Leah L Zullig1, Gretchen Kimmick2, Valerie Smith3, Katie Little4, Hayden B Bosworth5, Sarah Gonzales3, Megan M Oakes6, Rebecca A Shelby7, Lynda Owen4, Ivy P Altomare8. 1. Center for Health Services Research in Primary Care, Durham Veterans Affairs Health Care System, Durham, NC, United States; Department of Population Health Sciences, Duke University, Durham, NC, United States. Electronic address: leah.zullig@duke.edu. 2. Division of Medical Oncology, Duke University, Durham, NC, United States. 3. Center for Health Services Research in Primary Care, Durham Veterans Affairs Health Care System, Durham, NC, United States; Department of Population Health Sciences, Duke University, Durham, NC, United States. 4. Duke Cancer Network, Durham, NC, United States. 5. Center for Health Services Research in Primary Care, Durham Veterans Affairs Health Care System, Durham, NC, United States; Department of Population Health Sciences, Duke University, Durham, NC, United States; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States; School of Nursing, Duke University, Durham, NC, United States. 6. Department of Population Health Sciences, Duke University, Durham, NC, United States. 7. Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States. 8. Division of Medical Oncology, Duke University, Durham, NC, United States; Duke Cancer Network, Durham, NC, United States.
Abstract
OBJECTIVE: Geriatric screening tools assess functional limitations and inform clinical decision-making for older adults with cancer. Our objective was to evaluate the feasibility and effectiveness of a screener in community-based oncology clinics. MATERIALS AND METHODS: Eligible patients were from two rural, underserved community-based cancer clinics; within 12 months of a cancer diagnosis (breast, lung, colorectal, pancreas, esophageal); aged ≥60 years; and not exclusively pursuing palliative care. We used a previously validated tool that was embedded in the electronic health record (EHR). Patient-reported responses identified memory impairment, depressive symptoms, deficits in activities of daily living, poor nutrition, and polypharmacy. At the discretion of the oncologist, responses prompted service referrals. From the EHR, we extracted information about referrals and completion of planned therapy. We present descriptive statistics. RESULTS: Enrolled patients (n = 44) had a mean age of 71.5 years (SD = 6.9). Most were non-white (61%), women (66%), with government-sponsored health insurance (80%). The most commonly identified geriatric syndromes: polypharmacy (89%), reduced quality of life (39%), and poor nutrition (39%). The screener triggered a referral in 98% of patients. Generated referrals were for depressive symptoms (52% needed, 39% received), nutrition (43% needed, 37% received), and polypharmacy (89% needed, 26% received). Patients were referred to social work (56%), nutrition (44%), and pharmacy (25%). Many patients completed planned radiation therapy (100%), surgery (70%), and chemotherapy (60%). CONCLUSIONS: Use of an EHR-embedded brief geriatric oncology assessment in rural oncology clinics identified geriatric syndromes that would benefit from provision of services in nearly all enrolled patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02906592. Published by Elsevier Ltd.
OBJECTIVE: Geriatric screening tools assess functional limitations and inform clinical decision-making for older adults with cancer. Our objective was to evaluate the feasibility and effectiveness of a screener in community-based oncology clinics. MATERIALS AND METHODS: Eligible patients were from two rural, underserved community-based cancer clinics; within 12 months of a cancer diagnosis (breast, lung, colorectal, pancreas, esophageal); aged ≥60 years; and not exclusively pursuing palliative care. We used a previously validated tool that was embedded in the electronic health record (EHR). Patient-reported responses identified memory impairment, depressive symptoms, deficits in activities of daily living, poor nutrition, and polypharmacy. At the discretion of the oncologist, responses prompted service referrals. From the EHR, we extracted information about referrals and completion of planned therapy. We present descriptive statistics. RESULTS: Enrolled patients (n = 44) had a mean age of 71.5 years (SD = 6.9). Most were non-white (61%), women (66%), with government-sponsored health insurance (80%). The most commonly identified geriatric syndromes: polypharmacy (89%), reduced quality of life (39%), and poor nutrition (39%). The screener triggered a referral in 98% of patients. Generated referrals were for depressive symptoms (52% needed, 39% received), nutrition (43% needed, 37% received), and polypharmacy (89% needed, 26% received). Patients were referred to social work (56%), nutrition (44%), and pharmacy (25%). Many patients completed planned radiation therapy (100%), surgery (70%), and chemotherapy (60%). CONCLUSIONS: Use of an EHR-embedded brief geriatric oncology assessment in rural oncology clinics identified geriatric syndromes that would benefit from provision of services in nearly all enrolled patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02906592. Published by Elsevier Ltd.
Authors: Colby J Hyland; Ruby Guo; Ravi Dhawan; Manraj N Kaur; Paul A Bain; Maria O Edelen; Andrea L Pusic Journal: J Patient Rep Outcomes Date: 2022-03-07