Department of Neurology, Tufts Medical Center, Boston, MA, USA.
Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Mayo Clin Proc. 2022 Jun;97(6):1114-1122. doi: 10.1016/j.mayocp.2021.11.038. Epub 2022 Apr 27.
To assess the frequency of silent brain infarcts (SBIs) and white matter disease (WMD) and associations with stroke risk factors (RFs) in a real-world population.
This was an observational study of patients 50 years or older in the Kaiser Permanente Southern California health system from January 1, 2009, through June 30, 2019, with head computed tomography or magnetic resonance imaging for nonstroke indications and no history of stroke, transient ischemic attack, or dementia. A natural language processing (NLP) algorithm was applied to the electronic health record to identify individuals with reported SBIs or WMD. Multivariable Poisson regression estimated risk ratios of demographic characteristics, RFs, and scan modality on the presence of SBIs or WMD.
Among 262,875 individuals, the NLP identified 13,154 (5.0%) with SBIs and 78,330 (29.8%) with WMD. Stroke RFs were highly prevalent. Advanced age was strongly associated with increased risk of SBIs (adjusted relative risks [aRRs], 1.90, 3.23, and 4.72 for those aged in their 60s, 70s, and ≥80s compared with those in their 50s) and increased risk of WMD (aRRs, 1.79, 3.02, and 4.53 for those aged in their 60s, 70s, and ≥80s compared with those in their 50s). Magnetic resonance imaging was associated with a reduced risk of SBIs (aRR, 0.87; 95% CI, 0.83 to 0.91) and an increased risk of WMD (aRR, 2.86; 95% CI, 2.83 to 2.90). Stroke RFs had modest associations with increased risk of SBIs or WMD.
An NLP algorithm can identify a large cohort of patients with incidentally discovered SBIs and WMD. Advanced age is strongly associated with incidentally discovered SBIs and WMD.
评估真实人群中无症状性脑梗死 (SBI) 和脑白质疾病 (WMD) 的发生频率及其与中风危险因素 (RFs) 的关联。
这是一项观察性研究，纳入 2009 年 1 月 1 日至 2019 年 6 月 30 日期间，在 Kaiser Permanente Southern California 医疗系统就诊的年龄 50 岁及以上、因非中风原因接受头部计算机断层扫描或磁共振成像且无中风、短暂性脑缺血发作或痴呆病史的患者。采用自然语言处理 (NLP) 算法对电子病历进行分析，以确定报告有 SBI 或 WMD 的个体。多变量泊松回归估计了人口统计学特征、RFs 和扫描方式对 SBI 或 WMD 存在的风险比。
在 262875 名患者中，NLP 识别出 13154 例（5.0%）有 SBI，78330 例（29.8%）有 WMD。中风 RFs 非常普遍。年龄较大与 SBI（校正后相对风险 [aRR]，60 多岁、70 多岁和 80 岁以上人群分别为 1.90、3.23 和 4.72，与 50 多岁人群相比）和 WMD（aRR，60 多岁、70 多岁和 80 岁以上人群分别为 1.79、3.02 和 4.53，与 50 多岁人群相比）的风险增加有关。磁共振成像与 SBI（aRR，0.87；95%CI，0.83 至 0.91）风险降低和 WMD（aRR，2.86；95%CI，2.83 至 2.90）风险增加有关。中风 RFs 与 SBI 或 WMD 风险增加的相关性较小。
NLP 算法可识别出大量偶然发现的 SBI 和 WMD 患者。年龄较大与偶然发现的 SBI 和 WMD 密切相关。
