Health and Quality of Life Outcomes最新文献

Background: Parkinson's disease (PD) considerably impacts health-related quality of life (HRQoL) through motor and non-motor symptoms. The Parkinson's Disease Questionnaire-39 (PDQ-39) is the most widely used tool to assess HRQoL, encompassing eight dimensions and a Summary Index providing an overall score. Despite advances in machine learning (ML) for predicting disease symptoms and progression, its application to predict HRQoL across these dimensions remains underexplored.

Methods: This study uses complete-case data for 478 of 861 patients from PRISM, a cross-sectional observational survey conducted in six European countries in 2018-2019. Participants were adults with PD recruited through advocacy groups and clinical centers who completed online assessments, providing data on demographics, medication, comorbidities, and disease characteristics (Tolosa et al., 2021). ML models were trained to predict PDQ-39 dimensions and Summary Index scores (0-100; higher = worse HRQoL). Features were preselected using the Boruta algorithm on the training data. Model selection was based on the lowest mean RMSE from 100 bootstrap resamples on the training set. Selected models were then retrained using 1000 bootstrap resamples for robust performance estimation. Final performance was evaluated on a held-out 20% validation set using R², MAE, and RMSE. Feature importance was assessed using permutation importance with MAE loss (100 permutations) on the held-out validation set. Factor Analysis of Mixed Data (FAMD) was used to explore patterns between non-motor symptoms and PDQ-39.

Results: Selected models: xgbTree (Summary Index; Activities of Daily Living) and gaussprPoly (all other PDQ-39 dimensions). On the validation set, Summary Index/ Cognitions showed the strongest performance with R² = 0.56/0.53, MAE = 9.60/12.39, RMSE = 12.66/16.20. Permutation feature importance ranked the Non-Motor Symptoms Questionnaire score (sum of 30 non-motor symptoms, range 0-30) as the most important predictor across all models. FAMD showed clustering of Social Support, Bodily Discomfort, and Stigma dimensions with Anxiety.

Conclusions: Our findings demonstrate the critical role of non-motor symptoms in predicting HRQoL in patients with PD. While ML models effectively predict overall HRQoL and cognitive aspects, achieving comparable performance on other dimensions may require additional variables to reduce error. These insights emphasize comprehensive treatment strategies addressing both motor and non-motor symptoms.

Background: Postoperative symptom burden is considerable and markedly undermines the quality of life of esophageal cancer (EC) survivors. This study aimed to examine symptom clusters and the interrelationships among symptoms in postoperative EC survivors, with the goal of identifying core symptoms.

Methods: A cross-sectional study was conducted using the European Cancer Life Questionnaire and the EC-Specific Supplementary Questionnaire. EC survivors were recruited in Shandong between February 2023 and February 2024. Principal component analysis (PCA) was utilized to identify symptom clusters, while Gaussian graphical network models were used to estimate the network structure.

Results: A total of 460 EC survivors were included in the study, revealing three distinct symptom clusters: the reflux-dysphagia cluster, the respiratory-related symptom cluster, and the recovery-fatigue cluster. The final network model demonstrated interconnections among these symptoms. "Fatigue" (FA) exhibited the highest strength centrality, identifying it as the most prominent core symptom in the network. "Emotional functioning" (EF), "Fatigue" (FA), and "cognitive functioning" (CF) ranked highest in terms of bridge strengths. Additionally, the model showed excellent network stability.

Conclusions: EC survivors experienced significant postoperative symptom burden, with symptom network analysis revealing the complex interrelations among postoperative symptoms. This approach also identified core symptoms that play a crucial role in the network. Fatigue emerged as the most influential core symptom, highlighting the significance of targeted interventions to mitigate negative symptom interactions and improve quality of life.