Refining the Parkinson's Disease Risk Estimator for Decline In Cognition Tool (PREDICT).

BACKGROUND

Although cognitive decline in Parkinson disease (PD) is common, there are currently no easily accessible clinical tools to predict the risk of cognitive decline in PD.

OBJECTIVE

To refine a tool to predict cognitive decline in PD and identify potentially modifiable factors.

METHODS

We used neuropsychological scores from the Parkinson's Progression Markers Initiative de novo PD cohort to calculate a composite cognitive score (CCS) for each participant. Participants with a decrease of ≥0.5 SD in CCS between baseline and Year 4 were considered to have cognitive decline (PD-Decline), as predicted by conditional linear mixed modeling to account for nonignorable dropouts. We identified risk factors for PD-Decline using logistic regression and developed the PD Risk Estimator for Decline In Cognition Tool (PREDICT) scoring system using the regression β coefficients. The selected risk factors and the discriminative ability of the PREDICT scoring system were evaluated using bootstrapped samples.

RESULTS

Participants with excessive daytime sleepiness (odds ratio [OR]=2.8; 95% CI [1.3, 6.0]), moderate-severe motor symptoms (OR=2.6; 95% CI [1.2, 5.4]), and fewer years of formal education (OR=2.2; 95% CI [1.1, 4.1]) had significantly increased odds of being categorized as PD-decline. Possible PREDICT scores ranged from 0 to 3.5. The average bootstrap sensitivity and specificity were 59.9% and 43.5%, respectively.

CONCLUSION

We identified excessive daytime sleepiness and moderate-severe motor symptoms as potentially modifiable factors. In the future, PREDICT may offer wider opportunities for personalized risk assessment and potential risk reduction.