Development and validation of a nomogram for predicting mild cognitive impairment in middle-aged and elderly people

Abstract

Background: Mild cognitive impairment (MCI) is a clinical cognitive impairment state between dementia and normal aging. Early identification of MCI is beneficial, and it can delay the development of dementia. We aimed to develop and validate a prediction model to predict MCI of middle-aged and elderly people (aged 45 years and over).

Methods: According to 478 middle-aged and elderly people (48-85 years old) from a cross-sectional study, we developed and validated a predictive nomogram. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to select variables and develop a prediction model. The performance of the nomogram was evaluated in terms of its discriminative power, calibration, and decision curve analysis (DCA).

Results: The predictive nomogram was composed of the following: age, gender, education level, residence, and reading. The model showed good discrimination power (area under receiver-operating characteristic (ROC) curve was 0.8704) and good calibration. Similar results were seen in 10-fold cross-validation. The nomogram showed clinically useful in DCA analysis.

Conclusion: This predictive nomogram provides researchers with a practical tool for predicting MCI. The variables included in this nomogram were readily available. The population used for this nomogram was middle-aged and elderly people.

Keywords: Middle-aged and elderly people; Mild cognitive impairment (MCI); Nomogram; Rugao.