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Usefulness of data from magnetic resonance imaging to improve prediction of dementia

AbstractObjective To determine whether the addition of data derived from magnetic resonance imaging (MRI) of the brain to a model incorporating conventional risk variables improves prediction of dementia over 10 years of follow up.Results During 10 years of follow up, there were 119 confirmed cases of dementia, 84 of which were Alzheimer's disease. The conventional risk model incorporated age, sex, education, cognition, physical function, lifestyle (smoking, alcohol use), health (cardiovascular disease, diabetes, systolic blood pressure), and the apolipoprotein genotype (C statistic for discrimination performance was 0.77, 95% confidence interval 0.71 to 0.82). No significant differences were observed in the discrimination performance of the conventional risk model compared with models incorporating data from MRI including white matter lesion volume (C statistic 0.77, 95% confidence interval 0.72 to 0.82; P=0.48 for difference of C statistics), brain volume (0.77, 0.72 to 0.82; P=0.60), hippocampal volume (0.79, 0.74 to 0.84; P=0.07), or all three variables combined (0.79, 0.75 to 0.84; P=0.05). Inclusion of hippocampal volume or all three MRI variables combined in the conventional model did, however, lead to significant improvement in reclassification measured by using the integrated discrimination improvement index (P=0.03 and P=0.04) and showed increased net benefit in decision curve analysis. Despite the lack of an effective treatment for Alzheimer's disease, it is estimated that a two year delay in onset could have a dramatic effect on its prevalence, reducing incidence by about 20%.2 Risk assessment for future disease to better focus intervention to those at highest risk and reduce the cost of unnecessary diagnostics is therefore a major issue, and it has been the aim of many recent studies.3 4 5 6 7 In that regard, the development of a simple accurate method for prediction of risk of dementia is a priority.Having an accurate model for predicting future dementia in population based settings would be beneficial for several reasons. Firstly, targeting whole bvlgari necklace men imitation populations for modification of behaviour and reduction of risk factors might not always be cost effective, particularly when intervention strategies are costly or adherence rates low. Secondly, broad based targeting strategies are not always recommended for example, when there are safety concerns or a high risk of side effects of treatment. A complementary approach could be to target high risk individuals by developing a model to accurately identify these individuals as early as possible without being too broad in risk selection. These individuals could then be referred for services, improved care, clinical trials, and, when intervention is available, stratified or individualised risk factor reduction to ultimately improve patient outcomes. In contrast, people at low risk could be excluded from further immediate follow up thereby reducing costs, for example, of unnecessary diagnostics.While ageing is the most universally accepted risk factor for dementia, other conventional risk factors have been incorporated into prediction models developed in populations aged 65, including poor neuropsychological test performance, subjective memory complaint, low educational attainment, sex, depression, history of cardiovascular (such as coronary heart disease, peripheral vascular disease), cerebrovascular (such as stroke), and metabolic (such as diabetes) diseases and their risk factors (such as hypertension, smoking, alcohol use, physical inactivity, obesity), blood based biomarkers (serum total cholesterol concentration), inability to perform activities of daily living (such manage money and drugs), and genetic susceptibility (such as apolipoprotein e4 status).8 9 10 11 12 13 14 15 16 17 18 19 Non traditional risk factors (such as denture fit and eye and ear trouble) have also been used.20 21 Predictive accuracy of current models has generally been low to moderate.7Improvement in dementia risk prediction is needed for medical and research purposes to enhance diagnostic protocols (such as recruitment into clinical trials) and inform therapeutic decisions (such as personalised medicine). This could be achieved through the use of indicators of dementia derived from magnetic resonance imaging (MRI), including structural changes (such as hippocampal atrophy, medial temporal lobe atrophy, and bvlgari onyx necklace replica evidence of white matter disease) and functional changes (such as positron emission tomography imaging of amyloidosis and tauopathy), in addition to assessment of cerebral spinal fluid (such as amyloid 42 and tau). Variables derived from both cerebral spinal fluid analysis and MRI have been proposed for stratification of patients for research purposes under the new "lexicon" of Alzheimer's disease.22 23 24 The immediate implications of using such complex biomarkers are that they require technologically advanced, costly, burdensome (for participants as they can cause discomfort), and not easily available methods, especially in developing countries. This might offset any advantage of the use of such variables in predictive models. To make recommendations on the use of data from MRI in dementia risk prediction in population based settings, we need evidence on what this adds to more conventionally derived risk models.We evaluated the value of markers from MRI added to a model incorporating previously proposed conventional risk factors for the prediction of all cause dementia and Alzheimer's disease over 10 years' follow up in a large prospective population based cohort study.MethodsSampleThe Three City Study is a multi centre longitudinal population based cohort study, conducted in three French cities (Bordeaux, Dijon, and Montpellier), and designed to estimate the risk of dementia and cognitive impairment attributable to vascular factors. Full details of the methods and baseline characteristics of the participants have been published previously.25The current study is solely based on the Dijon centre, the only centre in which a cerebral MRI was conducted. In brief, at the 1999 French census, the total population of Dijon was 153800.26 To be eligible for recruitment a person had to be living in Dijon or its suburbs, registered on the electoral roll, aged 65, and not be living in an institution. Baseline interviews were undertaken in 1999 2001, with follow up interviews conducted about two, four, six, and 10 years after enrolment.From the original 4931 participants enrolled in Dijon, MRI was offered to those aged 65 80 who had been enrolled between June 1999 and September 2000. Although the consent rate for scanning was 83%, scans were obtained from 1923 participants (39%), as funding restrictions precluded MRI for everyone. From these 1923 participants we excluded from analysis 123 individuals with missing MRI variables (such as poor scan quality and artefacts), eight with prevalent dementia, and individuals with missing dementia status over the 10 years of follow up (n=71 participants were seen only at baseline). The remaining sample included 1721 individuals. Follow up time ranged from 0.6 to 10.6 years (mean 7.3 years, SD 2.3 years).Comparison of the baseline characteristics of our analytical sample with all remaining age eligible participants without dementia in Dijon is shown in appendix 1. Individuals excluded because of missing dementia status at follow up did not differ from those included with regard to sex (2=0.53, df=1, P=0.47), age (F1,1790=0.97, P=0.97), or educational attainment (2=1.92, df=1, P=0.38). Individuals without known dementia status over follow up, however, performed significantly worse on the mini mental state examination at baseline: median score 28 (interquartile range 27 29) for included v 27 (26 29) for excluded; Wilcoxon Mann Whitney test: z=2.42, P=0.02.Patient involvementThere was no patient involvement in the design, conduct, and interpretation of the study.Baseline assessmentsTrained psychologists collected data with a standardised questionnaire during a face to face interview at the participants' home. Information included sociodemographic status, lifestyle, medical history, drug use, and assessment of cognitive and functional status. Clinical examination included measurements of blood pressure with a digital tensiometer (OMRON M4). Anthropometric measures included height and weight. Fasting bloods samples were taken and markers (such as cholesterol, glucose) measured at a single laboratory.Magnetic resonance imagingBrain MRI scanning was undertaken on average of 4.2 months (SD 3.0 months) after the baseline examination. Scanning was completed with a 1.5 Tesla Magnetom (Siemens, Erlangen, Germany). Usual MRI exclusions were applied. The scanning sequence and data extraction methods have been described in detail previously.27 28 In brief, raw data were converted to the ACR NEMA standard format and then transformed for analysis and storage at the Department of Neurofunctional Imaging, Caen.25 This centre developed fully automatic image processing software for tissue segmentation and to detect and quantify white matter lesions.27 29 Automated imaging processing was also used to study brain volume (white matter, grey matter, and ventricles). Total intracranial volume by summing grey matter, white matter, and cerebral spinal fluid volumes were computed with voxel based morphometry techniques.We selected three MRI measures for analysis including white matter lesion volume (calculated by summing the volumes of all white matter lesions detected), hippocampal volume (combining left and right sides), and total brain volume (defined as the sum of grey and white matter) as these are commonly assessed and have been previously associated with cognitive decline and dementia.30 31 32 33 All three MRI variables were normalised to total intracranial volume and converted to a percentage that is, each volume (white matter lesion, hippocampal, and whole brain) was divided by total intracranial volume and multiplied by 100.Both the total brain volume and hippocampal volume variables were normally distributed. In contrast, white matter lesion volume had a markedly skewed distribution and therefore scores were log transformed before analysis to decrease the impact of extreme observations.Diagnosis of dementia Diagnosis of dementia was established with a three phase procedure. All participants were first screened with scores from the mini mental state examination34 (with education adjusted cut off points) and the Isaac set test.35 In Dijon, in the second phase, a neurologist saw individuals with suspected incident dementia based on their performance on neuropsychology tests. In the third phase, a panel of independent neurologists reviewed all potential prevalent and incident cases to obtain consensus on diagnosis and aetiology according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition.36 With regard to subtypes of dementia, Alzheimer's disease (possible and probable) was diagnosed according to criteria from the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS ADRDA), and vascular dementia was based on history of vascular disease, Hachinski score, and MRI (whenever possible).25 37Potential predictorsInformation on prognostic determinants of occurrence of dementia was extracted from the published literature on dementia risk prediction and previous findings from the Three City Study. We selected 13 variables. Sociodemographic factors included age, sex, and educational attainment. Lifestyle factors included smoking and alcohol use. Functional assessment was measured with the Lawton and Brody scale for instrumental activities of daily living38 that assesses ability to use the telephone, responsibility for drug treatment, managing money, mobility, shopping, grooming, housework, and laundry (the last three were asked in women only). Cognition was assessed with the mini mental state examination, Benton visual retention test,39 and the digit span test.40 Appendix 2 shows the adjusted cut off scores for cognitive impairment adjusted for age and education. Health variables included cardiovascular events (combining self reported history of myocardial infarction, coronary surgery, coronary angioplasty, surgery of the arteries in the legs, or stroke requiring admission to hospital), metabolic disease (diabetes; self reported, high glucose concentration 7.0 mmol/L, or receipt of hypoglycaemic treatment including oral diabetic drugs or bvlgari necklace for sale imitation insulin), and systolic blood pressure (continuous). Genetic risk assessed apolipoprotein e4 status (coded as e4 positive v e4 negative).Missing covariate informationOf the 1721 participants included in the analysis, few had missing information on covariates (Statistical analysesWe tested differences in demographics between the groups with and without dementia using 2 test (for categorical variables), analysis of variance (for continuous normally distributed variables), or the Wilcoxon Mann Whitney test (for continuous, non normally distributed variables). Data were censored at first diagnosis of dementia (for dementia cases) or last follow up interview (for those without dementia).A multivariable model incorporating all 13 conventional risk variables was calculated with Cox proportional hazards regression analysis. To test whether MRI data improve discrimination performance of this model, we performed Cox regression analyses with inclusion of each of the MRI variables and their combination. The proportional hazards assumption was tested with the estat phtest bvlgari necklaces and pendants knock off command in Stata and was not violated in any model (test carried out with the detail option in Stata to examine the proportional hazards assumption for each predictor as well as to carry out the global test). Non linearity of the three MRI variables was checked visually by plotting the martingale residuals41 and statistically by using a Wald test (using the Stata command nlcheck, with the spline option). There was no evidence of non linearity.For each model we obtained estimates of Harrell's concordance index (with 95% confidence intervals, calculated by bootstrapping) using the method described by Newson.42 Inference regarding improvement of the models incorporating MRI variables compared with the conventional risk model was undertaken by estimating the difference (and 95% confidence interval) in the concordance statistics using the lincom command in Stata. We calculated net reclassification improvement (with three risk groups corresponding to 0% 43 The net reclassification improvement index assesses correctness of reclassification (for example, up for events and down for non events) into different prespecified risk categories. In contrast, the integrated discrimination improvement index is a continuous measure that can be interpreted as the improvement in average sensitivity minus the change in average (1specificity). The integrated discrimination improvement index has the advantage that it is a continuous measure and therefore does not depend on arbitrary user defined risk categories. For the net reclassification improvement index and integrated discrimination improvement index values above zero indicate improved risk classification with the addition of the new variable(s). Each index was calculated with the predstat command in Stata. We also calculated Royston and Sauerbrei's44 index of discrimination (D) and optimism corrected D (Dadjusted) using the str2d command in Stata to assess prognostic separation. To assess possible clinical implications of adding the MRI variables to the conventional risk prediction model, we used the theoretical relation between the threshold probability of disease and the relative value of false positive and false negative results to ascertain the value of the various prediction models (decision curve analyses), accounting for censored observations using the stdca command in Stata.45 Preferred models are those with the highest net benefit calculated as the difference between the proportion of true positives and the proportion of false positives weighted by the relative harm of a false positive and false negative result. All analyses were repeated with Alzheimer's disease as the outcome (sensitivity analysis).Although the sample size is large for a brain imaging study, it is relatively small for risk model testing. Therefore, instead of splitting the sample into derivation and testing datasets we ran the analyses on the entire sample. To correct for optimism bias in the C statistic value (that is, over fitting to a specific sample), we undertook internal validation using 100 bootstrap samples. To use all the data from the 1721 participants and test whether missingness (assumed to be missing at random) influenced the results, we carried out multivariate imputation by chained equations (using the mi procedure in Stata) that included all 13 conventional predictors and the outcome variable. We created 10 imputed datasets and fitted each model separately on each. Results from the analysis of each imputed dataset were combined with Rubin's rules (mi estimate command in Stata)46 (see appendix 3). Analyses were completed with STATA version 13 (StataCorp, College Station, TX). All probabilities were two tailed, and significance was set at P

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