Dataset Information

Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia.

ABSTRACT: Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Since changes in the brains of affected individuals could be evidenced as early as 10 years before the onset of symptoms, prognosis research should consider this time frame. This study investigates a broad decision tree multifactorial approach for the prediction of dementia, considering 75 variables regarding demographic, social, lifestyle, medical history, biochemical tests, physical examination, psychological assessment and health instruments. Previous work on dementia prognoses with machine learning did not consider a broad range of factors in a large time frame. The proposed approach investigated predictive factors for dementia and possible prognostic subgroups. This study used data from the ongoing multipurpose Swedish National Study on Aging and Care, consisting of 726 subjects (91 presented dementia diagnosis in 10 years). The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Most of the variables selected by the tree are related to modifiable risk factors; physical strength was important across all ages. Also, there was a lack of variables related to health instruments routinely used for the dementia diagnosis.

SUBMITTER: Dallora AL

PROVIDER: S-EPMC7557767 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia.

Dallora Ana Luiza AL Minku Leandro L Mendes Emilia E Rennemark Mikael M Anderberg Peter P Sanmartin Berglund Johan J

International journal of environmental research and public health 20200914 18

Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Since changes in the brains of affected individuals could be evidenced as early as 10 years before the onset of symptoms, prognosis research should consider this time frame. This study investigates a broad decision tree multifactorial approach for the prediction of dementia, considering 75 variables regarding demographic, social, lifestyle, medical hi ...[more]

PMID: 32937765

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Project description:ImportanceThe clinical value of current multifactorial algorithms for individualized assessment of dementia risk remains unclear.ObjectiveTo evaluate the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk.Design, setting, and participantsThis 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.ExposuresFour 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 measuresDementia 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.ResultsOf 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).Conclusions and relevanceIn 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.

Dataset Information

Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia.

Publications

Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia.

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