Corewell Health and Michigan State University researchers are of the first in the state to use de-identified electronic health records of more than 1.5 million patients to analyze incidence rates and risk factors of mild cognitive impairment, or MCI, in rural and urban areas in West Michigan.
Results showed that many cases could be going undetected among those living in rural communities in the area, and researchers now will use the findings to develop AI tools that can detect MCI earlier among patients across the country.
The retrospective study, which included 10 years of historical patient data, is now published in the journal Alzheimer's & Dementia: Translational Research & Clinical Interventions and is of the first large-scale analyses representing most of the population of West Michigan, with some of its findings surprising study authors.
The study found that patients who progressed directly to dementia without a prior MCI diagnosis, also referred to in the study as MCI skippers, were three times more prevalent than those identified with MCI initially.
David Chesla, co-principal investigator and senior director of research data management at Corewell Health Research Institute in Grand Rapids, Michigan, said that this underreporting is what may be causing the MCI incidence rates to be so much lower.
“Our hypothesis from the beginning of this work was that we would have underreporting of cognitive impairment in communities across West Michigan; we just didn’t know to what extent,” Chesla said. “Our suspicion was initially derived from national data that reports a growing incidence rate of MCI within our aging U.S. population. Our patient data mirrors a subset of the national data; however, our patient MCI incidence rate in West Michigan is significantly lower than national averages.”
National averages can range from 10% to 18% depending on race, age and timeframe in which the data was collected.
Chesla also indicated that the research team decided to dive deeper into the geographic distribution of patients, allowing them to separate whether patients had an urban or rural location, something he said has not been done before. Doing this provided further evidence that potential underreporting exists with the ratio of MCI skippers to diagnosed MCI cases being 4.3 times higher in rural areas compared to 2.8 times in urban areas.
While lack of access to care in these communities along with other reasons could be driving the higher rate of underreporting, Chesla said that a limitation of the study was having to use information from 10 years ago when electronic record systems were in their earlier stages.
Additional findings showed that while risk factors for MCI were similar between the rural and urban populations, the urban areas exhibited a larger array of risks including being African American as well as having hearing loss, inflammatory bowel disease, obstructive sleep apnea and insomnia. Most common risk factors of MCI include diabetes, stroke, Parkinson’s disease and older age.
According to the researchers, the massive amount of data now gives them the ability to leverage artificial intelligence, or AI, to build high-performance machine learning models that can identify higher-risk patients earlier across the state and potentially across the country. It has been shown that early diagnosis is key to potentially reversing or delaying progression of cognitive impairment.
