Optimal MMSE and MoCA cutoffs for cognitive diagnoses in Parkinson's disease: A data-driven decision tree model.

Autor: Fiorenzato E; Department of Neuroscience, University of Padua, Padua, Italy. Electronic address: eleonora.fiorenzato@unipd.it., Cauzzo S; Department of Neuroscience, University of Padua, Padua, Italy; Department of Medicine, University of Padua, Padua, Italy. Electronic address: simone.cauzzo@unipd.it., Weis L; IRCCS San Camillo Hospital, Venice, Italy., Garon M; Department of Neuroscience, University of Padua, Padua, Italy; Padua Neuroscience Center (PNC), University of Padua, Padua, Italy. Electronic address: michela.garon@unipd.it., Pistonesi F; Department of Neuroscience, University of Padua, Padua, Italy. Electronic address: francesca.pistonesi@unipd.it., Cianci V; Department of Neuroscience, University of Padua, Padua, Italy., Nasi ML; Complex Operative Unit (UOC) of the Psychology, Neurology Hospital division, Padua University Hospital, Padua, Italy. Electronic address: marialaura.nasi@studenti.unipd.it., Vianello F; Department of Neuroscience, University of Padua, Padua, Italy. Electronic address: francesca.vianello.3@unipd.it., Zecchinelli AL; Parkinson Institute Milan, ASST G. Pini-CTO, Milan, Italy. Electronic address: anna.zecchinelli@asst-pini-cto.it., Pezzoli G; Fondazione Grigioni Per il Morbo Di Parkinson, Milan, Italy. Electronic address: gianni.pezzoli@asst-pini-cto.it., Reali E; Parkinson Institute Milan, ASST G. Pini-CTO, Milan, Italy., Pozzi B; Parkinson Institute Milan, ASST G. Pini-CTO, Milan, Italy., Isaias IU; Parkinson Institute Milan, ASST G. Pini-CTO, Milan, Italy; Department of Neurology, University Hospital of Würzburg, Julius Maximilian University of Würzburg, Würzburg, Germany. Electronic address: ioannis.isaias@asst-pini-cto.it., Siri C; Parkinson Institute Milan, ASST G. Pini-CTO, Milan, Italy; Movement Disorders Rehabilitation Department, Moriggia-Pelascini Hospital, Gravedona, Italy., Santangelo G; Department of Psychology, University of Campania 'Luigi Vanvitelli', Caserta, Italy. Electronic address: gabriella.santangelo@unicampania.it., Cuoco S; Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', Neuroscience Section, University of Salerno, Baronissi, Salerno, Italy., Barone P; Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', Neuroscience Section, University of Salerno, Baronissi, Salerno, Italy. Electronic address: pbarone@unisa.it., Antonini A; Department of Neuroscience, University of Padua, Padua, Italy; Padua Neuroscience Center (PNC), University of Padua, Padua, Italy; Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy. Electronic address: angelo.antonini@unipd.it., Biundo R; Complex Operative Unit (UOC) of the Psychology, Neurology Hospital division, Padua University Hospital, Padua, Italy; Department of General Psychology, University of Padua, Padua, Italy. Electronic address: roberta.biundo@unipd.it.
Jazyk: angličtina
Zdroj: Journal of the neurological sciences [J Neurol Sci] 2024 Nov 15; Vol. 466, pp. 123283. Date of Electronic Publication: 2024 Oct 22.
DOI: 10.1016/j.jns.2024.123283
Abstrakt: Background: Detecting cognitive impairment in Parkinson's disease (PD) is challenging due to diverse manifestations and outdated diagnostic criteria. Cognitive screening tools, as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), are adopted worldwide, but despite several cutoffs has been proposed for PD, no consensus has been reached, hindered by limited sample sizes, lack of validation, and inconsistent age- and education-adjustments.
Objectives: Determine the optimal MMSE and MoCA cutoffs in a large PD cohort, spanning from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD), and develop a decision tree model to assist physicians in cognitive workups.
Methods: Our retrospective Italian multicenter study involves 1780 PD, cognitively diagnosed with a level-II assessment: PD-NC(n = 700), PD-MCI(n = 706), and PDD(n = 374). Optimal cutoffs (for raw scores) were determined through ROC analysis. Then, a machine learning approach-decision trees-was adopted to validate and analyze the possible inclusion of other relevant clinical features.
Results: The decision tree model selected as primary feature a MMSE cutoff ≤24 to predict dementia, and a score ≤ 27 for PD-MCI. To enhance PD-MCIvs.PD-NC accuracy, it also recommends including a MoCA score ≤ 22 for PD-MCI, and > 22 for PD-NC. Age and education were not selected as relevant features for the cognitive workup. Both MoCA and MMSE cutoffs exhibited high sensitivity and specificity in detecting PD cognitive statues.
Conclusions: For the first time, a clinical decision tree model based on robust MMSE and MoCA cutoffs has been developed, allowing to diagnose PD-MCI and/or PDD with a high accuracy and short administration time.
Competing Interests: Declaration of competing interest The authors declare no competing financial interest.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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