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ORIGINAL RESEARCH article

Front. Public Health
Sec. Aging and Public Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1447366

Risk Prediction Model of Cognitive Performance in Older people with cardiovascular diseases: A Study of the National Health and Nutrition Examination Survey Database

Provisionally accepted
Wang Hui Wang Hui Dikang Pan Dikang Pan Sensen Wu Sensen Wu Yachan Ning Yachan Ning Cong Wang Cong Wang Jianming Guo Jianming Guo Yongquan Gu Yongquan Gu *
  • Xuanwu Hospital, Capital Medical University, Beijing, China

The final, formatted version of the article will be published soon.

    Background and aim: Changes in cognitive function are commonly associated with aging in patients with cardiovascular diseases. The objective of this research was to construct and validate a nomogram-based predictive model for the identification of cognitive impairment in older people suffering from cardiovascular diseases. Methods and results: This retrospective study included 498 participants with cardiovascular diseases aged >60 selected from the NHANES 2011-2014. The study employed the Minor Absolute Shrinkage and Selection Operator (LASSO) regression model, in conjunction with multivariate logistic regression analysis, to identify relevant variables and develop a predictive model. We used statistical techniques as in the Minor Absolute Shrinkage (MAS) and the Selection Operator (LASSO) regression model, in conjunction with multivariate logistic regression analysis, to identify variables that were significantly predictive of the outcome. After which, based on the selected relevant variables, we developed a machine learning model that was predictive of cognitive impairment such as Alzheimer's diseases in the older people. The effectiveness of the resultant nomogram was evaluated by assessing its discriminative capability, calibration, and conducting decision curve analysis (DCA). The constructed predictive nomogram included age, race, educational attainment, poverty income ratio, and presence of sleep disorder as variables. The model demonstrated 4 robust discriminative capability, achieving an area under the receiveroperating characteristic curve of 0.756, and exhibited precise calibration. Consistent performance was confirmed through 10-fold cross-validation, and DCA deemed the nomogram clinically valuable. Conclusions: We constructed a NHANES cardiovascular-based nomogram predictive model of Cognitive impairment. The model exhibited robust discriminative ability and validity, offering a scientific framework for community healthcare providers to assess and detect the risk of cognitive decline in these patients prematurely.

    Keywords: cognitive impairment, older adults, Alzheimer's disease, nomogram, Prediction model, NHANES

    Received: 12 Jun 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Hui, Pan, Wu, Ning, Wang, Guo and Gu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yongquan Gu, Xuanwu Hospital, Capital Medical University, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.