Division of Population Health, NIHR School for Primary Care Research, Centre for Primary Care, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom.
Division of Population Health, Centre for Biostatistics, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom.
PLoS One. 2024 Oct 4;19(10):e0310712. doi: 10.1371/journal.pone.0310712. eCollection 2024.
Health policy in the UK and globally regarding dementia, emphasises prevention and risk reduction. These goals could be facilitated by automated assessment of dementia risk in primary care using routinely collected patient data. However, existing applicable tools are weak at identifying patients at high risk for dementia. We set out to develop improved risk prediction models deployable in primary care.
Electronic health records (EHRs) for patients aged 60-89 from 393 English general practices were extracted from the Clinical Practice Research Datalink (CPRD) GOLD database. 235 and 158 practices respectively were randomly assigned to development and validation cohorts. Separate dementia risk models were developed for patients aged 60-79 (development cohort n = 616,366; validation cohort n = 419,126) and 80-89 (n = 175,131 and n = 118,717). The outcome was incident dementia within 5 years and more than 60 evidence-based risk factors were evaluated. Risk models were developed and validated using multivariable Cox regression.
The age 60-79 development cohort included 10,841 incident cases of dementia (6.3 per 1,000 person-years) and the age 80-89 development cohort included 15,994 (40.2 per 1,000 person-years). Discrimination and calibration for the resulting age 60-79 model were good (Harrell's C 0.78 (95% CI: 0.78 to 0.79); Royston's D 1.74 (1.70 to 1.78); calibration slope 0.98 (0.96 to 1.01)), with 37% of patients in the top 1% of risk scores receiving a dementia diagnosis within 5 years. Fit statistics were lower for the age 80-89 model but dementia incidence was higher and 79% of those in the top 1% of risk scores subsequently developed dementia.
Our models can identify individuals at higher risk of dementia using routinely collected information from their primary care record, and outperform an existing EHR-based tool. Discriminative ability was greatest for those aged 60-79, but the model for those aged 80-89 may also be clinical useful.
英国和全球的卫生政策都强调痴呆症的预防和风险降低。这些目标可以通过使用常规收集的患者数据在初级保健中自动评估痴呆症风险来实现。然而，现有的适用工具在识别痴呆症高危患者方面能力较弱。我们着手开发可在初级保健中部署的改进风险预测模型。
从临床实践研究数据链接（CPRD）GOLD 数据库中提取了来自 393 家英格兰普通诊所的 60-89 岁患者的电子健康记录（EHR）。分别有 235 家和 158 家诊所被随机分配到开发和验证队列中。为 60-79 岁（开发队列 n = 616,366；验证队列 n = 419,126）和 80-89 岁（n = 175,131 和 n = 118,717）患者分别开发了痴呆症风险模型。结局是在 5 年内发生痴呆症，评估了超过 60 种基于证据的风险因素。使用多变量 Cox 回归开发和验证风险模型。
60-79 岁的开发队列包括 10841 例痴呆症发病病例（每 1000 人年 6.3 例），80-89 岁的开发队列包括 15994 例（每 1000 人年 40.2 例）。由此产生的 60-79 岁模型的区分度和校准效果良好（哈雷尔 C 0.78（95%CI：0.78-0.79）；罗伊斯顿 D 1.74（1.70-1.78）；校准斜率 0.98（0.96-1.01）），风险评分最高的 1%的患者中有 37%在 5 年内被诊断出痴呆症。80-89 岁模型的拟合统计数据较低，但痴呆症发病率较高，风险评分最高的 1%的患者中有 79%随后被诊断出痴呆症。
我们的模型可以使用常规收集的初级保健记录信息识别出痴呆症风险较高的个体，并且优于现有的基于电子健康记录的工具。对于 60-79 岁的人群，区分能力最强，但对于 80-89 岁的人群，该模型也可能具有临床意义。
