Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China.
Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China.
JAMA Netw Open. 2022 Nov 1;5(11):e2242596. doi: 10.1001/jamanetworkopen.2022.42596.
Although researchers have devoted substantial efforts, money, and time to studying the causes of dementia and the means to prevent it, no effective treatment exists yet. Identifying preclinical risk factors of dementia could help prevent or delay its progression.
To develop a point risk score prediction model of dementia.
DESIGN, SETTING, AND PARTICIPANTS: This study used a large UK population-based prospective cohort study conducted between March 13, 2006, and October 1, 2010. Data analysis was performed from June 7 to September 15, 2021. Individual analyses of time end points were concluded at the first dementia diagnosis during the follow-up period. The data were split into training and testing data sets to separately establish and validate a prediction model.
Outcomes of interest included 5-, 9-, and 13-year dementia risk. Least absolute shrinkage and selection operator and multivariate Cox proportional hazards regression models were used to identify available and practical dementia predictors. A point risk score model was developed for the individual prediction of 5-, 9-, and 13-year dementia risk.
A total of 502 505 participants were selected; the population after exclusions for missing data and dementia diagnosis at baseline was 444 695 (205 187 men; mean [SD] age, 56.74 [8.18] years; 239 508 women; mean [SD] age, 56.20 [8.01] years). Dementia occurrence during the 13 years of follow-up was 0.7% for men and 0.5% for women. The C statistic of the final multivariate Cox proportional hazards regression model was 0.86 for men and 0.85 for women in the training data set, and 0.85 for men and 0.87 for women in the testing data set. Men and women shared some modifiable risk and protective factors, but they also presented independent risk factors that accounted for 31.7% of men developing dementia and 53.35% of women developing dementia according to the weighted population-attributable fraction. The total point score of the risk score model ranged from -18 to 30 in men and -17 to 30 in women. The risk score model yielded nearly 100% prediction accuracy of 13-year dementia risk both in men and women.
In this diagnostic study, a practical risk score tool was developed for individual prediction of dementia risk, which may help individuals identify their potential risk profile and provide guidance on precise and timely actions to promote dementia delay or prevention.
尽管研究人员已经投入了大量的精力、资金和时间来研究痴呆症的病因和预防方法，但目前仍没有有效的治疗方法。识别痴呆症的临床前风险因素可能有助于预防或延缓其进展。
开发痴呆症的点风险评分预测模型。
设计、地点和参与者：本研究使用了一项大型的英国基于人群的前瞻性队列研究，该研究于 2006 年 3 月 13 日至 2010 年 10 月 1 日进行。数据分析于 2021 年 6 月 7 日至 9 月 15 日进行。对每个时间终点的个体分析在随访期间首次诊断痴呆症时结束。数据被分为训练和测试数据集，以分别建立和验证预测模型。
感兴趣的结果包括 5 年、9 年和 13 年的痴呆风险。最小绝对收缩和选择算子和多变量 Cox 比例风险回归模型被用来识别可用的和实用的痴呆预测因素。为个体预测 5 年、9 年和 13 年的痴呆风险，建立了一个点风险评分模型。
共选择了 502505 名参与者；排除缺失数据和基线时痴呆诊断后，人群为 444695 人（男性 205187 人；平均[SD]年龄 56.74[8.18]岁；女性 239508 人；平均[SD]年龄 56.20[8.01]岁）。在 13 年的随访期间，男性的痴呆发生率为 0.7%，女性为 0.5%。在训练数据集中，最终多变量 Cox 比例风险回归模型的 C 统计量为男性 0.86，女性 0.85，在测试数据集中，男性为 0.85，女性为 0.87。男性和女性有一些可改变的风险和保护因素，但也有独立的风险因素，根据加权人群归因分数，男性有 31.7%的人患痴呆症，女性有 53.35%的人患痴呆症。风险评分模型的总评分范围在男性为-18 到 30，在女性为-17 到 30。该风险评分模型在男性和女性中对 13 年痴呆风险的预测准确率均接近 100%。
在这项诊断研究中，开发了一种实用的风险评分工具，用于个体预测痴呆风险，这可能有助于个体识别其潜在的风险状况，并为精确和及时的行动提供指导，以促进痴呆症的延迟或预防。
