, or survival.
CONCLUSION: Baseline FI is a granular measure that can help to identify frailer older patients who are more likely to require tailored therapy and support, and less frail older patients who are more likely to tolerate treatment.

Key words: Frailty, frailty index, frail elderly, geriatric oncology, geriatric assessment, cancer.

Abbreviations: FI: Frailty index; ECOG: Eastern Cooperative Oncology Group; PS: Performance status.



Approximately 60% of new cancer diagnoses in Australia in 2019 occurred in those over the age of 65 years (1). This population faces the potential challenges of comorbidity, polypharmacy, age-related physiological changes, geriatric syndromes, psychosocial vulnerability, and a paucity of evidence regarding treatment efficacy from clinical trials (2).
Frailty is a summative measure of health status and a marker of vulnerability, identifying individuals with reduced physiological reserve to compensate for stressor events (2). It can be conceptualised as an age-related accumulation of deficits and limited functional reserves across multiple domains of health (3). In the general population, frailty is associated with negative health outcomes, including hospitalisation, falls, delirium, institutionalisation, and mortality (2, 4). In a cancer population, frailty is associated with functional decline, treatment-related complications, disease progression and mortality (5, 6).
Comprehensive geriatric assessment (CGA) or geriatric assessment and management strategies in the older population can capture additional vulnerabilities which may provide an opportunity to refocus goals of care, address manageable geriatric syndromes, and it may influence treatment decisions (7-9). The American Society of Clinical Oncology (ASCO) and the International Society of Geriatric Oncology (SIOG) recommend the routine use of geriatric assessment or frailty screening tools in older adults to detect and manage frailty and other vulnerabilities, and to improve decision making and treatment outcomes (7, 10, 11).
The frailty index (FI) model employs a well-established method to derive an index as a proportion of deficits across multiple domains of health, including comorbidities, functional ability, physical ability, psychosocial factors, cognition and common geriatric syndromes (3, 12). An FI is calculated by counting the number of deficits present in an individual, divided by the total number of deficits assessed. It provides a granular quantification of frailty which is expressed as a continuous measure from zero to one, where one is the theoretical maximum of frailty (3, 13).
In an oncology setting, an FI can be derived from a multi-domain assessment (FI-CGA) and has been validated in a geriatric oncology population referred for consideration of chemotherapy (14). It has been associated with adverse treatment outcomes including incomplete treatment (14), post-operative complications (15), treatment-related toxicity and hospitalisation (16, 17) and mortality (18, 19).
Despite the appeal of a geriatric assessment with granular description of frailty, no studies have reported on the inclusion of a prospective FI assessment into routine practice in Australia, and few international groups have incorporated the FI outside of clinical trials (16). Prospective studies have also tended to focus on those who were initially planned for chemotherapy, rather than a more heterogenous and potentially more frail real-world population, or those receiving more nuanced treatments such as immunotherapy or targeted therapy which may be more tolerable in older adults (16, 20). Finally, few groups have sought to test associations between the FI as a continuous variable and treatment outcomes, instead relying on cut-off points to categorise older adults (21).
The aim of this single centre retrospective study was to determine whether baseline FI was associated with treatment completion and adverse outcomes in older patients with cancer when frailty measurement was incorporated into routine practice.




The Princess Alexandra Hospital (PAH) is a major metropolitan referral centre servicing over one million residents in Queensland, Australia. Approximately 650 adults over 70 years of age are referred to the PAH oncology clinics each year. An FI has been derived from a nurse-led multidomain geriatric assessment to evaluate frailty in patients aged 65 years or older with cancer (14). A nurse-led model of care with baseline comprehensive assessment and a modified 58-item frailty index (Appendix A, Table 1) has now been implemented for patients aged over 65 years with lung malignancies from January 2019, and over 70 years with other malignancies from June 2020. The goal of the current model of care is to assess for frailty and vulnerabilities in all patients aged 70 years or older before their first appointment with their treating specialists. Interventions, allied health referrals, and other management strategies are enacted by the geriatric oncology specialist nurse completing the frailty assessment.

Participants and Procedure

Electronic medical records of new referrals to the oncology outpatients between January 2019 and July 2021 were reviewed to identify patients with a recorded FI. Patients were eligible to be included in this study if they were aged 65 years or older; had a diagnosis of a solid malignancy; had completed an FI assessment; and were referred for consideration of systemic anticancer therapy. Patients were excluded if they did not have a recorded FI, did not attend their specialist appointment, or were referred for endocrine therapy alone.


The FI utilised is a multidimensional assessment tool derived from a multi-domain assessment which takes between 15 to 45 minutes to complete. The scoring of this frailty index has a denominator of 58 if all deficits are assessed (see appendix a, table a1). In keeping with established evidence, an FI is considered valid if at least 30 items across multiple health domains are evaluated, provided these deficits do not saturate with age, are prevalent in at least one percent of the population, and are associated with health status (12). A higher FI represents a greater degree of frailty, and an FI > 0.25 was used to categorise patients as frail based on its contruct and predictive validity in community-dwelling adults (21, 22). Patients were divided into quartiles according to their FI to understand the association between greater levels of frailty and clinical outcomes. In keeping with prior research in this setting (14), the FI was also tested in 0.1 increments for the purpose of expressing a clinically meaningful odds ratio (OR).
Baseline demographics were recorded including age, sex, Eastern Cooperative Oncology Group (ECOG) performance status, cancer type and stage, and treatment intent. Treatment intent was defined as either curative or palliative according to treatment options available at their oncologist appointment, irrespective of whether or not they proceeded with treatment. Treatment details included were regimen, number of cycles, and upfront dose modification.
The primary outcome was treatment completion. This was divided into ‘treatment complete’, ‘treatment incomplete’, or ‘not treated’. ‘Treatment complete’ was defined as having completed all proposed cycles of systemic therapy at baseline without dose modification. ‘Treatment incomplete’ was defined as having treatment discontinuation or dose modification due to intolerance, toxicity, or patient or physician choice. ‘No treatment’ was defined as having no treatment due to either patient or physician choice (14).
Secondary outcomes included the association between baseline FI and adverse events for those who started treatment. These were grade 3+ treatment-related toxicity as per CTCAE v5.0, and unplanned hospital admissions during treatment. Data regarding significant treatment-related toxicities were assessed retrospectively by study authors via electronic medical records of outpatient oncology assessment notes, nurse treatment administration entries, and from acute hospital admissions. As part of standard practice, toxicities are routinely graded according to CTCAE by oncology nurses at each subsequent treatment assessment administration. Hospital admission data were available for the primary institution and linked public hospitals. Overall survival was determined from the date of first treatment, or the appointment when a decision was made to not pursue treatment, to death from any cause.

Statistical Analysis

Data were analysed using STATA v.16. For continuous data, mean, standard deviation (SD), or median, interquartile range (IQR), and 95% confidence intervals (CI) were assessed, for normal and non-normally distributed data, as necessary. Oneway ANOVA and Kruskal-Wallis H tests were performed between quartiles. Categorical variables were assessed using Chi-squared or Fisher’s exact tests. Spearman’s correlation coefficient was tested to determine correlation between continuous FI and ECOG. Univariate and multivariable ordinal regression was performed to assess the association of treatment completion with baseline continuous FI. Covariates included age, sex, ECOG, treatment intent (palliative vs. curative), and stage. FI was tested separately as an ordinal variable in 0.1 increments for ease in understanding odds ratios. Covariates were retained in a forward selection process if they were significant at p < .15. A similar process was undertaken to determine the association of adverse outcomes with baseline continuous FI. Covariates included age, sex, ECOG, treatment intent, and treatment type (chemotherapy, immunotherapy, or targeted therapy). No significant issues with assumption of proportionality were identified.
Overall survival was analysed with Kaplan-Meier estimations for frail (FI > 0.25) and fit (FI ≤ 0.25) patients. Cox proportional hazard analysis was conducted for covariates of age, sex, continuous FI, frailty (FI > 0.25), performance status (ECOG 0-1 vs. ECOG ≥ 2), and treatment status (treated vs. non treated). No significant issues with fit or assumption of proportional hazards were identified.




Between January 2019 and July 2021, there were 815 new referrals to the oncology clinic which met the age and cancer inclusion criteria. Of these referrals, 267 had a documented FI. Forty were excluded for the following reasons: referred for endocrine therapy alone (n=25), were treated in another health service (n=3), did not attend clinic (n=2), had already commenced treatment (n=2), no available treatment options (n=8). Two hundred and twenty-seven patients met eligibility criteria and were included in the analysis. Baseline demographics are presented in Table 1. For the whole sample, the median (IQR) age was 75 years (71-79) and patients were predominantly male (n = 130, 57%). Lung cancer (n = 127, 56%) and gastrointestinal cancer (n = 42, 19%) were the most prevalent malignancies. Most patients had a good performance status (ECOG 0-1; n = 159, 70%).

Table 1. Baseline characteristics according to frailty quartile

Abbreviations: IQR: interquartile range; SD: standard deviation; ECOG: Eastern Cooperative Oncology Group. Patients divided into quartiles from least frail (Q1) to most frail (Q4) according to increasing frailty index. * statistically significant † Was not significant when excluding ‘No Treatment’ (p = 0.207).


The most frequent treatment was chemotherapy (n = 52, 23%), followed by immunotherapy (n = 42, 19%), concurrent chemoradiotherapy (n = 30, 13%), targeted therapy (n = 23, 10%) and combination chemoimmunotherapy (n = 10, 4%). Dual agent chemotherapy (n = 30, 58%) was more common than monotherapy (n = 22, 42%). Nineteen (8%) had upfront dose modifications.
The FI was distributed with slight right skew, mean (SD) of 0.24 (0.12) and median (IQR) of 0.24 (0.15–0.31). The highest recorded FI was 0.59, which was below the clinical maximum of 0.70.(23) Fifty-seven percent (n = 129) were fit and 43% (n = 98) were frail.

Frailty and Correlation with Age, Sex, and Cancer Type

Table 1 also shows patient demographics across the quartiles of frailty. The median FI (IQR) values were 0.11 (0.03), 0.20 (0.02), 0.28 (0.02), and 0.40 (0.07) (table 1). There was no significant difference comparing the age of these groups (p = .79), however women were more likely to be in the frailer quartiles (p = .01).
The two frailer quartiles had a significantly higher proportion of patients with ECOG 2 and ECOG 3-4 (p < .01). There were 45 (28%) patients who were ECOG 0-1 and frail. There was a moderate positive correlation between continuous FI and ECOG (rs = 0.61, p < .01).

Associations with Treatment Completion

Those who completed treatment were the least frail (FI median (IQR): 0.20 (0.13-0.27)), compared with those who did not complete allocated treatment (FI median (IQR): 0.24 (0.14-0.30)) and those who did not commence treatment (FI median (IQR): 0.29 (0.21-0.40)). There was a significant difference in treatment outcomes between frailty quartiles (χ2(6)= 25.53, p < .01). As demonstrated in Figure 1, 40% (n = 74) of the least frail quartile (Q1) completed treatment compared with 24% (n = 13) of the frailest quartile, half of whom did not commence systemic therapy (n = 26, 48%).

Figure 1. Treatment completion outcomes according to frailty index quartile, from least frail (Q1) to most frail (Q4)

Treatment outcomes were operationally defined as: ‘Treatment Complete’, completed all planned baseline cycles of systemic therapy without significant dose modification; ‘Treatment Incomplete’, did not complete all planned cycles of systemic therapy at initial dose due to adverse events, or decision to cease therapy due to toxicity; and ‘Not Treated’, did not receive treatment after first appointment due to patient and/or physician decision.


Multivariable ordinal regression demonstrated that baseline FI was associated with treatment outcome (coefficient 3.4, 95% CI 0.50-6.44; p = .02) when adjusted for age, stage and ECOG. Age was not significantly associated with treatment completion (coefficient 0.2, 95% CI -0.03 – 0.07; p = .44). Each 0.1 increase in FI was significantly associated a 37% increase in the likelihood of not completing or not receiving treatment (OR 1.37, 95% CI 1.02-1.84; p = .04).

Associations with Adverse Outcomes

The frailest group (n = 26), were more likely to experience high grade treatment-related toxicity (46% vs. 26%; p < .01) and unplanned hospital admissions (50% vs. 28%; p = .03) compared with the least frail group (table 2). For those who received treatment (n = 137), age, stage and ECOG were not significant predictors of adverse outcomes in univariable logistic regression. In multivariate linear regression, continuous FI was associated with grade 3+ treatment-related toxicity (adjusted for sex; coefficient 3.95, 95% CI 0.58-7.32; p = .02). It was also associated with unplanned hospitalisation (adjusted for palliative treatment intent; coefficient 3.90, 95% CI 0.59-7.28; p = .02) (see table 3). Each 0.1 increase in FI was associated with a 60% increase in the likelihood of experiencing toxicity (OR 1.60, 95% CI 1.14-2.23; p < .01) and a 61% increase in the likelihood of unplanned hospitalisation (OR 1.61; 95% CI 1.16-2.25; p < .01). Those receiving targeted therapies (n = 23) and immunotherapy (n = 42) had a lower likelihood of toxicity (adjusted OR 0.37, 95% CI 0.16-0.86; p = .02) compared to those receiving chemotherapy (n = 92).

Table 2. Treatment outcomes and adverse events

Abbreviation SD: standard deviation. * statistically significant. Treatment Outcomes defined as: ‘Treatment Complete’, completed all planned baseline cycles of systemic therapy without significant dose modification; ‘Treatment Incomplete’, did not complete all planned cycles of systemic therapy at initial dose due to adverse events, or decision to cease therapy due to toxicity; ‘Not Treated’, did not receive treatment after first appointment due to patient and/or physician decision.

Table 3. Univariate and multivariable regression analysis of adverse treatment outcomes for patients who received treatment (N = 127)

* Statistically significant. ECOG, Eastern Cooperative Oncology Group; CI, confidence interval. Covariates were retained in a forward selection method for multivariable analysis at p < .15.


Associations with Survival

After median follow up of 7.5 months (range 0.9–32.2), 52 patients had died, 38 were frail and 14 were fit. Kaplan-Meier estimates (figure 2) show that median survival in the frail group was 8.9 months (95% CI 7.2-17.7) and not reached in the fit group (95% CI 20.2-NR) (log-rank test p < .001). Continuous FI (HR 51.81, 95% CI 7.07-379.57; p < .01), but not age (HR 1.00, 95% CI 0.94-1.05; p = .87) nor sex (female; HR 1.16, 95% CI 0.67-2.02; p = .60), was predictive of overall survival in univariate cox proportional hazards analyses. In multivariable analysis, frailty (HR 2.81, 95% CI 1.42-5.56; p < .01) and receiving treatment (HR 0.31, 95% CI 0.17-0.57; p < .01) were associated with overall survival when adjusted for ECOG.

Figure 2. Kaplan Meier overall survival estimates are illustrated for fit (FI ≤ 0.25) and frail (FI > 0.25) patients



This is the first study to report outcomes after implementing baseline FI into routine practice in an Australian cancer centre. It is also one of very few studies testing and reporting associations between treatment-related outcomes and the frailty index as a continuous measure. The FI was found to be a clinically meaningful assessment for older patients referred for consideration of systemic therapy. Increasing FI was associated with lower likelihood of commencing or completing treatment, and the group receiving no treatment were significantly frailer than those who commenced treatment. For patients who commenced systemic therapy, increasing FI was associated with an increased likelihood of significant treatment-related toxicity and unplanned hospital admissions during treatment. Increasing FI was also associated with increased risk of mortality.
There has been increasing interest in screening tools to measure frailty, and while some are independently associated with survival and adverse outcomes, no single tool has adequate discriminatory ability to replace a more comprehensive assessment (7). Incorporating a multi-domain geriatric assessment, whilst also deriving an FI from that assessment, is advantageous as it identifies health domains for potential management strategies, and it provides a continuous measure of frailty to assist patients, caregives, and clinicians in decision making regarding risks and benefits of interventions. Furthermore, it may help to identify associations between frailty and outcomes where single domain vulnerabilities do not (24). This study therefore adds weight to the existing evidence supporting frailty measurement in routine practice, particularly its ability to discern vulnerabilities in a heterogenous population with good performance status.
In the present study, nearly half of the population referred for consideration of systemic therapy were frail (FI>0.25) (21). There was significant variation in those with good performance status, consistent with recommendations to screen all older patients for vulnerabilities not identified in routine practice, regardless of their performance status (25). The median FI in this study is similar to previously reported data, despite the inclusion of an older and more diverse cohort (14). While lower FI has been reported in several studies, they selected for characteristics likely to influence the relative fitness of the population, including early stage breast cancer, ovarian cancer after completion of primary debulking surgery, and patients deemed suitable for chemotherapy (17, 26, 27). The higher FI reported in a recent Australian study may be attributed to recruiting patients during unplanned hospital admissions, rather than in a community-dwelling setting (18). The high prevalence of frailty in the present study is consistent with the use of a sensitive multidimensional assessment rather than rules-based measures such as the frailty phenotype, and likely reflects population characteristics such as advanced stage disease, thoracic malignancies, and comorbidities (5).
Frailty has been associated with adverse treatment outcomes, though studies have often been limited by unidimensional assessments, small cohorts, heterogenous populations and inconsistent frailty criteria (28, 29). In a population of 500 patients commencing chemotherapy, Cohen et al. (2016) found that frailty (FI ≥ 0.35) predicted high grade treatment-related toxicity, chemotherapy discontinuation, and hospitalisation, but was not associated with dose-delays or reductions. While this study was instrumental in future implementation of categorical frailty assessment in international centres using CGA-derived FI (robust/non-frail, < 0.2; prefrail, 0.2 < 0.35; and frail, ≥ 0.35), the associations between their continuous FI and treatment outcomes were not reported. In contrast, in the present study, a continuous FI was associated with both treatment completion and adverse outcomes, while also including patients on less toxic therapies such as immunotherapy and targeted therapies. Frailty was also associated with significantly worse survival, even when adjusted for ECOG and treatment status. Continuous FI, rather than age, was associated with mortality in univariate analysis. The median FI for those with incomplete treatment was 0.24. This is clinically relevant, as in some studies these patients would be considered frail, and in others they would not (16, 19). As a major advantage of the FI is its granulity, and few studies have yet to derive or validate optimum FI cut-off points to predict outcomes, these data support the value in maintaining the FI as a continuous variable, rather than potentially miscategorising patients (21).
This study has several limitations, most notably that it is a retrospective review with a small sample size. In contrast with hospital admissions and discontinuation due to toxicity, the capture of retrospective treatment-related toxicity is imperfect, subject to interpreter bias, and may have underreported significant toxicities. Early implementation of frailty assessment in thoracic oncology, where frailty is known to influence treatment-toxicities and survival, contributed to its overrepresentation and these results may not be generalisable in a non-lung cancer setting (19, 26). Additionally, while the current model of care aims to implement frailty screening for all older adults with cancer, it remains likely that some frailer patients were prioritised due to clinician concern, however this data could not be captured. Further, as the frailty assessments were generally completed prior to initial oncologist appointments, the authors were not able to determine to the degree to which FI itself influenced treatment decisions (30). The operational definitions did not account for the nuances of treatment selection based on PD-L1 status and tumour characteristics, where patients may have commenced more tolerable ‘non-standard’ therapies. In addition, low grade toxicities were not recorded in this study, and these may have an important impact on function and quality of life for frail patients (31). Finally, patients were assessed and supported by a specialist geriatric oncology nurse, which limits its applicability to settings where these services are not available.
This study also had strengths, notably that frailty was measured as a continuous variable in a diverse range of cancer types and that frail patients were not excluded from treatment. Despite known and perceived barriers to geriatric assessment in oncology, one geriatric oncology nurse was able to complete FI assessment in one third of older adults with cancer at this tertiary centre, suggesting that routine frailty measurement is feasible as part of routine clinical practice (32). The acceptability and feasibility of our FI assessment is currently being evaluated in this centre and in non-cancer cohorts. Futhermore, the feasibility and validity of a self-reported FI assessment will be tested in a similar population with a view to more broadly implementing these key geriatric assessments.
This study supports the growing evidence that frailty assessment tools and geriatric assessments are a valuable component of patient-centred care (33). Importantly, multidimensional assessment at baseline offers the opportunity for individualised treatment plans and potential interventions for older patients at risk (34, 35). Whether these interventions can reverse frailty and definitively improve treatment outcomes and patient reported outcomes warrants further investigation (36).



Routine FI is a clinically meaningful assessment for older adults with cancer referred for consideration of systemic therapy, and may be predictive of treatment outcomes and adverse outcomes in a vulnerable population.


Conflicts of interest: The authors declare that they have no conflicts of interest.

Ethics approval: This study received ethics approval from local institution human research and ethics committee (HREC/2021/QMS/77072 (Sep ver 2)).

Consent for publication: All authors have seen and approved the final manuscript, and agree to its submission to the Journal of Frailty & Aging.

Availability of data: Data available from corresponding author on reasonable request with approval from data custodian at Princess Alexandra Hospital.

Funding: The researchers also acknowledge the inkind funding and support provided by Division of Cancer Services, Princess Alexandra Hospital to conduct this research.

Author contributions: Study concepts: Fletcher J, Reid N, Hubbard RE, Berry R, Weston M, Walpole E, Ladwa R. Study design: Fletcher J, Reid N, Hubbard RE, Ladwa R. Data acquisition: Fletcher J, Berry R. Quality control of data and algorithms: Fletcher J. Data analysis and interpretation: Fletcher J, Reid N, Hubbard RE, Thaker DA, Ladwa R. Statistical analysis: Fletcher J, Reid N. Manuscript preparation: Fletcher J. Manuscript editing: Fletcher J, Reid N, Hubbard RE, Thaker DA, Ladwa R. Manuscript review: Fletcher J, Reid N, Hubbard RE, Berry R, Weston M, Walpole E, Kimberley R, Thaker DA, Ladwa R. All authors read and approved the final manuscript.





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