The treatment of cognitive dysfunction in dementia: a multiple treatments meta-analysis

Perng, Cheng-Hwang; Chang, Yue‐Cune; Tzang, Ruu‐Fen

doi:10.1007/s00213-018-4867-y

Cited by 58 publications

(36 citation statements)

References 27 publications

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“…Nicametate Citrate, a vasodilator, was also negatively associated with incident AD. This may be in line with the literature showing effects of vasodilators on increasing cognitive function and reducing the risk of vascular dementia, although the exact mechanism remains unclear 33,34 . Further research is required.…”

Section: Discussionsupporting

confidence: 89%

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

et al. 2020

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Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals' history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer's disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: "definite AD" with diagnostic codes and dementia medication (n = 614) and "probable AD" with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on "definite AD" and "probable AD" outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

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Section: Discussionsupporting

confidence: 89%

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

et al. 2020

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“…Dementia affected more than 47 million patients in 2015 worldwide, and the number of patients is predicted to be approximately 135 million in 2050 [1]. Given that there is no specific treatment for advanced dementia, the identification of high-risk patients and the management of their risk factors are crucial for reducing the burden of the disease [2,3].…”

Section: Introductionmentioning

confidence: 99%

Glomerular hyperfiltration is associated with dementia: A nationwide population-based study

et al. 2020

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Background Glomerular hyperfiltration may be a clinical phenotype of endothelial dysfunction. Endothelial dysfunction may cause vascular dementia through the deterioration of cerebral blood flow. We aimed to identify the risk of dementia in people with glomerular hyperfiltration. Methods Using the Korean National Health Information Database, we included subjects aged �45 years who underwent national health screening examinations between 2012 and 2015 and who had no previous history of end-stage renal disease or dementia (n = 2,244,582). The primary exposure was glomerular hyperfiltration. We divided the subjects into groups by sex and five-year age intervals and categorized each group into 8 intervals according to estimated glomerular filtration (eGFR). The subjects with an eGFR �95 th percentile in each group were defined as the hyperfiltration group. The outcomes were development of all types of dementia, Alzheimer's dementia and vascular dementia. Multivariable Cox proportional hazards models were used to analyze the hazard ratios (HRs) for outcomes. Results The Hyperfiltration group showed a higher risk for the development of all types of dementia [adjusted HR 1.09 (95% CI, 1.03-1.15)] and vascular dementia [adjusted HR 1.33 (95% CI, 1.14-1.55)] than the reference group. However, the association between hyperfiltration and Alzheimer's dementia was not statistically significant.

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“…This may be in line with the literature showing effects of vasodilators on increasing cognitive function and reducing the risk of vascular dementia, although the exact mechanism remains unclear. 34,35 Further research is required.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Prediction of Incidence of Alzheimer’s Disease Using Large-Scale Administrative Health Data

Park

Cho

Wall

et al. 2019

Preprint

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AbstractNationwide population-based cohort provides a new opportunity to build a completely automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N=40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. To define incident AD two operational definitions were considered: “definite AD” with diagnostic codes and dementia medication (n=614) and “probable AD” with only diagnosis (n=2,026). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age, and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

show abstract

The treatment of cognitive dysfunction in dementia: a multiple treatments meta-analysis

Cited by 58 publications

References 27 publications

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Glomerular hyperfiltration is associated with dementia: A nationwide population-based study

Machine Learning Prediction of Incidence of Alzheimer’s Disease Using Large-Scale Administrative Health Data

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