Prediction of cognitive worsening in de novo Parkinson's disease: Clinical use of biomarkers

Arnaldi, Dario; Carli, Fabrizio De; Famà, Francesco; Brugnolo, Andrea; Girtler, Nicola; Picco, Agnese; Pardini, Matteo; Accardo, Jennifer; Proietti, Luca; Massa, Federico; Bauckneht, Matteo; Morbelli, Silvia; Sambuceti, Gianmario; Nobili, Flavio

doi:10.1002/mds.27190

Cited by 47 publications

(42 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the methodological approach set up can be extended by integrating and learning from follow-up data to be able to predict patient progression. Recently, Arnaldi and colleagues 31 reported results to support this statement. In their study, a group of early-diagnosed, drug-naïve patients underwent clinical and neuropsychological assessments as well as Dopamine Transporter (DaT)-SPECT and QEEG at baseline.…”

Section: Clinical Significancementioning

confidence: 89%

Electroencephalography‐based machine learning for cognitive profiling in Parkinson's disease: Preliminary results

et al. 2018

View full text Add to dashboard Cite

Background Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening. Objective The aim of this study was to investigate the use of the combination of resting‐state EEG and data‐mining techniques to build characterization models. Methods Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine‐learning algorithms to build and train characterization models, namely, support vector machines and k‐nearest neighbors models. The models were then blindly tested on data from 18 patients. Results The overall classification accuracies were 84% and 88% for the support vector machines and k‐nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate. Conclusion These results suggest that EEG features computed from a daily clinical practice exploration modality in—that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient—can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. © 2018 International Parkinson and Movement Disorder Society

show abstract

Section: Clinical Significancementioning

confidence: 89%

Electroencephalography‐based machine learning for cognitive profiling in Parkinson's disease: Preliminary results

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Novel biomarkers may enable more accurate stratification of PD based on the expected prognosis. There is also a significant interest in using the biomarkers for prediction of disease outcome [20][21][22][23][24][25]28], to properly adapt clinical trial studies as applied to appropriate patients.…”

Section: Introductionmentioning

confidence: 99%

Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease

et al. 2020

View full text Add to dashboard Cite

“…The lower rates of neuropsychological testing were observed in patients with PD and AIT. The high incidence of cognitive impairment in patients with PD is confirmed by previous clinical trials [8,9].…”

Section: Discussion Of the Resultsmentioning

confidence: 58%

“…Patients have neurodynamic disorders in the form of slowness, astonity (bradyphenia), decreased attention, functional capacity and regulatory disturbances that reflect dysfunction of the frontal lobes [7,8]. In the later stages of the disease, dementia occurs in 80 % of patients with PD [9,10]. The development of dementia is associated with a decrease in the effectiveness of antiparkinsonian drugs, an increase in axial motor disturbances (postural instability, hardening, hypomimia, dystonia, dysarthria, bradykinesia, camptocormia, autonomic dysfunction, sleep disturbances) [7].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Disorders in Patients With Parkinson's Disease on the Background of Autoimmune Pathology

Cheboraka

Слободін

Holovchenko

2018

EUREKA: Health Sciences

View full text Add to dashboard Cite

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by the predominantly dopaminergic neuronal black matter degeneration. The multicentre study of PRIAMO (PaRkinson & non Motor symptom) showed that in 98.6 % of cases, non-motor symptoms (NMS) were observed in patients with PD. Cognitive impairment is one of the most common NMS PD. According to these studies, cognitive dysfunction develops in most patients at an early stage of the disease, with mild to moderate cognitive impairment. In the later stages of the disease, dementia occurs in 80 % of patients with PD. Aim of the research. To study the peculiarities of cognitive impairment in patients with PD and autoimmune thyroiditis (AIT), their association with motor and non-motor disorders, and to assess the impact on the quality of life of patients. Materials and methods of the research. 109 patients with PD aged 47 to 75 years were examined. The main group consisted of patients with IA and IB subgroups, control – IIA and IIB subgroups. General clinical and neurological examinations, evaluation of motor functions by the Unified Parkinson's disease Rating Scale (UPDRS), neuropsychological testing (MMSE, MoCA, FAB, BDI), Hamilton's Alert Scale (HARS), scale used for assessing autonomic disorders in patients with PD and Parkinson’s disease questionnaire (PDQ-39), statistical analysis using the "Statistica 6.0" program. Results. Neuropsychological testing showed that MMSE, MoCA, and FAB obtained from patients with PD and AIT are lower compared to patients with PD. Analyzing the indicators of MMSE, FAB, and MoCA scales in dynamics, a statistically significant difference was found in subgroups of IB and IIB; in subgroups of IA and IIA – was not observed. During the analysis of the results of the neuropsychological testing, no association was found at the statistically significant level in the subgroups of IA and IB between the duration of the disease and MMSE, MoCA, and FAB scales. In the IA subgroup, in the initial review, feedback was observed on a statistically significant level of average strength between the level of anxiety and the indicator of the MMSE scale, an indicator of the MoCA scale. In the IA subgroup, there was no relationship at the statistically significant level between the indicators of neuropsychological testing (MMSE, MoCA, FAB) and motor and non-motor manifestations (depression, vegetative disorders). There was a connection at a statistically significant level between the indicators of neuropsychological testing (MMSE, MoCA, FAB) and motor and non-motor manifestations of PD in patients who received anti-parkinsonian therapy for a long period of time. The negative influence of the level of cognitive impairments on the quality of life of patients with PD and AIT was revealed, indicating the high medical and social significance of these violations. Conclusions. Neuropsychological testing showed that MMSE, MoCA, FAB scores in patients with PD and AIT (IB subgroup) were lower compared to patients with PD (IIB subgroup). There was a connection at a statistically significant level between the indicators of neuropsychological testing (MMSE, MoCA, FAB) and motor and non-motor manifestations of PD in patients who received anti-parkinsonian therapy for a long period of time. The negative influence of the level of cognitive impairments on the quality of life of patients with PD and AIT was revealed, indicating the high medical and social significance of these violations.

show abstract

Prediction of cognitive worsening in de novo Parkinson's disease: Clinical use of biomarkers

Cited by 47 publications

References 52 publications

Electroencephalography‐based machine learning for cognitive profiling in Parkinson's disease: Preliminary results

Electroencephalography‐based machine learning for cognitive profiling in Parkinson's disease: Preliminary results

Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease

Cognitive Disorders in Patients With Parkinson's Disease on the Background of Autoimmune Pathology

Contact Info

Product

Resources

About