Predictive Model of Quality of Life in Patients with Parkinson's Disease

Burden of non-motor symptoms of Parkinson's disease: cost-of-illness and quality-of-life estimates through a scoping review

INTRODUCTION

Parkinson's Disease (PD) is a progressive, chronic neurodegenerative disease, representing significant economic and social burdens. It is typically defined by motor symptoms (MSs), however, this does not reflect the full patient burden. Non-motor symptoms (NMSs) are increasingly recognized as central characteristics of PD. However, they still lack recognition in research. Therefore, this study aims to identify relevant NMSs, their prevalence, and the effect they have on Quality-of-Life (QoL) and Cost-of-Illness (COI). Secondly, it aims to identify gaps in the current body of knowledge and propose possible ways future research could bridge those gaps.

METHODS

The study employed a scoping review, identifying 60 records for inclusion, using PubMed and Web of Science. It included studies from Spain or Italy, including data on People with Parkinson's Disease. A comparative analysis was performed using Microsoft Excel.

RESULTS

It showed that the body of evidence relevant to NMSs, their prevalence, QoL, and COI is limited, or that estimates vary to an extent where interpretation is difficult.

CONCLUSION

Most studies suffer from generalization, representation, and standardization issues, stemming from their designs and methodological decisions. Although the findings of this study should be interpreted with caution, several recommendations are made for future research.

Predicting Quality of Life in Parkinson's Disease: A Machine Learning Approach Employing Common Clinical Variables

Background: Parkinson's Disease significantly impacts health-related quality of life, with the Parkinson's Disease Questionnaire-39 extensively used for its assessment. However, predicting such outcomes remains a challenge due to the subjective nature and variability in patient experiences. This study develops a machine learning model using accessible clinical data to enable predictions of life-quality outcomes in Parkinson's Disease and utilizes explainable machine learning techniques to identify key influencing factors, offering actionable insights for clinicians. Methods: Data from the Parkinson's Real-world Impact Assessment study (PRISM), involving 861 patients across six European countries, were analyzed. After excluding incomplete data, 627 complete observations were used for the analysis. An ensemble machine learning model was developed with a 90% training and 10% validation split. Results: The model demonstrated a Mean Absolute Error of 4.82, a Root Mean Squared Error of 8.09, and an R2 of 0.75 in the training set, indicating a strong model fit. In the validation set, the model achieved a Mean Absolute Error of 11.22, a Root Mean Squared Error of 13.99, and an R2 of 0.36, showcasing moderate variation. Key predictors such as age at diagnosis, patient's country, dementia, and patient's age were identified, providing insights into the model's decision-making process. Conclusions: This study presents a robust model capable of predicting the impact of Parkinson's Disease on patients' quality of life using common clinical variables. These results demonstrate the potential of machine learning to enhance clinical decision-making and patient care, suggesting directions for future research to improve model generalizability and applicability.

Patient With Parkinson Disease and Care Partner Perceptions of Key Domains Affecting Health-Related Quality of Life: Systematic Review

BACKGROUND AND OBJECTIVES

To effectively customize Parkinson disease (PD) programs, it is important to incorporate the "individual's voice" and have a thorough understanding of the symptom priorities of people with PD (PwP) and care partners (CP). In this convergent integrated mixed-method systematic review, we aimed to analyze qualitative and quantitative evidence of PD motor and nonmotor symptoms affecting health-related quality of life (HRQOL) in PwP and CP, comparing priorities across different levels of disease severity.

METHODS

We searched MEDLINE, PsycINFO, Web of Science, Embase, and Scopus; ProQuest Dissertations and Theses Global; and the Michael J. Fox Foundation Data Resources for studies published up to June 29, 2022. We included qualitative, quantitative, and mixed-method studies investigating PD symptom priorities among PwP and CP. We critically appraised eligible studies for methodological quality using the Mixed-Methods Appraisal Tool. Derived terms were mapped and coded according to thematic attribution. Independent syntheses of qualitative and quantitative evidence and transformation of quantitative data into qualitative formats were performed.

RESULTS

Of the 7,716 identified studies, we included 70 that provided qualitative (n = 13), quantitative (n = 53), and mixed (n = 4) evidence. We included 604 mapped terms representing 11 PwP-identified and CP-identified motor and nonmotor symptom categories. Across all PD stages, both PwP and CP considered 5 domains more affecting their HRQOL, namely: "motor functionality," "mood," "cognition," "gait, balance, posture, and falls," and "nighttime sleep disorders." In early disease, PwP and CP considered "mood" the domain that most affected their HRQOL. In advanced PD, PwP considered "pain" the domain that most affects their HRQOL, while CP considered "psychiatric symptoms." The domain "gait, balance, posture, and falls" was equally considered by both PwP and CP as the second domain that most affects their HRQOL in the advanced stage of PD.

DISCUSSION

The ranking of the priority of symptoms is largely shared by PwP and CP, and motor symptom priorities dominate the full disease spectrum. However, the nonmotor symptom priorities shift according to the disease severity stage. Tailored care and research require that providers consider these shifting priorities and incorporate the "individual's voice" into treatment decisions.

The mediating role of psychological capital between post-traumatic growth and uncertainty in illness among patients with Parkinson's disease

With the application of positive psychology in health management, many studies have confirmed the close relationship between post-traumatic growth, psychological capital and uncertainty in illness. However, there is still a lack of attention to the positive psychology of patients with Parkinson's disease, and previous studies have not clarify the specific relationship. Therefore, the present study sought to explore the multiple mediating roles of psychological capital between post-traumatic growth and uncertainty in illness among patients with Parkinson's disease. We conducted a cross-sectional survey, a total of 268 patients with Parkinson's disease were investigated by the Post-Traumatic Growth Inventory, the Positive Psychological Capital Questionnaire and the Mishel Uncertainty in Illness Scale - Adults from November 2021 to June 2022 in the Parkinson's specialist outpatient department of three 3a-grade hospitals in Tianjin, China. The results showed that the score of post-traumatic growth was (51.78±17.872), the score of uncertainty in illness was (68.08±17.555), and the score of self-efficacy, resilience, hope, and optimism dimensions of psychological capital were (28.93±10.511), (28.79±11.553), (27.57±8.558) and (25.35±9.768). Post-traumatic growth was negatively correlated with uncertainty in illness and positively correlated with all four dimensions of psychological capital (p < 0.01), and uncertainty in illness was negatively correlated with all four dimensions of psychological capital (p < 0.01). Bootstrap test showed that the direct effect of post-traumatic growth on uncertainty in illness of patients with Parkinson's disease was not significant; the total indirect effect of psychological capital between post-traumatic growth and uncertainty in illness was significant. The mediating effects of resilience and hope were significant, accounting for 53.07% and 19.41% of the total indirect effects, respectively. Based on the research results, psychological capital played a completely mediating role between post-traumatic growth and uncertainty in illness of patients with Parkinson's disease. Post-traumatic growth affected uncertainty in illness through two dimensions: resilience and hope. Healthcare professionals should pay attention to the level of psychological capital of patients with Parkinson's disease and implement targeted positive psychological interventions focusing on the two dimensions of resilience and hope, to enhance post-traumatic growth and reduce the level of uncertainty in illness.