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

Abstract

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.

Keywords: Adaptive diagnostics; Cerebrovascular diseases; Neuroepidemiology; Pattern recognition.

Fig. 1

Theoretically differentiating expectations in healthy aging from cognitive declines consistent with cerebrovascular and ischemic diseases. Note that results from this study may help to identify when possible cerebrovascular events are insufficient to cause dementia as noted by the lack of crossover of the cerebrovascular and ischemic disease trajectory and the clinically diagnostic threshold.

Fig. 2

Differential pathological characterization based on aging pattern in episodic memory. Average curves were calculated using results shown in online supplementary Table S1 (model 1).