Background:Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.Aims:To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.Methods:Cohort study that included primary care patients from two academic medical centers. Patients were 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort). Predictors of PSCI were ascertained from the electronic health record. The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.Results:The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.Conclusions:Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts. Data access statement: Mass General Brigham data contains protected health information and cannot be shared publicly. The data processing scripts used to perform analyses will be made available to interested researchers upon reasonable request to the corresponding author.

中文翻译：

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使用电子健康记录数据预测中风后认知障碍

背景：早期识别高风险个体可以帮助减少中风后认知障碍（PSCI）的二级预防干预措施，这些人将从危险因素修正中受益。目的：开发和评估预测模型来识别风险增加的患者使用从电子健康记录中轻松获取的数据，对 PSCI 进行了超过 5 年的研究。方法：队列研究包括来自两个学术医疗中心的初级保健患者。患者年龄为 45 岁或以上，没有既往卒中或普遍的认知障碍，在 2003 年至 2016 年（开发/内部验证队列）或 2010 年至 2022 年（外部验证队列）期间接受过初级保健并发生过缺血性卒中。 PSCI 的预测因素是从电子健康记录中确定的。结果是中风后 5 年内和开始的 3 个月内发生痴呆/认知障碍，使用 ICD-9/10 代码确定。对于模型变量选择，我们考虑了 PSCI 的潜在预测因素，并构建了 400 个引导样本，其中三分之二为模型推导样本。我们使用最小绝对收缩和选择算子 (LASSO) 惩罚运行了 10 倍交叉验证的 Cox 比例风险模型。包括在 > 25% 的样本中选择的变量。 结果：分析包括发展队列中的 332 例 PSCI 事件诊断 (n = 3,741)，以及内部 (n = 1,925) 和外部 (n =第2,237章）验证队列。预测 PSCI 的 c 统计量在内部验证队列中为 0.731 (95% CI: 0.694-0.768)，在外部验证队列中为 0.724 (95% CI: 0.681-0.766)。基于开发队列中预测因子的 beta 系数的风险评分将患者分为低风险组（0-7 分）、中风险组（8-11 分）和高风险组（12-35 分）。内部（高，HR：6.2，95% CI：4.1-9.3；中，HR 2.7，95% CI：1.8-4.1）和外部（高，HR：6.1， 95% CI：3.9-9.6；中级，HR 2.8，95% CI：1.9-4.3）验证队列。结论：使用常规收集的数据可以准确预测 PSCI 的五年风险。模型输出可用于风险分层并识别 PSCI 风险较高的个人，以采取预防措施。数据访问声明：麻省总医院数据包含受保护的健康信息，不能公开共享。用于执行分析的数据处理脚本将根据相应作者的合理要求提供给感兴趣的研究人员。