Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease

Abstract

Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD), significantly affecting patients' quality of life and posing challenges for clinical management. Early prediction of cognitive decline in PD is critical for timely diagnosis and intervention. However, the interplay of multivariate factors such as age, gender, and disease duration complicate early prediction. To address the multifactorial nature of cognitive impairment in PD, this study proposes a neuroscience-informed nomogram model constructed using multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to identify highly correlated clinical variables influencing cognitive function. Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. Performance evaluation of the model demonstrated high accuracy, consistency, and clinical applicability, significantly enhancing diagnostic efficiency for neurologists. Furthermore, the model provides visual comparisons of patient distributions across different predictor values, enabling personalized risk assessments. According to experimental analysis and verification, the model demonstrated outstanding prediction with a region under the ROC curve of 0.872 on the original training set and 0.870 on the validation set. Because the anticipated and observed probabilities were so consistent, the model was able to forecast the patient's likelihood of cognitive impairment.