Browsing by Author "Dilawari, Asma A."

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    Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning: The Thinking and Living with Cancer study
    (Elsevier, 2022-11) Van Dyk, Kathleen; Ahn, Jaeil; Zhou, Xingtao; Zhai, Wanting; Ahles, Tim A.; Bethea, Traci N.; Carroll, Judith E.; Cohen, Harvey Jay; Dilawari, Asma A.; Graham, Deena; Jacobsen, Paul B.; Jim, Heather; McDonald, Brenna C.; Nakamura, Zev M.; Patel, Sunita K.; Rentscher, Kelly E.; Saykin, Andrew J.; Small, Brent J.; Mandelblatt, Jeanne S.; Root, James C.; Radiology and Imaging Sciences, School of Medicine
    Introduction: Many cancer survivors report cognitive problems following diagnosis and treatment. However, the clinical significance of patient-reported cognitive symptoms early in survivorship can be unclear. We used a machine learning approach to determine the association of persistent self-reported cognitive symptoms two years after diagnosis and neurocognitive test performance in a prospective cohort of older breast cancer survivors. Materials and Methods: We enrolled breast cancer survivors with non-metastatic disease (n=435) and age- and education-matched non-cancer controls (n=441) between August 2010 and December 2017 and followed until January 2020; we excluded women with neurological disease and all women passed a cognitive screen at enrollment. Women completed the FACT-Cog Perceived Cognitive Impairment (PCI) scale and neurocognitive tests of attention, processing speed, executive function, learning, memory and visuospatial ability, and timed activities of daily living assessments at enrollment (pre-systemic treatment) and annually to 24 months, for a total of 59 individual neurocognitive measures. We defined persistent self-reported cognitive decline as clinically meaningful decline (3.7+ points) on the PCI scale from enrollment to twelve months with persistence to 24 months. Analysis used four machine learning models based on data for change scores (baseline to twelve months) on the 59 neurocognitive measures and measures of depression, anxiety, and fatigue to determine a set of variables that distinguished the 24-month persistent cognitive decline group from non-cancer controls or from survivors without decline. Results: The sample of survivors and controls ranged in age from were ages 60–89. Thirty-three percent of survivors had self-reported cognitive decline at twelve months and two-thirds continued to have persistent decline to 24 months (n=60). Least Absolute Shrinkage and Selection Operator (LASSO) models distinguished survivors with persistent self-reported declines from controls (AUC=0.736) and survivors without decline (n=147; AUC=0.744). The variables that separated groups were predominantly neurocognitive test performance change scores, including declines in list learning, verbal fluency, and attention measures. Discussion: Machine learning may be useful to further our understanding of cancer-related cognitive decline. Our results suggest that persistent self-reported cognitive problems among older women with breast cancer are associated with a constellation of mild neurocognitive changes warranting clinical attention.
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    Associating Persistent Self-Reported Cognitive Decline with Neurocognitive Decline in Older Breast Cancer Survivors Using Machine Learning: The Thinking and Living with Cancer Study
    (Elsevier, 2022) Van Dyk, Kathleen; Ahn, Jaeil; Zhou, Xingtao; Zhai, Wanting; Ahles, Tim A.; Bethea, Traci N.; Carroll, Judith E.; Cohen, Harvey Jay; Dilawari, Asma A.; Graham, Deena; Jacobsen, Paul B.; Jim, Heather; McDonald, Brenna C.; Nakamura, Zev M.; Patel, Sunita K.; Rentscher, Kelly E.; Saykin, Andrew J.; Small, Brent J.; Mandelblatt, Jeanne S.; Root, James C.; Radiology and Imaging Sciences, School of Medicine
    Introduction: Many cancer survivors report cognitive problems following diagnosis and treatment. However, the clinical significance of patient-reported cognitive symptoms early in survivorship can be unclear. We used a machine learning approach to determine the association of persistent self-reported cognitive symptoms two years after diagnosis and neurocognitive test performance in a prospective cohort of older breast cancer survivors. Materials and methods: We enrolled breast cancer survivors with non-metastatic disease (n = 435) and age- and education-matched non-cancer controls (n = 441) between August 2010 and December 2017 and followed until January 2020; we excluded women with neurological disease and all women passed a cognitive screen at enrollment. Women completed the FACT-Cog Perceived Cognitive Impairment (PCI) scale and neurocognitive tests of attention, processing speed, executive function, learning, memory and visuospatial ability, and timed activities of daily living assessments at enrollment (pre-systemic treatment) and annually to 24 months, for a total of 59 individual neurocognitive measures. We defined persistent self-reported cognitive decline as clinically meaningful decline (3.7+ points) on the PCI scale from enrollment to twelve months with persistence to 24 months. Analysis used four machine learning models based on data for change scores (baseline to twelve months) on the 59 neurocognitive measures and measures of depression, anxiety, and fatigue to determine a set of variables that distinguished the 24-month persistent cognitive decline group from non-cancer controls or from survivors without decline. Results: The sample of survivors and controls ranged in age from were ages 60-89. Thirty-three percent of survivors had self-reported cognitive decline at twelve months and two-thirds continued to have persistent decline to 24 months (n = 60). Least Absolute Shrinkage and Selection Operator (LASSO) models distinguished survivors with persistent self-reported declines from controls (AUC = 0.736) and survivors without decline (n = 147; AUC = 0.744). The variables that separated groups were predominantly neurocognitive test performance change scores, including declines in list learning, verbal fluency, and attention measures. Discussion: Machine learning may be useful to further our understanding of cancer-related cognitive decline. Our results suggest that persistent self-reported cognitive problems among older women with breast cancer are associated with a constellation of mild neurocognitive changes warranting clinical attention.
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    Associations between longitudinal changes in sleep disturbance and depressive and anxiety symptoms during the COVID-19 virus pandemic among older women with and without breast cancer in the thinking and living with breast cancer study
    (Wiley, 2022) Bethea, Traci N.; Zhai, Wanting; Zhou, Xingtao; Ahles, Tim A.; Ahn, Jaeil; Cohen, Harvey J.; Dilawari, Asma A.; Graham, Deena M.A.; Jim, Heather S.L.; McDonald, Brenna C.; Nakamura, Zev M.; Patel, Sunita K.; Rentscher, Kelly E.; Root, James; Saykin, Andrew J.; Small, Brent J.; Van Dyk, Kathleen M.; Mandelblatt, Jeanne S.; Carroll, Judith E.; Radiology and Imaging Sciences, School of Medicine
    Purpose: Several studies have reported sleep disturbances during the COVID-19 virus pandemic. Little data exist about the impact of the pandemic on sleep and mental health among older women with breast cancer. We sought to examine whether women with and without breast cancer who experienced new sleep problems during the pandemic had worsening depression and anxiety. Methods: Breast cancer survivors aged ≥60 years with a history of nonmetastatic breast cancer (n = 242) and frequency-matched noncancer controls (n = 158) active in a longitudinal cohort study completed a COVID-19 virus pandemic survey from May to September 2020 (response rate 83%). Incident sleep disturbance was measured using the restless sleep item from the Center for Epidemiological Studies-Depression Scale (CES-D). CES-D score (minus the sleep item) captured depressive symptoms; the State-Anxiety subscale of the State Trait Anxiety Inventory measured anxiety symptoms. Multivariable linear regression models examined how the development of sleep disturbance affected changes in depressive or anxiety symptoms from the most recent prepandemic survey to the pandemic survey, controlling for covariates. Results: The prevalence of sleep disturbance during the pandemic was 22.3%, with incident sleep disturbance in 10% and 13.5% of survivors and controls, respectively. Depressive and anxiety symptoms significantly increased during the pandemic among women with incident sleep disturbance (vs. no disturbance) (β = 8.16, p < 0.01 and β = 6.14, p < 0.01, respectively), but there were no survivor-control differences in the effect. Conclusion: Development of sleep disturbances during the COVID-19 virus pandemic may negatively affect older women's mental health, but breast cancer survivors diagnosed with the nonmetastatic disease had similar experiences as women without cancer.
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    Loneliness and mental health during the COVID‐19 pandemic in older breast cancer survivors and noncancer controls
    (Wiley, 2021-10-01) Rentscher, Kelly E.; Zhou, Xingtao; Small, Brent J.; Cohen, Harvey J.; Dilawari, Asma A.; Patel, Sunita K.; Bethea, Traci N.; Van Dyk, Kathleen M.; Nakamura, Zev M.; Ahn, Jaeil; Zhai, Wanting; Ahles, Tim A.; Jim, Heather S.L.; McDonald, Brenna C.; Saykin, Andrew J.; Root, James C.; Graham, Deena M.A.; Carroll, Judith E.; Mandelblatt, Jeanne S.; Radiology and Imaging Sciences, School of Medicine
    Background: The coronavirus disease 2019 (COVID-19) pandemic has had wide-ranging health effects and increased isolation. Older with cancer patients might be especially vulnerable to loneliness and poor mental health during the pandemic. Methods: The authors included active participants enrolled in the longitudinal Thinking and Living With Cancer study of nonmetastatic breast cancer survivors aged 60 to 89 years (n = 262) and matched controls (n = 165) from 5 US regions. Participants completed questionnaires at parent study enrollment and then annually, including a web-based or telephone COVID-19 survey, between May 27 and September 11, 2020. Mixed-effects models were used to examine changes in loneliness (a single item on the Center for Epidemiologic Studies-Depression [CES-D] scale) from before to during the pandemic in survivors versus controls and to test survivor-control differences in the associations between changes in loneliness and changes in mental health, including depression (CES-D, excluding the loneliness item), anxiety (the State-Trait Anxiety Inventory), and perceived stress (the Perceived Stress Scale). Models were adjusted for age, race, county COVID-19 death rates, and time between assessments. Results: Loneliness increased from before to during the pandemic (0.211; P = .001), with no survivor-control differences. Increased loneliness was associated with worsening depression (3.958; P < .001) and anxiety (3.242; P < .001) symptoms and higher stress (1.172; P < .001) during the pandemic, also with no survivor-control differences. Conclusions: Cancer survivors reported changes in loneliness and mental health similar to those reported by women without cancer. However, both groups reported increased loneliness from before to during the pandemic that was related to worsening mental health, suggesting that screening for loneliness during medical care interactions will be important for identifying all older women at risk for adverse mental health effects of the pandemic.