Classification Models for Early Identification of Persons at Risk for Dementia in Primary Care: An Evaluation in a Sample Aged 80 Years and Older (original) (raw)
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Classification models for early identification of persons at risk for dementia
Alzheimers & Dementia, 2009
Aim: To evaluate previously developed classification models to make implementation in primary care possible and aid early identification of persons at risk for dementia. Methods: Data were drawn from the OCTO-Twin study. At baseline, 521 persons 6 80 years of age were nondemented, and for 387 a blood sample was available. Predictors of dementia were collected and analyzed in initially nondemented persons using generalized estimating equations and Cox survival analyses. Results: In the basic model using predictors already known or easily obtained (basic set), the mean 2-year predictive value increased from 6.9 to 28.8% in persons with memory complaints and an MMSE score ^ 25. In the extended model, using both the basic set and an extended set of predictors requiring further assessment, the 8-year predictive value increased from 15.0 to 45.8% in persons with low cholesterol and an MMSE score ^ 24. Conclusion: Both models can contribute to an improved early identification of persons at risk for dementia in primary care.
Improving Prediction of Dementia in Primary Care
Annals of family medicine, 2018
The Mini-Mental State Examination (MMSE) is one of the most widely used instruments to screen for cognitive deficits; however, this instrument alone is not sensitive enough to detect early symptoms of dementia. We aimed to investigate whether additionally using the Visual Association Test (VAT) improves the predictive value of the MMSE score for development of dementia. Analyses were based on data from 2,690 primary care patients aged 70 to 78 years who participated in the Prevention of Dementia by Intensive Vascular Care (preDIVA) trial. We assessed change in the 30-point MMSE score over 2 years and the VAT score at 2 years-dichotomized as perfect (6 points) or imperfect (≤5 points)-and evaluated the predictive values of these tests for a diagnosis of dementia in the subsequent 4 to 6 years. Data were analyzed with logistic regression analysis. Patients having a decline of 2 points or more in total MMSE score over 2 years had an odds ratio of 3.55 (95% CI, 2.51-5.00) for developing...
Estimating Dementia Risk Using Multifactorial Prediction Models
JAMA network open, 2023
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 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).
Predicting dementia from primary care records: A systematic review and meta-analysis
PLOS ONE
Introduction Possible dementia is usually identified in primary care by general practitioners (GPs) who refer to specialists for diagnosis. Only two-thirds of dementia cases are currently recorded in primary care, so increasing the proportion of cases diagnosed is a strategic priority for the UK and internationally. Variables in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. We conducted a meta-analysis to identify clinical entities with potential for use in such a predictive model for dementia in primary care. Methods and findings We conducted a systematic search in PubMed, Web of Science and primary care database bibliographies. We included cohort or case-control studies which used routinely collected primary care data, to measure the association between any clinical entity and dementia. Meta-analyses were performed to pool odds ratios. A sensitivity analysis assessed the impact of non-independence of cases between studies. From a sift of 3836 papers, 20 studies, all European, were eligible for inclusion, comprising >1 million patients. 75 clinical entities were assessed as risk factors for all cause dementia, Alzheimer's (AD) and Vascular dementia (VaD). Data included were unexpectedly heterogeneous, and assumptions were made about definitions of clinical entities and timing as these were not all well described. Meta-analysis showed that neuropsychiatric symptoms including depression, anxiety, and seizures, cognitive symptoms, and history of stroke, were positively associated with dementia. Cardiovascular risk factors such as hypertension, heart disease, dyslipidaemia and diabetes were positively associated with VaD and negatively with AD. Sensitivity analyses showed similar results. Conclusions These findings are of potential value in guiding feature selection for a risk prediction tool for dementia in primary care. Limitations include findings being UK-focussed. Further predictive
Alzheimer's & dementia : the journal of the Alzheimer's Association, 2017
With a rapidly aging population, general practitioners are confronting the challenge of how to determine those who are at greatest risk for dementia and potentially need more specialized follow-up to mitigate symptoms early in its course. We created a practical dementia risk score and provided individualized estimates of future dementia risk. Using the Framingham Heart Study data, we built our prediction model using Cox proportional hazard models and developed a point system for the risk score and risk estimates. The score system used total points ranging from -1 to 31 and stratifies individuals into different levels of risk. We estimated 5-, 10-, and 20-year dementia risk prediction and incorporated these into the points system. This risk score system provides a practical tool because all included predictors are easy to assess by practitioners. It can be used to estimate future probabilities of dementia for individuals.
Development and validation of a brief dementia screening indicator for primary care
Alzheimer's & Dementia, 2014
Background: Detection of "any cognitive impairment" is mandated as part of the Medicare annual wellness visit, but screening all patients may result in excessive false positives. Methods: We developed and validated a brief Dementia Screening Indicator using data from four large, ongoing cohort studies (the Cardiovascular Health Study [CHS]; the Framingham Heart Study [FHS]; the Health and Retirement Study [HRS]; the Sacramento Area Latino Study on Aging [SALSA]) to help clinicians identify a subgroup of high-risk patients to target for cognitive screening.
Dementia Risk Prediction: Are We There Yet?
Clinics in Geriatric Medicine, 2010
Dementia is a term for memory impairment and loss of other intellectual abilities severe enough to cause interference with daily life. Alzheimer disease accounts for 50% to 70% of dementia cases. Other types of dementia include Lewy body dementia, vascular dementia, mixed dementia, and frontotemporal dementia. The precise cause of many dementias, including Alzheimer disease, is not known, but several risk factors that increase the risk for the development of dementia and several protective factors that may protect against the development of dementia are known. As more is understood about the relative importance of individual risk and protective factors, a dementia risk index (DRI) may be able to be developed in the future. Controlling modifiable risk and protective factors is advisable not only in patients with dementia but also in their at-risk family members.
A hierarchy of predictors for dementia-free survival in old-age: results of the AgeCoDe study
Acta Psychiatrica Scandinavica, 2013
for the AgeCoDe study group. A hierarchy of predictors for dementia-free survival in old-age: results of the AgeCoDe study. Objective: Progression from cognitive impairment (CI) to dementia is predicted by several factors, but their relative importance and interaction are unclear. Method: We investigated numerous such factors in the AgeCoDe study, a longitudinal study of general practice patients aged 75+. We used recursive partitioning analysis (RPA) to identify hierarchical patterns of baseline covariates that predicted dementia-free survival. Results: Among 784 non-demented patients with CI, 157 (20.0%) developed dementia over a follow-up interval of 4.5 years. RPA showed that more severe cognitive compromise, revealed by a Mini-Mental State Examination (MMSE) score < 27.47, was the strongest predictor of imminent dementia. Dementia-free survival time was shortest (mean 2.4 years) in such low-scoring patients who also had impaired instrumental activities of daily living (iADL) and subjective memory impairment with related worry (SMI-w). Patients with identical characteristics but without SMI-w had an estimated mean dementiafree survival time of 3.8 years, which was still shorter than in patients who had subthreshold MMSE scores but intact iADL (4.2-5.2 years). Conclusion: Hierarchical patterns of readily available covariates can predict dementia-free survival in older general practice patients with CI. Although less widely appreciated than other variables, iADL impairment appears to be an especially noteworthy predictor of progression to dementia.
A Summary Risk Score for the Prediction of Alzheimer Disease in Elderly Persons
Archives of Neurology, 2010
Patients: One thousand fifty-one Medicare recipients aged 65 years or older and residing in New York who were free of dementia or cognitive impairment at baseline. Main Outcome Measures: We separately explored the associations of several vascular risk factors with late-onset Alzheimer disease (LOAD) using Cox proportional hazards models to identify factors that would contribute to the risk score. Then we estimated the score values of each factor based on their  coefficients and created the LOAD vascular risk score by summing these individual scores. Results: Risk factors contributing to the risk score were age, sex, education, ethnicity, APOE ε4 genotype, history of diabetes, hypertension or smoking, high-density lipoprotein levels, and waist to hip ratio. The resulting risk score predicted dementia well. According to the vascular risk score quintiles, the risk to develop probable LOAD was 1.0 for persons with a score of 0 to 14 and increased 3.7-fold for persons with a score of 15 to 18, 3.6-fold for persons with a score of 19 to 22, 12.6-fold for persons with a score of 23 to 28, and 20.5-fold for persons with a score higher than 28.