Estimating Dementia Risk Using Multifactorial Prediction Models.
Estimating Dementia Risk Using Multifactorial Prediction Models.
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DOI：
10.1001/jamanetworkopen.2023.18132
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发表时间：
2023-06-01
影响因子：
13.8
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中科院分区：
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目前的多因素算法在评估10年痴呆风险方面的临床价值是什么？在这项队列研究中，包括来自英国生物库的465名 929参与者，4个广泛使用的风险评分(心血管风险因素、衰老和痴呆[CAIDE-临床]、CAIDE-APOE补充、简要痴呆症筛查指标[BDSI]和澳大利亚国立大学阿尔茨海默病风险指数[ANU-ADRI])在将阳性测试结果的阈值校准为假阳性率达到5%时，错过了84%至91%的事件痴呆症参与者，并检测到至少一半的事件痴呆症参与者，真与假阳性的比率超过1：66。这些数字更好，在仅基于年龄估计痴呆症风险时，84%的痴呆症事件被遗漏或真假阳性比为1：43。这些发现表明，目前的风险评分在估计10年痴呆症风险方面的临床实用价值有限。这项队列研究评估了4个广泛使用的痴呆症风险评分在英国生物库参与者中评估10年痴呆症风险的临床价值。目前用于痴呆风险个体化评估的多因素算法的临床价值尚不清楚。评价4种广泛使用的痴呆风险评分在估计10年痴呆风险中的临床价值。这项基于人群的前瞻性英国生物库队列研究评估了基线(2006-2010年)的4个痴呆症风险评分，并确定了随后10年的痴呆症事件。复制和20年的跟踪调查是基于英国白厅II的研究。在这两项分析中，包括在基线时没有痴呆症、至少有1个痴呆症风险评分的完整数据，以及与住院或死亡率的电子健康记录相关联的参与者。数据分析时间为2022年7月5日至2023年4月20日。现有的四个痴呆症风险评分：心血管风险因素、衰老和痴呆(CAIDE)-临床评分、CAIDE-APOE补充评分、简明痴呆症筛查指标(BDSI)和澳大利亚国立大学阿尔茨海默病风险指数(ANU-ADRI)。痴呆症是从链接的电子健康记录中确定的。为了评估每个分数预测痴呆症10年风险的准确性，计算了每个风险分数和一个单独包括年龄的模型的一致性(C)统计数据、检测率、假阳性率和真假阳性比。在465名 929名无痴呆的英国生物库参与者中(平均年龄56.5[8.1]岁；范围38-73岁；252名 778[54.3%]女性参与者)中，3421名在随访时被诊断为痴呆症(每10,000 人年7.5人)。如果将阳性测试结果的阈值校准为5%的假阳性率，则所有4个风险分数都检测到9%至16%的痴呆症事件，因此错过了84%至91%(失败率)。对于仅包括年龄的模型，相应的失败率为84%。对于被校准为检测至少一半未来事件痴呆症的阳性测试结果，真阳性与假阳性的比率在1至66(对于添加了CAIDE-APOE的患者)和1与116(对于ANU-ADRI)之间。仅就年龄而言，这一比例为1：43。CAIDE临床版为0.66(95%CI，0.65~0.67)，CAIDE-APOE补充版为0.73(95%CI，0.72~0.73)，BDSI为0.68(95%CI，0.67~0.69)，ANU-ADRI为0.59(95%CI，0.58~0.60)，单独年龄组为0.79(95%CI，0.79~0.80)。在白厅II研究队列中，包括4865名参与者(平均年龄54.9[5.9]岁；1342名[27.6%]女性参与者)，20年痴呆症风险也有类似的C统计数据。在对65岁(±1)岁的同龄参与者进行的分组分析中，风险分数的区分能力很低(C统计介于0.52和0.60之间)。在这些队列研究中，使用现有风险预测分数对痴呆症风险进行个性化评估的错误率很高。这些发现表明，这些分数在针对痴呆症预防人群方面的价值有限。需要进一步的研究来开发更准确的痴呆症风险估计算法。
What is the clinical value associated with current multifactorial algorithms in estimating 10-year dementia risk? In this cohort study including 465 929 participants from the UK Biobank, 4 widely-used risk scores (Cardiovascular Risk Factors, Ageing and Dementia [CAIDE-Clinical], CAIDE–APOE-supplemented, Brief Dementia Screening Indicator [BDSI], and Australian National University Alzheimer Disease Risk Index [ANU-ADRI]) missed 84% to 91% of participants with incident dementia when the threshold for a positive test result was calibrated to achieve a 5% false-positive rate, and to detect at least half of participants with incident dementia, the ratio of true to false positives exceeded 1 to 66. These numbers were better, with 84% of incident dementia missed or a true to false positives ratio of 1 to 43, when estimating dementia risk based on age alone. These findings suggest that current risk scores have limited clinical utility for estimation of 10-year dementia risk. This cohort study evaluates the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk among participants in the UK Biobank. The clinical value of current multifactorial algorithms for individualized assessment of dementia risk remains unclear. To evaluate the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk. This prospective population-based UK Biobank cohort study assessed 4 dementia risk scores at baseline (2006-2010) and ascertained incident dementia during the following 10 years. Replication with a 20-year follow-up was based on the British Whitehall II study. For both analyses, participants who had no dementia at baseline, had complete data on at least 1 dementia risk score, and were linked to electronic health records from hospitalizations or mortality were included. Data analysis was conducted from July 5, 2022, to April 20, 2023. Four existing dementia risk scores: the Cardiovascular Risk Factors, Aging and Dementia (CAIDE)-Clinical score, the CAIDE–APOE-supplemented score, the Brief Dementia Screening Indicator (BDSI), and the Australian National University Alzheimer Disease Risk Index (ANU-ADRI). Dementia was ascertained from linked electronic health records. To evaluate how well each score predicted the 10-year risk of dementia, concordance (C) statistics, detection rate, false-positive rate, and the ratio of true to false positives were calculated for each risk score and for a model including age alone. Of 465 929 UK Biobank participants without dementia at baseline (mean [SD] age, 56.5 [8.1] years; range, 38-73 years; 252 778 [54.3%] female participants), 3421 were diagnosed with dementia at follow-up (7.5 per 10 000 person-years). If the threshold for a positive test result was calibrated to achieve a 5% false-positive rate, all 4 risk scores detected 9% to 16% of incident dementia and therefore missed 84% to 91% (failure rate). The corresponding failure rate was 84% for a model that included age only. For a positive test result calibrated to detect at least half of future incident dementia, the ratio of true to false positives ranged between 1 to 66 (for CAIDE–APOE-supplemented) and 1 to 116 (for ANU-ADRI). For age alone, the ratio was 1 to 43. The C statistic was 0.66 (95% CI, 0.65-0.67) for the CAIDE clinical version, 0.73 (95% CI, 0.72-0.73) for the CAIDE–APOE-supplemented, 0.68 (95% CI, 0.67-0.69) for BDSI, 0.59 (95% CI, 0.58-0.60) for ANU-ADRI, and 0.79 (95% CI, 0.79-0.80) for age alone. Similar C statistics were seen for 20-year dementia risk in the Whitehall II study cohort, which included 4865 participants (mean [SD] age, 54.9 [5.9] years; 1342 [27.6%] female participants). In a subgroup analysis of same-aged participants aged 65 (±1) years, discriminatory capacity of risk scores was low (C statistics between 0.52 and 0.60). In these cohort studies, individualized assessments of dementia risk using existing risk prediction scores had high error rates. These findings suggest that the scores were of limited value in targeting people for dementia prevention. Further research is needed to develop more accurate algorithms for estimation of dementia risk.