Development and validation of a prognostic scoring system for cognitive decline in adults aged 50 and older in China

Abstract

Background: With the gradual aging of the global population, early identification and intervention are crucial for mitigating the negative impact of cognitive decline on patients and the healthcare system. This study aimed to develop and validate a prognostic scoring system for predicting cognitive decline in adults aged 50 and over in Shanghai, China. Methods: This community-based longitudinal study included 1,032 participants aged 50 and older with normal cognitive function at baseline. Of them, 986 participants were followed up for 2 years. Complete data from 864 individuals were randomly divided into derivation (n = 686) and validation (n = 178) cohorts and used to generate a prognostic scoring model. Sociodemographic and behavioral characteristics, comorbidities, and biochemical factors were collected from all participants. The least absolute shrinkage and selection operator (LASSO) logistic regression method was used to identify significant predictors. A multivariable logistic regression model was developed and validated using derivation and validation cohorts. Results: Of the thirteen variables initially selected, nine (age, gender, smoking, tea drinking history, hypertension, diabetes mellitus, coronary artery disease (CAD), hyperlipidemia, cerebral hemorrhage, and decline in daily function) were included in the final model. The nomogram-based scoring system showed moderate discriminatory power, with the area under the curve (AUC) of 0.65 and 0.67 in the training and validation sets, respectively, and good calibration. Conclusion: The developed prognostic scoring system provides a practical tool for predicting cognitive decline among adults aged 50 and older in Shanghai, China. The moderate discriminatory power and good calibration suggest that the model can effectively guide early interventions. Future research should validate the model in diverse populations and explore additional risk factors to enhance its predictive accuracy.