Unlocking Electronic Medical Record Data to Identify Possible Connections Between Cancer and Cognitive Declines

As cancer survival rates continue to improve, the number of people reaching old age with a history of cancer increases. Researchers and clinicians have identified several long-term effects of both cancer and its treatment, ranging from late effects of treatment, such as fatigue, to increased risk of serious medical comorbidities. Unfortunately for older adult cancer survivors, this may place them at greater risk for health complications over and above the effects of aging alone. Among the most dreaded diseases associated with aging is dementia, with older adults in particular reporting concerns about whether cognitive declines or memory failings are simply agerelated changes or early symptoms of progressive neurologic disease. Although there are reports of cognitive changes associated with cancer, quantifying the impact of cancer and various cancer therapies on cognitive functioning has proven somewhat challenging, especially for older adult cancer survivors. Several recent meta-analyses of non-CNS cancers have examined the relationships between cancer treatments and performance on various neuropsychological tests. Meta-analytic data revealed significant impairments after chemotherapy for breast cancer, but another small meta-analysis failed to find any cognitive declines after high-dose chemotherapy for patients who received bone marrow transplants. With regard to androgen deprivation therapy (ADT) for prostate cancer, findings from 14 studies demonstrated a moderate negative effect of ADT on visuomotor ability (eg, block design) but no effect on other cognitive domains. Hence, the objective neuropsychological evidence for cognitive changes after different cancer treatments has only confirmed a limited number of deficits for patients with a history of cancer. However, few studies evaluating cognitive performance have focused on patients during long-term survivorship (5 or more years after treatment completion) and do not provide information about rates of mild cognitive impairment or dementia. In fact, many of these studies screened out patients with possible cognitive disorders. Another approach for evaluating the cognitive impact of cancer and its treatments are natural experiments using twins or population-based data; those with a history of cancer and control participants without cancer are compared on rates of impairment in the years after cancer treatment. Heflin et al demonstrated that twins discordant for mixed cancer types performed more poorly than cotwins without cancer on telephone-administered cognitive tasks and demonstrated a trend for higher rates of clinically confirmed dementia. A follow-up twin study by the same research group found increased dementia risk for female cancer survivors, especially gynecologic cancer survivors who received treatment that disrupts ovarian function; male cancer survivors, in contrast, were not at increased risk. In population-based studies, data failed to show a connection between cancer and increased risk for dementia in mixed cancer samples compared with control participants without cancer. Porter examined cognitive scores in a population-based sample of adults older than age 50 years controlling for health conditions and health behaviors that may affect cognition; analyses failed to show poorer performance in those with a history of any cancer or chemotherapy compared with those no such histories. When focusing exclusively on patients with breast cancer with or without a history of chemotherapy in a population-based cancer registry, there was no difference between groups on overall dementia risk. Hence, findings have been mixed with regard to the link between cancer, its treatments, and cognitive impairments, but few studies have been precise about the population or treatment of interest. The article by Nead et al that accompanies this editorial built on the natural experiment model for examining dementia risk in patients with cancer by harnessing information from the electronic medical records (EMRs) of two large medical centers (Stanford University and Mt. Sinai) to focus specifically on one cancer site, prostate cancer, and one increasingly common form of treatment, ADT. By narrowing the population of interest, this strategy may overcome some of the methodologic shortcomings underlying inconsistent findings associated with mixed disease sites and various treatment regimens in previous studies. A text-processing pipeline was used to extract information, including the International Classification of Diseases (9th revision) diagnostic codes, current procedural terminology codes, medication lists, and relevant data from clinical notes. This sophisticated data collection strategy represents a shift from relying on a cancer registry or self-reported data, which carries certain limitations, to instead using existing deidentified EMR data across entire medical centers. EMR data as a resource opens up a wealth of information on patient demographics, medication use, timing of new medications or procedures, treatment history, and history or onset of other medical conditions. This