A prediction model for the risk of developing mild cognitive impairment in older adults with sarcopenia: evidence from the CHARLS
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Background
Sarcopenia significantly increases the risk of cognitive impairments in older adults. Early detection of mild cognitive impairment (MCI) in individuals with sarcopenia is essential for timely intervention.
Aims
To develop an accurate prediction model for screening MCI in individuals with sarcopenia.
Methods
We employed machine learning and deep learning techniques to analyze data from 570 patients with sarcopenia from the China Health and Retirement Longitudinal Study (CHARLS). Our model forecasts MCI incidence over the next four years, categorizing patients into low and high-risk groups based on their risk levels.
Results
The model was constructed using CHARLS data from 2011 to 2015, incorporating eight validated variables. It outperformed logistic regression, achieving an Area Under the Curve (AUC) of 0.708 (95% CI: 0.544–0.844) for the test set and accurately classifying patients’ risk in the training set. The deep learning model demonstrated a low false-positive rate of 10.23% for MCI in higher-risk groups. Independent validation using 2015–2018 CHARLS data confirmed the model’s efficacy, with an AUC of 0.711 (0.95 CI, 0.588–0.823). An online tool to implement the model is available at http://47.115.214.16:8000/ .
Conclusions
This deep learning model effectively predicts MCI risk in individuals with sarcopenia, facilitating early interventions. Its accuracy aids in identifying high-risk individuals, potentially enhancing patient care.
