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Introduction: Falls are a common and debilitating complication in Parkinson's disease (PD). Previous studies have primarily focused on cohorts with prevalent falls or advanced disease stages. This study assessed risk factors for falls in early-stage falls-naïve cohort stratified by sex., Methods: A total of 172 PD patients (<5 years disease duration) were selected from a registry-based study that reported no falls at baseline. All patients were assessed with a standardized data extraction form and falls were defined according to UPDRS Item 13. Hazard Ratios were calculated with univariable and stepwise multivariable Cox Proportional Hazard regression models., Results: Among the study sample, 61 (35.4 %) patients were female. At baseline, female and male PD groups were comparable in terms of age, disease duration, and UPDRS scores, although female PD patients had higher scores for UPDRS IV. Over a mean follow-up period of 3.9 (3.0) years, falls were reported in 13 female (21.3 %) and 18 male (16.2 %) PD patients. In female PD, motor fluctuations (HR [95 %] = 1.8 [1.3 - 2.6], p < 0.001) and postural stability (HR [95 %] = 4.2 [1.6 - 10.6], p = 0.003) emerged as significant predictors for falls, whereas in male PD, stepwise Cox regression selected freezing of gait (HR [95 %] = 2.0 [1.0 - 4.2], p = 0.053) and postural instability (HR [95 %] = 2.2 [0.89 - 5.4], p = 0.089) as the primary predictors for falls, although they were non-significant., Conclusion: Our findings suggest that the risk factors for falls differ between female and male early-stage PD patients, which may have important implications for clinical management., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: In Parkinson's Disease (PD), despite available treatments focusing on symptom alleviation, the effectiveness of conventional therapies decreases over time. This study aims to enhance the identification of candidates for device-aided therapies (DAT) using artificial intelligence (AI), addressing the need for improved treatment selection in advanced PD stages., Methods: This national, multicenter, cross-sectional, observational study involved 1086 PD patients across Spain. Machine learning (ML) algorithms, including CatBoost, support vector machine (SVM), and logistic regression (LR), were evaluated for their ability to identify potential DAT candidates based on clinical and demographic data., Results: The CatBoost algorithm demonstrated superior performance in identifying DAT candidates, with an area under the curve (AUC) of 0.95, sensitivity of 0.91, and specificity of 0.88. It outperformed other ML models in balanced accuracy and negative predictive value. The model identified 23 key features as predictors for suitability for DAT, highlighting the importance of daily "off" time, doses of oral levodopa/day, and PD duration. Considering the 5-2-1 criteria, the algorithm identified a decision threshold for DAT candidates as > 4 times levodopa tablets taken daily and/or ≥1.8 h in daily "off" time., Conclusion: The study developed a highly discriminative CatBoost model for identifying PD patients candidates for DAT, potentially improving timely and accurate treatment selection. This AI approach offers a promising tool for neurologists, particularly those less experienced with DAT, to optimize referral to Movement Disorder Units., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:• Eric Freire-Alvarez has received advisory, consulting, and lecture fees from AbbVie, Almirall, Bial, Eisai, UCB Pharma, Teva, Neuraxpharm, Stada, Esteve and Zambon. He is an investigator on clinical trials as principal investigator for AbbVie, Neuroderm, Anavex, Cerevel, Roche, Irlab, Zambon, Bial, Impax and Annovis.• Rocio García-Ramos has participated as a consultant and received sponsorship for multiple activities from Abbvie, Bial, Merz, Esteve, Stada and Zambon.• Francisco Escamilla-Sevilla has participated as a consultant and received sponsorship for multiple activities from Abbvie, Bial, Boston Scientific, Esteve, Medtronic, Stada and Zambon., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: The applause sign has been associated with various neurodegenerative diseases. We investigate its validity in the differential diagnosis of progressive supranuclear palsy and Parkinson's disease, and its relationship with neuropsychological tests., Patients and Methods: 23 patients with progressive supranuclear palsy and 106 patients with Parkinson's disease were included and administered the following scales: progressive supranuclear palsy rating scale, unified Parkinson's disease rating scale (UPDRS), mini-mental state examination (MMSE), frontal assessment battery (FAB), neuropsychiatric inventory and three-clap test., Results: 73.9% with progressive supranuclear palsy and 21.7% with Parkinson's disease showed a positive applause sign. Only a positive applause sign, UPDRS II score and disease duration were found to be predictors of progressive supranuclear palsy. Both patient-groups showed statistically significant correlations between the applause sign and neuropsychological tests: in progressive supranuclear palsy patients MMSE correlation coefficient: 0.62 (p: 0.002) and FAB correlation coefficient: 0.48 (p: 0.02), and in Parkinson's disease patients MMSE correlation coefficient: 0.47 (p<0.001) and FAB correlation coefficient: 0.43 (p<0.001). Verbal fluency and inhibitory control (FAB) and writing and orientation in time (MMSE) discriminated between patients with normal and positive applause sign., Conclusions: A positive applause sign is not specific to progressive supranuclear palsy and may also be observed in Parkinson's disease patients with altered cognition, and it's related to cortical frontal abnormalities such as language disorders and inhibitory control., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Introduction: The presence of asymmetry in symptoms and clinical signs favours the diagnosis of Parkinson's disease (PD). The aim of this study is to analyse this symptom asymmetry as a function of different variables and compare it with other parkinsonisms., Materials and Methods: 201 Patients with PD were studied. The sample was supplemented with 29 patients diagnosed with MSA-P (according to the criteria established by the American Academy of Neurology) and 17 with PSP (according to the criteria established by the NINDS-SPSP International Workshop). The symmetry was evaluated, based on items 20-23, 25 and 26 of the Unified Parkinson's Disease Rating Scale, by subtracting the motor score for the left side from that for the right side. Those patients with a difference of one point or more were designated as being asymmetric., Results: Around 16.4% of patients presented symmetrical clinical symptoms. There were no differences between those patients with or without family history of the disease. Those patients with symmetric symptoms were found to have longer symptomatic disease duration (10.8 vs. 7.9 years), a worse mental state (UPDRS I: 3.9 vs. 3.2), a higher incidence of complications (UPDRS IV: 4.5 vs. 3.2) and had their activities of daily living (ADL) affected to a greater degree (UPDRS II: 13.0 vs. 11.0). Around 48.3% of the MSA-P patients and 52.9% of the PSP patients showed symmetric symptoms., Conclusions: The degree of symmetry is not useful in differentiating between sporadic and familial PD. However, the observation of highly symmetrical symptoms in a patient with short evolution time indicates that an atypical parkinsonism should be suspected., (Copyright 2010 Elsevier B.V. All rights reserved.)

Introduction: The aim of this study is to analyze the clinical differences between Parkinson's disease patients with major (MD) and minor depression (md) and to see how both affect the quality of life., Material and Methods: 118 patients diagnosed with Parkinson's disease. The mean age of onset was 60.4+/-11.2 years with a mean duration of 8.5+/-6.2 years. Depression was diagnosed according to DSM-IV-TR criteria. Scores on the Hamilton depression inventory, MMSE, PDQ-39, NPI-10, UPDRS III, and UPDRS IV were recorded., Results: Twenty-one patients (17.8%) met the criteria of major depression (MD) and 33 (28.0%) those of minor depression (md). The scores on the PDQ-39 and NPI-10 of patients with MD were higher than in patients with md, and control group. The MMSE scores were lower in patients with MD. In 52.2% of the patients with MD, the diagnosis of depression was made prior to that of PD, this occurred only in 24.2% of the patients with md (p<0.001). The presence of anhedonia was related to cognitive impairment and the presence of neuropsychiatric symptoms., Discussion: MD is probably a part of the disease process of PD; it is associated with cognitive impairment and may precede motor symptoms.