Parkinson’s disease rating scales: a literature review

The effects of treatment with pimavanserin on activities of daily living in patients with Parkinson's disease psychosis: a 16-week, single-arm, open-label study

Background

More than half of patients with Parkinson's disease will experience psychosis symptoms in the form of hallucinations or delusions at some point over the course of their disease. These symptoms can significantly impact patients' health-related quality of life, cognitive abilities, and activities of daily living (ADLs) and function. Clinical assessment of how psychosis impacts these measures is crucial; however, few studies have assessed this sufficiently, in part due to a lack of appropriate scales for comprehensively assessing function.

Objective

The objective was to assess how symptoms of Parkinson's disease psychosis (PDP) impact ADLs and function, cognitive function, and health-related quality of life.

Design

To address this unmet need, we utilized a modified version of the Functional Status Questionnaire (mFSQ) to measure the impact of psychosis on ADLs and function in patients with PDP treated with pimavanserin, a US Food and Drug Administration-approved medication to treat hallucinations and delusions associated with PDP.

Methods

Eligible patients entered a 16-week, single-arm, open-label study of oral pimavanserin (34 mg) taken once daily. The primary endpoint was change from baseline to Week 16 on the mFSQ. Secondary endpoints included the Movement Disorders Society-modified Unified Parkinson's Disease Rating Scale (MDS-UPDRS) I and II; Schwab and England ADL; Clinical Global Impression-Severity of Illness (CGI-S), Clinical Global Impression-Improvement (CGI-I), and Patient Global Impression-Improvement (PGI-I), and were also measured as change from baseline to Week 16 using mixed-effects model for repeated measures (MMRM) and least-squares mean (LSM).

Results

Our results in a proof-of-concept, 16-week, open-label clinical study in 29 patients demonstrated that an improvement in psychosis symptoms following treatment with pimavanserin was associated with improvements in multiple measures of ADLs and function. Notably, a significant improvement was found on the primary endpoint, change from baseline to Week 16 in mFSQ score [LSM [SE] 14.0 [2.50], n = 17; 95% CI (8.8, 19.3); p < 0.0001].

Conclusion

These findings highlight the potential for improvement in function with improvement of psychosis symptoms in patients with PDP and suggest that the mFSQ may be a measurement tool to evaluate the level of improvement in function.

Trial registration

ClinicalTrials.gov Identifier: NCT04292223.

Screening cut-off scores for clinically significant fatigue in early Parkinson's disease

Background

Fatigue is one of the most disabling non-motor symptoms in PD. Researchers have previously used cut-offs validated in non-PD conditions when using the Fatigue Severity Scale (FSS) or the Multidimensional Fatigue Inventory (MFI) scores to evaluate fatigue in PD.

Objective

We used a set of criteria for diagnosing clinically significant fatigue in PD to identify the proper cut-offs of the FSS and MFI.

Methods

One hundred thirty-one PD patients (59F; age 67.3 ± 7.6 y; H&Y 1.6 ± 0.7) were assessed for clinically significant fatigue, followed by the FSS, MFI, Center for Epidemiologic Studies Depression Scale (CES-D), and Montreal Cognitive Assessment (MOCA). Mean scores were compared between 17 patients who met diagnostic criteria (significant fatigue group, SFG) and 114 who did not (non-significant fatigue group, NSFG).

Results

The SFG had significantly higher scores in the 9-item FSS (p <.0001), total MFI score (p <.0001), and every MFI dimension except reduced motivation (p =.1) than the NSFG. Using area under the curve (AUC) of receiver operating characteristic (ROC) analyses, we recommend the following cut-offs: 9-item FSS 37; total MFI 60; general fatigue 11; reduced activity 10; physical fatigue 9; mental fatigue 9; and reduced motivation 9.

Conclusions

The recommended cut-offs for clinically significant fatigue in the FSS, MFI, and MFI dimensions will be valuable for diagnosing clinically significant fatigue and for future studies in investigating pathophysiology and potential treatments of fatigue in PD.

Overview of Movement Disorders Secondary to Drugs

Drug-induced movement disorders affect a significant percentage of individuals, and they are commonly overlooked and underdiagnosed in clinical practice. Many comorbidities can affect these individuals, making the diagnosis even more challenging. Several variables, including genetics, environmental factors, and aging, can play a role in the pathophysiology of these conditions. The Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Statistical Classification of Diseases and Related Health Problems (ICD) are the most commonly used classification systems in categorizing drug-induced movement disorders. This literature review aims to describe the abnormal movements associated with some medications and illicit drugs. Myoclonus is probably the most poorly described movement disorder, in which most of the reports do not describe electrodiagnostic studies. Therefore, the information available is insufficient for the diagnosis of the neuroanatomical source of myoclonus. Drug-induced parkinsonism is rarely adequately evaluated but should be assessed with radiotracers when these techniques are available. Tardive dyskinesias and dyskinesias encompass various abnormal movements, including chorea, athetosis, and ballism. Some authors include a temporal relationship to define tardive syndromes for other movement disorders, such as dystonia, tremor, and ataxia. Antiseizure medications and antipsychotics are among the most thoroughly described drug classes associated with movement disorders.

Clinical Evaluation of Sleep Disorders in Parkinson’s Disease

The paradigm of the framing of Parkinson’s disease (PD) has undergone significant revision in recent years, making this neurodegenerative disease a multi-behavioral disorder rather than a purely motor disease. PD affects not only the “classic” substantia nigra at the subthalamic nuclei level but also the nerve nuclei, which are responsible for sleep regulation. Sleep disturbances are the clinical manifestations of Parkinson’s disease that most negatively affect the quality of life of patients and their caregivers. First-choice treatments for Parkinson’s disease determine amazing effects on improving motor functions. However, it is still little known whether they can affect the quantity and quality of sleep in these patients. In this perspective article, we will analyze the treatments available for this specific clinical setting, hypothesizing a therapeutic approach in relation to neurodegenerative disease state.

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives

According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.

Pimavanserin and Parkinson's Disease Psychosis: A Narrative Review

Pimavanserin (PMV) is the first approved drug for treating hallucinations and delusions in Parkinson's disease (PD) psychosis. Psychosis is one of the leading causes of nursing home placement in people with PD. Furthermore, hallucinations are a more frequent cause of institutionalization than motor disability or dementia related to PD. The management of PD psychosis involves antipsychotic medications. Most of the drugs in this class directly block dopamine D2 receptors, leading to significantly worsening motor symptoms in patients with PD. The most commonly used medications for managing PD psychosis are quetiapine, clozapine, and PMV. This literature review aims to study pimavanserin's history, mechanism, clinical trials, and post-marketing experience. PMV is a potent 5-HT2A receptor antagonist/inverse agonist. Moreover, this drug can interact with 5-HT2C receptors. We calculated some physicochemical descriptors and pharmacokinetic properties of PMV. Eight clinical trials of PMV and PD psychosis are registered on ClinicalTrials.gov. Only four of them have complete results already published. Meta-analytic results showed that PMV efficacy is inferior to clozapine. However, PMV has a significantly lower number of side-effects for managing psychosis in PD. Medicare database assessment revealed 35% lower mortality with PMV compared to other atypical antipsychotics. Moreover, sensitive statistical analysis demonstrated that PMV is a protective factor for the risk of falls in individuals with PD.

The impact of clinical scales in Parkinson’s disease: a systematic review

Background

Parkinson’s disease is one of the non-curable diseases and occurs by the prominent loss of neurotransmitter (dopamine) in substantia nigra pars compacta (SNpc). The main cause behind this is not yet identified and even its diagnosis is very intricate phase due to non-identified onset symptoms. Despite the fact that PD has been extensively researched over the decades, and various algorithms and strategies for early recognition and avoiding misdiagnosis have been published. The objective of this article is to focus on the current scenario and to explore the involvement of various clinical diagnostic scales in the detection of PD.

Method

An exhaustive literature review is conducted to synthesize the earlier work in this area, and the articles were searched using different keywords like Parkinson disease, motor/non-motor, treatment, diagnosis, scales, PPMI, etc., in all repositories such as Google scholar, Scopus, Elsevier, PubMed and many more. From the year 2017 to 2021, a total of 451 publications were scanned, but only 24 studies were chosen for a review process.

Findings

Mostly as clinical tools, UPDRS and HY scales are commonly used and even there are many other scales which can be helpful in detection of symptoms such as depression, anxiety, sleepiness, apathy, smell, anhedonia, fatigue, pain, etc., that affect the QoL of pateint. The recognition of non-motor manifests is typically very difficult than motor signs.

Conclusion

This study can give the beneficial research paths at an early stage diagnosis by focusing on frequent inspection of daily activities, interactions, and routine, which may also give a plethora of information on status changes, directing self-reformation, and clinical therapy.