Establishment of a Risk Prediction Model for Mild Cognitive Impairment Among Elderly Chinese

Wang, B.; Shen, Tingting; Mao, Lin; Xie, Lei; Fang, Qi; Wang, Xiaoping

doi:10.1007/s12603-020-1335-2

Cited by 13 publications

(12 citation statements)

References 33 publications

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“…It was first applied in 1972 to assess the effects of alcohol and tobacco on the risk of oral and laryngeal cancers. It considers both independent and interactive effects of influencing factors and has been applied in the risk assessment and prevention of a multitude of chronic diseases [ 19 ]. The relative ratio (RR) can be replaced by the odds ratios (OR) when the outcome occurs in less than 10% [ 20 , 21 ].…”

Section: Methodsmentioning

confidence: 99%

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A risk scoring system to predict the individual incidence of early-onset colorectal cancer

et al. 2022

BMC Cancer

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Background The incidence of early-onset colorectal cancer (EOCRC) is increasing at an alarming rate and further studies are needed to identify risk factors and to develop prevention strategies. Methods Risk factors significantly associated with EOCRC were identified using meta-analysis. An individual risk appraisal model was constructed using the Rothman–Keller model. Next, a group of random data sets was generated using the binomial distribution function method, to determine nodes of risk assessment levels and to identify low, medium, and high risk populations. Results A total of 32,843 EOCRC patients were identified in this study, and nine significant risk factors were identified using meta-analysis, including male sex, Caucasian ethnicity, sedentary lifestyle, inflammatory bowel disease, and high intake of red meat and processed meat. After simulating the risk assessment data of 10,000 subjects, scores of 0 to 0.0018, 0.0018 to 0.0036, and 0.0036 or more were respectively considered as low-, moderate-, and high-risk populations for the EOCRC population based on risk trends from the Rothman–Keller model. Conclusion This model can be used for screening of young adults to predict high risk of EOCRC and will contribute to the primary prevention strategies and the reduction of risk of developing EOCRC.

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Section: Methodsmentioning

confidence: 99%

“…These eligible variables were then included in the risk scoring system. Variables that exhibit significance in only one model will be excluded from the risk system since they were considered to be unstable [ 19 ]. The risk of publication bias was calculated using Egger’s test.…”

Section: Methodsmentioning

confidence: 99%

A risk scoring system to predict the individual incidence of early-onset colorectal cancer

et al. 2022

BMC Cancer

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“…Machine learning techniques have been used for classification, which can help in revealing potential hidden dependencies between factors and outcomes (Bratić et al, 2018 ). To our knowledge, this study is among the first in developing a machine learning framework for identifying Chinese elderly people at risk of cognitive impairment (Wang B. et al, 2020 ; Hu M. et al, 2021 ). Few studies have shown that demographics, genetic factors, brain imaging, and blood biomarkers have the potential to inform a healthy person’s likelihood of progression to mild cognitive impairment (Chang et al, 2021 ; Stonnington et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people

Wang

et al. 2022

Front. Aging Neurosci.

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Objectives: This study firstly aimed to explore predicting cognitive impairment at an early stage using a large population-based longitudinal survey of elderly Chinese people. The second aim was to identify reversible factors which may help slow the rate of decline in cognitive function over 3 years in the community.Methods: We included 12,280 elderly people from four waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), followed from 2002 to 2014. The Chinese version of the Mini-Mental State Examination (MMSE) was used to examine cognitive function. Six machine learning algorithms (including a neural network model) and an ensemble method were trained on data split 2/3 for training and 1/3 testing. Parameters were explored in training data using 3-fold cross-validation and models were evaluated in test data. The model performance was measured by area-under-curve (AUC), sensitivity, and specificity. In addition, due to its better interpretability, logistic regression (LR) was used to assess the association of life behavior and its change with cognitive impairment after 3 years.Results: Support vector machine and multi-layer perceptron were found to be the best performing algorithms with AUC of 0.8267 and 0.8256, respectively. Fusing the results of all six single models further improves the AUC to 0.8269. Playing more Mahjong or cards (OR = 0.49,95% CI: 0.38–0.64), doing more garden works (OR = 0.54,95% CI: 0.43–0.68), watching TV or listening to the radio more (OR = 0.67,95% CI: 0.59–0.77) were associated with decreased risk of cognitive impairment after 3 years.Conclusions: Machine learning algorithms especially the SVM, and the ensemble model can be leveraged to identify the elderly at risk of cognitive impairment. Doing more leisure activities, doing more gardening work, and engaging in more activities combined were associated with decreased risk of cognitive impairment.

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“…Some studies have found that education, age, gender, stroke history, neighborhood socioeconomic status, diabetes, apolipoprotein ε4 carrier and body mass index (BMI) could be used as risk factors to construct a predictive model for cognitive decline, with a sensitivity of 75% and speci city of 81% [8]. Another study on the elderly in China used 10 risk factors to construct an MCI prediction model with a sensitivity of 86.6% and speci city of 76.5% [9]. However, these studies have focused on the elderly in the community and little attention has been paid to hospitalized elderly patients.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Clinical Prediction Model for Elderly Patients with Preoperative mild cognitive impairment: A Prospective Cohort Study

Zhang

Xin

Yang

et al. 2022

Preprint

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Background Mild cognitive impairment (MCI) in elderly patients undergoing surgery is neglected easily by clinicians and families. Preoperative patients with MCI are more likely to suffer from postoperative cognitive dysfunction and postoperative delirium, so an effective MCI prediction method has important implications for ameliorating perioperative cognitive function. Objective This study is designed to construct a predictive model to provide a novel approach for preoperative MCI diagnosis in geriatric patients. Methods Patients over 65 years old who underwent elective surgery with general anesthesia were screened. Patients were randomly divided into training cohort (n = 258) and test cohort (n = 49) by the ratio of 8:2, and baseline demographic variables and characteristics of the patients in the different cohort were compared. The least absolute shrinkage and selection operator (LASSO) regression was used to identify risk factors in the training cohort. A nomogram was constructed based on the logistic regression. Receiver operating characteristic (ROC) curves and calibration charts were drawn in the training cohort and test cohort respectively to evaluate the diagnostic value of the prediction model. The decision curve analysis (DCA) was used to value the clinical utility of the prediction model. Results In this study, a total of 307 elderly surgical patients were enrolled, including 137 patients with MCI and 170 patients with normal cognitive function. Multivariate analysis showed that history of more than two operations, higher urea nitrogen, lack of education, body mass index (BMI) < 24kg/m2 and lower albumin/globulin ratio were the independent risk factors for preoperative MCI. The C statistic of the prediction model in the training cohort and test cohort was 0.754 (95%CI, 0.695–0.812) and 0.708 (95%CI, 0.559–0.856) respectively. The threshold probability of the net benefit ranged from 45–81% in the DCA. Conclusions The independent risk factors for preoperative MCI in elderly patients were two or more operations, higher blood urea nitrogen level, shorter years of education, BMI < 24kg/m2, and lower albumin/globulin ratio. The predictive model has a certain diagnostic value for preoperative MCI in elderly patients, and provides a novel method for anesthetists to evaluate preoperative cognitive function in elderly patients.

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Establishment of a Risk Prediction Model for Mild Cognitive Impairment Among Elderly Chinese

Cited by 13 publications

References 33 publications

A risk scoring system to predict the individual incidence of early-onset colorectal cancer

A risk scoring system to predict the individual incidence of early-onset colorectal cancer

Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people

Development and Validation of a Clinical Prediction Model for Elderly Patients with Preoperative mild cognitive impairment: A Prospective Cohort Study

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