Observational studyInternational journal of environmental research and public health2022
Predictive Model of Quality of Life in Patients with Parkinson's Disease.
Observational study in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Patient With Parkinson Disease and Care Partner Perceptions of Key Domains Affecting Health-Related Quality of Life: Systematic ReviewNeurology · 2024Pooled it
- Predictive analysis of health-related quality of life trajectories in older patients with chronic pain based on explainable machine learning models.Health and quality of life outcomes · 2026Article
- Predicting Cognition and Affective Changes in Newly Diagnosed Parkinson's Disease Through Longitudinal Data-Driven Clustering.Journal of geriatric psychiatry and neurology · 2026Article
- Non-motor symptoms as critical predictors of quality of life in Parkinson's disease: a machine learning approach.Health and quality of life outcomes · 2025Observational
- Prediction of glycaemic control and quality of life in people with type 2 diabetes using glucose-lowering drugs with machine learning-The Maastricht study.Diabetes, obesity & metabolism · 2025Article
- Predicting Quality of Life in Parkinson's Disease: A Machine Learning Approach Employing Common Clinical Variables.Journal of clinical medicine · 2024Article
- Editorial: Motor interventions: balance and cognition in older individuals.Frontiers in human neuroscience · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease is a chronic, progressive, and disabling neurodegenerative disease which evolves until the end of life and triggers different mood and organic alterations that influence health-related quality of life. The objective of our study was to identify the factors that negatively impact the quality of life of patients with Parkinson's disease and construct a predictive model of health-related quality of life in these patients.
methodsAn analytical, prospective observational study was carried out, including Parkinson's patients at different stages in the Albacete Health Area. The sample consisted of 155 patients (T0) who were followed up at one (T1) and two years (T2). The instruments used were a purpose-designed data collection questionnaire and the "Parkinson's Disease Questionnaire" (PDQ-39), with a global index where a higher score indicates a worse quality of life. A multivariate analysis was performed by multiple linear regression at T0. Next, the model's predictive capacity was evaluated at T1 and T2 using the area under the ROC curve (AUROC).
resultsPredictive factors were: sex, living in a residence, using a cane, using a wheelchair, having a Parkinson's stage of HY > 2, having Alzheimer's disease or a major neurocognitive disorder, having more than five non-motor symptoms, polypharmacy, and disability greater than 66%. This model showed good predictive capacity at one year and two years of follow-up, with an AUROC of 0.89 (95% CI: 0.83-0.94) and 0.83 (95% CI: 0.76-0.89), respectively.
conclusionsA predictive model constructed with nine variables showed a good discriminative capacity to predict the quality of life of patients with Parkinson's disease at one and two years of follow-up.
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