Estimating Dementia Risk Using Multifactorial Prediction Models

被引:13
|
作者
Kivimaki, Mika [1 ,2 ]
Livingston, Gill [1 ]
Singh-Manoux, Archana [1 ,3 ]
Mars, Nina [4 ,5 ]
Lindbohm, Joni V. [1 ,2 ,5 ]
Pentti, Jaana [2 ]
Nyberg, Solja T. [2 ]
Pirinen, Matti [2 ,4 ,6 ]
Anderson, Emma L. [1 ,7 ]
Hingorani, Aroon D. [8 ]
Sipila, Pyry N. [2 ]
机构
[1] UCL, Dept Mental Hlth Older People, UCL Brain Sci, London, England
[2] Univ Helsinki, Clinicum, Fac Med, Helsinki, Finland
[3] Univ Paris Cite, U1153, Epidemiol Ageing & Neurodegenerat Dis, Inserm, Paris, France
[4] Univ Helsinki, Inst Mol Med, Helsinki, Finland
[5] Broad Inst & Harvard, Cambridge, MA USA
[6] Univ Helsinki, Dept Math & Stat, Helsinki, Finland
[7] Univ Bristol, MRC Integrat Epidemiol Unit & Populat Hlth Sci, Med Sch, Bristol, Avon, England
[8] UCL, Inst Cardiovasc Sci, London, England
基金
英国惠康基金; 芬兰科学院; 英国医学研究理事会;
关键词
DISEASE; COHORT;
D O I
10.1001/jamanetworkopen.2023.18132
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
IMPORTANCE The clinical value of current multifactorial algorithms for individualized assessment of dementia risk remains unclear. OBJECTIVE To evaluate the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk. DESIGN, SETTING, AND PARTICIPANTS 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. EXPOSURES 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). MAIN OUTCOMES AND MEASURES 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. RESULTS Of 465929 UK Biobank participants without dementia at baseline (mean [SD] age, 56.5 [8.1] years; range, 38-73 years; 252778 [54.3%] female participants), 3421 were diagnosed with dementia at follow-up (7.5 per 10000 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). CONCLUSIONS AND RELEVANCE 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.
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页数:13
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