Dataset Information

Population-based dementia prediction model using Korean public health examination data: A cohort study.

ABSTRACT: The early identification and prevention of dementia is important for reducing its worldwide burden and increasing individuals' quality of life. Although several dementia prediction models have been developed, there remains a need for a practical and precise model targeted to middle-aged and Asian populations. Here, we used national Korean health examination data from adults (331,126 individuals, 40-69 years of age, mean age: 52 years) from 2002-2003 to predict the incidence of dementia after 10 years. We divided the dataset into two cohorts to develop and validate of our prediction model. Cox proportional hazards models were used to construct dementia prediction models for the total group and sex-specific subgroups. Receiver operating characteristics curves, C-statistics, calibration plots, and cumulative hazards were used to validate model performance. Discriminative accuracy as measured by C-statistics was 0.81 in the total group (95% confidence interval (CI) = 0.81 to 0.82), 0.81 in the male subgroup (CI = 0.80 to 0.82), and 0.81 in the female subgroup (CI = 0.80 to 0.82). Significant risk factors for dementia in the total group were age; female sex; underweight; current hypertension; comorbid psychiatric or neurological disorder; past medical history of cardiovascular disease, diabetes mellitus, or hypertension; current smoking; and no exercise. All identified risk factors were statistically significant in the sex-specific subgroups except for low body weight and current hypertension in the female subgroup. These results suggest that public health examination data can be effectively used to predict dementia and facilitate the early identification of dementia within a middle-aged Asian population.

SUBMITTER: Park KM

PROVIDER: S-EPMC6372230 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Population-based dementia prediction model using Korean public health examination data: A cohort study.

Park Kyung Mee KM Sung Ji Min JM Kim Woo Jung WJ An Suk Kyoon SK Namkoong Kee K Lee Eun E Chang Hyuk-Jae HJ

PloS one 20190212 2

The early identification and prevention of dementia is important for reducing its worldwide burden and increasing individuals' quality of life. Although several dementia prediction models have been developed, there remains a need for a practical and precise model targeted to middle-aged and Asian populations. Here, we used national Korean health examination data from adults (331,126 individuals, 40-69 years of age, mean age: 52 years) from 2002-2003 to predict the incidence of dementia after 10 ...[more]

PMID: 30753205

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Project description:Hypertension and diabetes mellitus are major chronic diseases that are important factors in the management of cardiovascular disease. In order to prevent the occurrence of chronic diseases, proper health management through periodic health check-ups is necessary. The purpose of this study is to determine the incidence of hypertension and diabetes mellitus according to the health check-up, and to develop a predictive model for hypertension and diabetes according to the health check-up. We used the National Health Insurance Corporation database of Korea and checked whether hypertension or diabetes occurred from that date according to the number of health check-ups over the past 10 years. Compared to those who underwent five health check-ups, those who participated in the first screening had hypertension (OR = 2.18, 95% CI = 2.14-2.22), diabetes mellitus (OR = 1.33, 95% CI = 1.30-1.35) and both diseases (OR = 2.46, 95% CI = 2.39-2.53); individuals who underwent 10 screenings had hypertension (OR = 0.86, 95% CI = 0.83-0.88), diabetes mellitus (OR = 0.83, 95% CI = 0.81-0.85) and both diseases (OR = 0.83, 95% CI = 0.79-0.87). Individuals who attended fewer than five screenings compared with individuals who attended five or more screenings had hypertension (OR = 1.61, 95% CI = 1.59-1.62; AUC = 0.66), diabetes mellitus (OR = 1.21, 95% CI = 1.20-1.22; AUC = 0.59) and both diseases (OR = 1.75, 95% CI = 1.72-1.78, AUC = 0.63). The machine learning-based prediction model using XGBoost showed higher performance in all datasets than the conventional logistic regression model in predicting hypertension (accuracy, 0.828 vs. 0.628; F1-score, 0.800 vs. 0.633; AUC, 828 vs. 0.630), diabetes mellitus (accuracy, 0.707 vs. 0.575; F1-score, 0.663 vs. 0.576; AUC, 0.710 vs. 0.575) and both diseases (accuracy, 0.950 vs. 0.612; F1-score, 0.950 vs. 0.614; AUC, 0.952 vs. 0.612). It was found that health check-up had a great influence on the occurrence of hypertension and diabetes, and screening frequency was more important than other factors in the variable importances.

Project description:ObjectiveTo 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.DesignPopulation based cohort study of individuals aged ≥ 65.SettingThe Dijon magnetic resonance imaging study cohort from the Three-City Study, France.Participants1721 people without dementia who underwent an MRI scan at baseline and with known dementia status over 10 years' follow-up.Main outcome measureIncident dementia (all cause and Alzheimer's disease).ResultsDuring 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. Similar results were observed when the outcome was restricted to Alzheimer's disease.ConclusionsData from MRI do not significantly improve discrimination performance in prediction of all cause dementia beyond a model incorporating demographic, cognitive, health, lifestyle, physical function, and genetic data. There were, however, statistical improvements in reclassification, prognostic separation, and some evidence of clinical utility.

Project description:BackgroundMany dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records.MethodsWe identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters' Dementia Risk Score, Mehta's RxDx-Dementia Risk Index, and Nori's ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models.ResultsWe reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67-0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69-0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62-0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard.ConclusionWe observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.

Project description:PurposeChronic cough is a prevalent condition in the community and may pose considerable impairment to quality of life (QoL). However, its disease burden remains largely undefined in the general population. The present study investigated the relationship between chronic cough and health-related QoL in a Korean nationwide population database, with an emphasis on clinical conditions which may confound the impact of cough.MethodsThis study analyzed cross-sectional datasets of adults (aged ≥ 40 years) in the Korean National Health and Nutrition Examination Survey 2010-2016. Health-related QoL was assessed using the 3-level EuroQoL 5-dimension component (EQ-5D-3L) index score. The presence of chronic cough and other conditions were defined using structured questionnaires.ResultsThe prevalence of chronic cough was 3.48% ± 0.17% among adults aged ≥ 40 years. The overall EQ-5D-3L index score was significantly lower in subjects with than without chronic cough (0.79 ± 0.01 vs. 0.86 ± 0.00, P < 0.001). In subgroup analyses by age and sex, chronic cough had a notably large impact on QoL in women aged ≥ 65 years (vs. those without chronic cough: 0.55 ± 0.04 vs. 0.70 ± 0.01, P < 0.001), although the mean difference in the scores exceeded the minimally important difference score of 0.05 in all subgroups. In multivariate analyses, chronic cough was significantly associated with QoL, independent of confounders including depression, arthritis, asthma, and chronic obstructive pulmonary disease. In dimension analyses, chronic cough was more associated with anxiety/depression, pain/discomfort, and usual activities than with self-care or mobility in the EQ-5D.ConclusionsThe present study demonstrated significant associations between chronic cough and health-related QoL in a nationwide large general adult population aged ≥ 40 years, which were independent of clinical confounders. The impact of chronic cough was greater in women aged ≥ 65 years. These findings indicate a considerable burden of chronic cough in the general population and warrant further investigations to assess the disease burden of chronic cough in a global scale.

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Population-based dementia prediction model using Korean public health examination data: A cohort study.

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Population-based dementia prediction model using Korean public health examination data: A cohort study.

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