Cognitive and behavioral neurology : official journal of the Society for Behavioral and Cognitive Neurology · 2026
Refining the Parkinson's Disease Risk Estimator for Decline In Cognition Tool (PREDICT).
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Carlisle Tara C, Gunn-Sandell Lauren B, Lippitt William L, Zhu Shuai, Medina Luis D, Holden Samantha K, Amara Amy W, Bettcher Brianne M, Carlson Nichole E
Аннотация
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.
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.