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.

中文翻译：

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在认知档案中识别中风的模式识别：一项前瞻性队列研究的二级分析。

背景技术中风会在大脑中产生微妙的变化，可能会产生太小的症状而无法诊断。注意到缺乏诊断可能会使研究估计偏倚，本研究试图依靠对认知的系列评估来检查模式识别的效用，从而客观地识别类似于卒中的卒中样模式（认知模式卒中，p卒中）。方法使用健康与退休研究中未报告卒中史的参与者进行二级数据分析，该研究是一项大规模（n = 16,113）认知衰老的流行病学研究，年龄在50岁及以上的受访者中，1996年至2014年期间每两年一次测量发作性记忆分析仅限于参与者采用至少4种连续性情景记忆的方法。利用模式识别来识别p-中风事件的发生和日期，以识别与中风一致的逐步认知下降。描述性统计数据包括p卒中人群的百分比，导致卒中阳性的发作性记忆的平均变化以及p卒中与首次确诊的严重卒中之间的平均时间。统计分析将p卒中病例与报告的主要卒中病例进行了比较，这取决于接收者操作曲线（AUC）下的面积。纵向模型被用来检查人口统计学调整后有或没有重大卒中者的变化率。结果模式识别协议识别出7,499个未报告的p笔画。平均而言，p卒中的个体在推断的卒中时间的情景记忆中减少了1.986（SD = 0.023）个单词。最终的模式识别协议能够识别自我报告的主要卒中（AUC = 0.58，95％CI = 0.57-0.59，p <0.001）。在报告有严重中风的患者中，在首次报告诊断之前，平均可检测到p中风事件为4.963（4.650-5.275）年（p <0.001）。对中风的发生率为40.23 / 1,000（95％CI = 39.40-41.08）人年。调整性别后，年龄与p卒中和大卒中的发生率相似。结论这是首次提出利用模式识别来识别P卒中的发生率和时机的研究。有必要做进一步的工作来检查模式识别在识别纵向认知特征中的p-中风的临床实用性。58、95％CI = 0.57-0.59，p <0.001）。在报告有严重中风的患者中，在首次报告诊断之前，平均可检测到p中风事件为4.963（4.650-5.275）年（p <0.001）。对中风的发生率为40.23 / 1,000（95％CI = 39.40-41.08）人年。调整性别后，年龄与p卒中和大卒中的发生率相似。结论这是首次提出利用模式识别来识别P卒中的发生率和时机的研究。有必要做进一步的工作来检查模式识别在识别纵向认知特征中的p-中风的临床实用性。58、95％CI = 0.57-0.59，p <0.001）。在报告有严重中风的患者中，在首次报告诊断之前，平均可检测到p中风事件为4.963（4.650-5.275）年（p <0.001）。对中风的发生率为40.23 / 1,000（95％CI = 39.40-41.08）人年。调整性别后，年龄与p卒中和大卒中的发生率相似。结论这是首次提出利用模式识别来识别P卒中的发生率和时机的研究。有必要做进一步的工作来检查模式识别在识别纵向认知特征中的p-中风的临床实用性。对中风的发生率为40.23 / 1,000（95％CI = 39.40-41.08）人年。调整性别后，年龄与p卒中和大卒中的发生率相似。结论这是首次提出利用模式识别来识别P卒中的发生率和时机的研究。有必要做进一步的工作来检查模式识别在识别纵向认知特征中的p-中风的临床实用性。对中风的发生率为40.23 / 1,000（95％CI = 39.40-41.08）人年。调整性别后，年龄与p卒中和大卒中的发生率相似。结论这是首次提出利用模式识别来识别P卒中的发生率和时机的研究。有必要做进一步的工作来检查模式识别在识别纵向认知特征中的p-中风的临床实用性。