Pattern Recognition to Identify Stroke in the Cognitive Profile: Secondary Analyses of a Prospective Cohort Study

TitlePattern Recognition to Identify Stroke in the Cognitive Profile: Secondary Analyses of a Prospective Cohort Study
Publication TypeJournal Article
Year of Publication2019
AuthorsClouston, SAP, Zhang, Y, Smith, DM
JournalCerebrovascular Diseases Extra
KeywordsCognition, Stroke

Background: Stroke can produce subtle changes in the brain that may produce symptoms that are too small to lead to a diagnosis. Noting that a lack of diagnosis may bias research estimates, the current study sought to examine the utility of pattern recognition relying on serial assessments of cognition to objectively identify stroke-like patterns of cognitive decline (pattern-detected stroke, p-stroke).

Methods: Secondary data analysis was conducted using participants with no reported history of stroke in the Health and Retirement Study, a large (n = 16,113) epidemiological study of cognitive aging among respondents aged 50 years and older that measured episodic memory consistently biennially between 1996 and 2014. Analyses were limited to participants with at least 4 serial measures of episodic memory. Occurrence and date of p-stroke events were identified utilizing pattern recognition to identify stepwise declines in cognition consistent with stroke. Descriptive statistics included the percentage of the population with p-stroke, the mean change in episodic memory resulting in stroke-positive testing, and the mean time between p-stroke and first major diagnosed stroke. Statistical analyses comparing cases of p-stroke with reported major stroke relied on the area under the receiver-operating curve (AUC). Longitudinal modeling was utilized to examine rates of change in those with/without major stroke after adjusting for demographics.

Results: The pattern recognition protocol identified 7,499 p-strokes that went unreported. On average, individuals with p-stroke declined in episodic memory by 1.986 (SD = 0.023) words at the inferred time of stroke. The resulting pattern recognition protocol was able to identify self-reported major stroke (AUC = 0.58, 95% CI = 0.57-0.59, p < 0.001). In those with a reported major stroke, p-stroke events were detectable on average 4.963 (4.650-5.275) years (p < 0.001) before diagnosis was first reported. The incidence of p-stroke was 40.23/1,000 (95% CI = 39.40-41.08) person-years. After adjusting for sex, age was associated with the incidence of p-stroke and major stroke at similar rates.

Conclusions: This is the first study to propose utilizing pattern recognition to identify the incidence and timing of p-stroke. Further work is warranted examining the clinical utility of pattern recognition in identifying p-stroke in longitudinal cognitive profiles. © 2019 The Author(s) Published by S. Karger AG, Basel.


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Citation KeyClouston2019114