Background
Vulnerable older patients with cancer are at high risk for adverse outcomes, including serious toxicities from cancer treatments.1,2 Despite a dramatic increase in the number of new cancer diagnoses in older patients projected over the next 20 years,3 a critical gap in knowledge exists regarding how to improve outcomes for these patients.4,5 NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Older Adult Oncology6,7 recommend a geriatric assessment (GA) for older patients (>65 years of age) with cancer to identify health status issues that increase the risk of adverse outcomes. A GA is a comprehensive evaluation of a patient's physical, functional, social, and psychological well-being that predicts morbidity and mortality in community-dwelling older adults.8 A GA can also predict chemotherapy toxicity and survival in patients with cancer.1,9–12 Although GA has been extensively studied, incorporating the results into oncology clinical practice have not been standardized.
Most oncologists have received little training in the care of older patients.13 The development of structured “care processes” that provide a framework for how oncologists can incorporate GA into clinical practice could improve outcomes of vulnerable older adults with cancer.14 Care processes refer to the tasks done to and for the patient by practitioners during treatment. Outcome measures are the desired states resulting from care processes, which may include reduction in morbidity and mortality and improvement in quality of life. In geriatric oncology, care processes use GA in 2 distinct, but related, ways. First, GA is used to identify specific evidence-based geriatric interventions to be implemented, such as ordering physical therapy (PT) for a patient with mobility deficits. Second, GA is used to guide cancer treatment decisions, such as modification of chemotherapy dosing in patients with physical or functional impairments. Thus, a “GA-guided care process” is the use of GA to select targeted geriatric interventions and to guide choices for cancer treatment.
The U13 conference series, “Geriatric Oncology Research to Improve Clinical Care,” sponsored by the Cancer and Aging Research Group (CARG) and the National Institute of Health (NIH; grant number U13 AG038151), have highlighted the need for developing GA-guided care processes that could be incorporated into oncology clinical practice.4–8,15 A dedicated group of geriatric oncology teams has developed clinical and research programs that use GA-guided care processes.16
The goal of this study was to obtain expert consensus, using a modified Delphi approach, from leaders in geriatric oncology in the United States on how to best to translate information from GA into care processes (interventions and treatment decisions) for older patients with cancer.
Methods
The Delphi method is a flexible iterative survey process with the goal of transforming individual opinions into a group consensus on issues for which the exact solutions are unknown.17,18 In this study, a 3-round Delphi process was performed, consisting of brainstorming, narrowing down, and quantification of the opinions of expert participants. Data were collected and stored in Research Electronic Data Capture (REDCap), a software toolset for electronic collection and management of research data.19 The University of Rochester provided Institutional Review Board approval of the study.
Expert Panel Selection and Recruitment
Participants included researchers, oncologists, and geriatricians who attended the geriatric oncology U13 conferences.4,5 Participants had to be located in the United States, have research or clinical interests in geriatric oncology, and be 2 or more years from fellowship. Experts were contacted via e-mail, with a link to a survey with 3 parts: a consent form, a demographics section, and a commitment agreement that explained the importance of their continued participation throughout the Delphi process. Participants who consented were sent the first round (Figure 1).
Study Procedures and Analysis
The 3 Delphi rounds were sent to participants 8 to 12 weeks apart. Round 1 (R1) started on August 16, 2012, and Round 3 (R3) ended on July 30, 2013. All responses were quasi-anonymous in that the participants might know the group composition given attendance at a common conference, but could not identify a response to any given participant. Similarly, data analysts were blinded to the identity of participants. A flow chart illustrating the Delphi process is shown in Figure 1.
In R1, participants rated the importance of particular domains to be included in a full GA and to develop criteria to determine who should get the full GA. Participants answered open-ended questions to identify screening tools and high-priority interventions that should be implemented based on GA results. In round 2 (R2), participants rated the criteria and screening tools for selecting patients for GA, assessments for each domain, and GA-guided care processes. Areas of agreement and disagreement helped inform R3. Between rounds, group responses were quantified, summarized, and re-presented to the panel, along with individual responses. Panelists were asked to either confirm their agreement on items that met consensus or provide reasons for responses remaining outside consensus. After R3, the analysis team finalized the algorithms for each GA domain.
Flow chart of Delphi process to reach consensus. A standard Delphi process was used to reach consensus over 3 rounds. Items that did not meet consensus during round 2 were sent back to the panel to reach consensus. Qualitative questions were used in the first round, and quantitative questions were used in the second and third rounds.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 13, 9; 10.6004/jnccn.2015.0137
Flow chart of Delphi process to reach consensus. A standard Delphi process was used to reach consensus over 3 rounds. Items that did not meet consensus during round 2 were sent back to the panel to reach consensus. Qualitative questions were used in the first round, and quantitative questions were used in the second and third rounds.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 13, 9; 10.6004/jnccn.2015.0137
Flow chart of Delphi process to reach consensus. A standard Delphi process was used to reach consensus over 3 rounds. Items that did not meet consensus during round 2 were sent back to the panel to reach consensus. Qualitative questions were used in the first round, and quantitative questions were used in the second and third rounds.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 13, 9; 10.6004/jnccn.2015.0137
In concordance with previous work, a predetermined threshold for consensus was chosen: an interquartile range (IQR) of 2 or more units or greater than 66.7%, providing a utility or helpfulness rating of more than 7 on a 10-point Likert scale.18 For nominal data, consensus was defined as agreement among more than 66.7% of the group. Data in REDCap were exported to SAS 9.3 (Cary, NC) and Excel worksheets for statistical analysis.
Consensus Findings
Demographics
Of 33 invited participants, 30 completed R1 through R3; 2 participants completed R1 only (Table 1). Most (75%) use GA in clinical care. The expert panel was primarily white, non-Hispanic (77%), and female (60%), with an average of 12 years in practice post-fellowship. Most had funding to conduct geriatric oncology research (70%).
Selection Criteria for Full GA
At the completion of R3, 73% of the panelists agreed there should be an age cutoff to establish a standard for which patients should get a full GA (Table 2). When required to choose an age cutoff, 93% of panelists gave a high rating to “All patients aged 75 years and older and those who are younger with age-related issues or concerns” for getting a full GA (IQR=1, meeting consensus). Ninety percent of the panelists agreed that screening with a short geriatric-based tool should be instituted in oncology clinics to determine who should get a full GA.20 However, opinion varied regarding which screening tool was best to use. In this US-based sample, the Vulnerable Elders Survey-13 (VES-13),21 CARG,1 and Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH)8 chemotherapy risk tools and objective physical performance (no specific test) all met consensus by IQR criteria but not by rating criteria.
Geriatric Assessment Domains and Specific Measures
Participants reached consensus on all domains to be included in GA (each with IQR≤2 and a mean rating of ≥8.8 out of 10.0). These domains included functional status, cognition, social support, objective physical performance, psychological status (anxiety and depression), nutrition, comorbidity, and polypharmacy (Table 3). Consensus was often met for more than one tool to assess each domain in clinical practice as part of a GA. The highest-rated tools22 were Activities of Daily Living and Instrumental Activities of Daily Living for functional status; gait speed and “Timed Up and Go”
Demographic Characteristics of Delphi Participants (N=30)
GA-Guided Care Processes
Several high-priority care processes for each geriatric domain reached consensus (Table 4). GA-guided interventions are illustrated in Figure 2 as algorithms that can be followed for impairments in each of the domains.
GA-guided interventions addressing impaired domains that reached consensus included (1) PT and occupational therapy for impaired function;
Usefulness of Age Cutoffs Versus Screening Tools to Select Patients for Geriatric Assessment (N=30)
Utility of Assessment Tools for Incorporation in Geriatric Assessment That Met Consensus (N=30)
The panel also met consensus on ways that impairment in a specific geriatric domain could influence cancer treatment decisions. Treatment decision changes to address impaired domains that met consensus included (1) modification of cancer treatment regimen and evaluation of fall risk for impaired functional status; (2) assessing the presence of a caregiver and limiting the complexity of treatment for patients with impaired cognition; (3) assessing patient safety/tolerability and assessing caregiver support for poor social support; (4) assessing safety of treatment for impaired physical performance; and (5) addressing supportive care and evaluating drug tolerance for poor nutritional status.
Generalizing interventions for comorbidity/polypharmacy in this format was not undertaken because of the numerous comorbidities and drug interactions that would need to be considered. Preliminary results indicate that the panel believes a review of history and physical/medical records (IQR=3; percentage of panel choosing a utility rating of ≥7 = 83%), the Charlson comorbidity index (IQR=2; percentage of panel choosing a utility rating of ≥7 = 59%), and the Cumulative Illness Rating Scale–Geriatric (IQR = 3; percentage of panel choosing a utility rating of ≥7 = 72%) are the most useful tools to assess impairments in comorbidity/polypharmacy. In an open-ended question in R1, 70% of panelists who responded identified pharmacists or primary care providers as essential in developing and comanaging interventions.
Importance of Interventions and Treatment Decisions That Met Consensus (N=30)
Discussion
Although the use of GA in clinical practice for older adults with cancer has been advocated, more evidence-based information is needed to improve outcomes in this population. To our knowledge, this research is the first in the United States that addresses, using a formal consensus process with geriatric oncology experts, how GA can guide care processes in oncology clinical practice.
GA identifies risk factors for adverse outcomes in older patients and adds information to standard oncology performance measures used, which were validated in younger patients.23 Multicenter studies have found that items included in a GA identifies older patients at greatest risk for chemotherapy toxicity and mortality.1,9–11 GA has been found to be feasible in settings other than community oncology clinics.1,24,25 The geriatric oncology expert panelists felt that all GA domains were important to include, and several validated tools met consensus, which is consistent with the growing geriatric oncology literature and other expert panels.22,26,27 Ultimately, the choice of which tool to use should be based on the question being asked, how GA results will be used (eg, for research or clinical practice), and the resources available for implementation.
Controversy still exists regarding how best to select patients for GA in clinical practice. Careful selection of patients, for whom GA is beneficial, is important to guide the use of GA results to inform treatment decisions for vulnerable older patients and to spare fit older patients from unnecessary testing. The NCCN Guidelines for Older Adult Oncology recommend that all patients aged 65 years and older receive a GA (to view the most recent version of these guidelines, visit NCCN.org).6,7 In contrast, the expert panelists supported both an age-based criterion (all patients aged ≥75 years and/or younger patients with age-related health issues) and the use of short, functionally based tools to screen patients for GA.6,7,22 The panelists did not meet consensus on which screening tool to use. In this US study, the VES-13 and chemotherapy toxicity tools (eg, CARG and CRASH) scored the highest in terms of usefulness. In the literature, G8, which was developed from the Mini Nutritional Assessment and is more widely tested in Europe, has been shown in some settings to have higher sensitivity and specificity than VES-13 for identifying older patients who would benefit from GA.28–31 In one study of 1967 patients, 71% had an abnormal G8 score warranting GA, and the G8 was more or equally sensitive than other screening instruments.30 Our results are consistent with
Algorithm of geriatric assessment–guided processes. Geriatric assessment domains are identified in the first column. For each domain, assessment options are listed in the middle column. For each group of assessment options, specific care processes are identified to address the identified needs in the last column. These care processes are presented numerically in order of highest interquartile range (IQR) and percentage of panel participants that chose a utility rating of ≥7 for that item.
Abbreviations: ADL, activities of daily living; Blessed OMC, Blessed Orientation-Memory-Concentration; IADL, instrumental activities of daily living.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 13, 9; 10.6004/jnccn.2015.0137
Algorithm of geriatric assessment–guided processes. Geriatric assessment domains are identified in the first column. For each domain, assessment options are listed in the middle column. For each group of assessment options, specific care processes are identified to address the identified needs in the last column. These care processes are presented numerically in order of highest interquartile range (IQR) and percentage of panel participants that chose a utility rating of ≥7 for that item.
Abbreviations: ADL, activities of daily living; Blessed OMC, Blessed Orientation-Memory-Concentration; IADL, instrumental activities of daily living.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 13, 9; 10.6004/jnccn.2015.0137
Algorithm of geriatric assessment–guided processes. Geriatric assessment domains are identified in the first column. For each domain, assessment options are listed in the middle column. For each group of assessment options, specific care processes are identified to address the identified needs in the last column. These care processes are presented numerically in order of highest interquartile range (IQR) and percentage of panel participants that chose a utility rating of ≥7 for that item.
Abbreviations: ADL, activities of daily living; Blessed OMC, Blessed Orientation-Memory-Concentration; IADL, instrumental activities of daily living.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 13, 9; 10.6004/jnccn.2015.0137
There is no evidence-based approach for how to implement GA-guided care processes in oncology clinical care. Benefits of nononcologic GA-guided interventions include prevention of geriatric syndromes, recognition of cognitive deficits, prevention of hospitalizations and nursing home admissions, and overall improvement of quality of life.33–36 A limited number of studies have shown that GA-guided care processes are feasible to implement in oncology.24,37–39 The ELCAPA study found that the initial oncology treatment plan was modified in 21% of patients based on GA conducted by a geriatrician-led multidisciplinary team.38 In addition, geriatric consultation led to other nononcologic interventions, including a change in prescribed medications (31%), social support assistance (46%), physiotherapy (42%), nutritional care (70%), psychological care (36%), and memory evaluation (21%). In a study by McCorkle et al,40 geriatric nurse practitioners conducted GA and implemented interventions for older patients with cancer, which led to a survival advantagein the intervention group. In a study by Goodwin et al,41 patients with breast cancer in the group who received GA with interventions were significantly more likely to return to normal functioning than controls. Each of these studies suggests the value of GA-guided care processes for older patients with cancer.
A review from Hamaker et al42 summarizes the current state of the literature with regards to GA-guided care processes. Ten observational cohort studies met the inclusion criteria for high-quality studies. The median sample size was 50 patients (range, 15–1967), and samples were heterogeneous with regard to underlying type and stage of cancer. Although there was selection bias in those referred for GA, the prevalence of impairment was high in all geriatric domains. Nononcologic GA-guided interventions were common (>70% in all but one study) and included social interventions (38%), medication management (37%), and nutritional interventions (26%). Psychological, mobility, and comorbidity interventions were recommended for approximately 20% of patients. Although each study reported an approach to care, it is not clear whether any specific algorithm was followed that guided interventions for impairment for each geriatric domain.
In addition to nononcologic GA-guided interventions, GA information affected oncologic treatment decision-making. In 6 of the 10 studies in the review by Hamaker et al,42 the initial treatment plan was modified in 39% of patients after GA evaluation. Considering all studies, two-thirds resulted in less-intensive treatment. Lowering the intensity of recommended treatment is likely an attempt to adjust treatment in patients who have impairments.8 Still, it is important to note that one-third of cancer treatment decisions were changed to become more intensive. In a study by Chaibi et al,43 45 of 161 patients (28%) received more-intensive cancer treatment as a result of GA. Using GA to guide treatment options in clinical trials for older and frailer individuals may help determine whether modifications are appropriate. Various approaches for chemotherapy selection and dosing for older and/or frailer patients are supported by the literature.4 For example, the FOCUS-2 trial found that chemotherapy for advanced colorectal cancer was safe and efficacious in the older and/or frail patient if started at a 20% dose reduction, with escalation as tolerated.44
Some limitations must be taken into consideration when interpreting the results. Expert consensus is not as rigorous as randomized controlled studies. Nevertheless, these results could inform and help guide future efforts to study the impact of care processes on outcomes of older patients with cancer. Because of funding limitations, only US experts were included. A parallel effort included experts outside of the United States,45 and future work will examine the similarities and differences between expert opinion in different parts of the world. Although the experts did reach consensus regarding many treatment-related decisions, the recommendations were often vague (eg, “modify treatment”), which likely reflects the limited data available.
Despite these limitations, this Delphi study provides specific information from expert consensus on how GA should guide nononcologic interventions and oncology treatment decisions. The recommended algorithms, based on expert consensus, presented here need further validation, but they are a first step in standardizing a model of care delivery for vulnerable older patients with cancer.
Conclusions
ASCO has stated that to improve quality of care, oncologists and patients should carefully weigh the risks and benefits of cancer-directed therapy for patients with a low performance status, who are not eligible for a clinical trial, and for whom no strong evidence exists to support the clinical value of treatment.46 These issues commonly affect older adults, and GA can help identify the risks of treatment in older, frailer patients. In this study, experienced geriatric oncology experts reached consensus on how best to use GA to guide both nononcologic and oncologic decisions. These recommendations can be incorporated into clinical oncology practice, including academic centers that are investing in new geriatric oncology programs. Several multicenter studies are underway to evaluate the effect of a multicomponent intervention using these algorithms on adverse clinical events and patient-reported outcomes. Until those results are known, these expert consensus positions provide the best evidence for GA-guided care processes for older patients with cancer.
Dr. Hurria has disclosed that she receives research support from GlaxoSmithKline and Celgene Corporation, and consultant fees/honoraria from GTx Inc. and Boehringer Ingelheim GmbH. The remaining authors have disclosed that they have no financial interests, arrangements, affiliations, or commercial interests with the manufacturers of any products discussed in this article or their competitors.
Presented at the International Society of Geriatric Oncology 2013 Annual Conference, October 24–26, 2013 in Copenhagen, Denmark.
This work was funded by the National Institute on Aging and the National Cancer Institute (U13 AG038151 from the National Institute on Aging and National Cancer Institute [NCI; National Institutes of Health]; R03 AG042342 U10CA37420 and R01 CA177592). The work was funded through a Patient-Centered Outcomes Research Institute (PCORI) Program Award (4634). The work was also funded by the Susan H Green Memorial Grant (to Magnuson) and by the philanthropic donation of Sandy Lloyd to the Geriatric Oncology Program at the James Wilmot Cancer Institute.
Authorship contributions: SGM was involved in the literature search, figures, study design, data collection, data analysis, data interpretation, and writing. CV was involved in the literature search, figures, study design, data collection, data interpretation, and writing. AH was involved in the study design, data interpretation, and writing (editing manuscript). AM was involved in the literature search, data analysis, data interpretation, and writing. LL was involved in the data interpretation and writing of the manuscript. CP was involved in data collection, data analysis, data interpretation, and final manuscript approval. AO was involved in the study design and data interpretation. RGB was involved in the study design, methods on the Delphi technique. WD was involved in the study design, data collection, data interpretation, and writing of the manuscript.
Acknowledgments
The authors wish to thank the following experts for their valuable time and inputs to the study: Andrew Artz, Arti Hurria, Beatriz Korc-Grodzicki, Stuart Lichtman, Aanand Naik, Lodovico Balducci, Andrew Chapman, Louise Walter, Arati Rao, Martine Extermann, Cary Gross, Martine Puts, Cynthia Owusu, Miriam Rodin, Efrat Dotan, Mary Sehl, Ajeet Gajra, Nadine Jackson McCleary, Abdo Haddad, Dale Shepard, Harvey Cohen, Sarah Kagan, Holly Holmes, Supriya Mohile, Heidi Klepin, William Tew, Hyman Muss, Tanya Wildes, Ilene Browner, Gjisberta Vanlonden, James Wallace, and William Dale.
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