ArticleJournal of multidisciplinary healthcare2024
Analysis of Health-Related Quality of Life in Elderly Patients with Stroke Complicated by Hypertension in China Using the EQ-5D-3L Scale.
Article in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Explainable Machine Learning for Risk Prediction of Reduced Quality of Life in Hypertension.Vascular health and risk management · 2026Article
- Smoking cessation improves health status of patients with chronic diseases: evidence from a longitudinal study of older adults in China.BMC public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate the health-related quality of life(HRQoL)status of elderly patients with hypertensive stroke, to understand the factors influencing it, and to provide a basis for the development of health intervention policies. Patients and Methods: This study used the EQ-5D-3L scale to assess the HRQoL among elderly patients who experienced a stroke related to high blood pressure. Various analytical methods were employed to examine the factors that influenced the patient's quality of life. Univariate analysis, Tobit regression, random forest, and XGBoost models were applied to analyze the HRQoL of the patients. Furthermore, to interpret the machine learning results, the SHAP method was utilized. This method involved assessing the importance of each feature, examining the effect of each feature on the prediction result of a single sample, and determining the impact of individual features on the overall prediction. Results: The study found that the median health utility value for elderly patients with hypertensive stroke was 0.427, with an interquartile range of 0.186 to 0.745. The results of the Tobit regression model, Random Forest, and XGBoost model were compared. The results of the model evaluation show that the performance of the machine learning model and the Tobit regression model are not very different. The XGBoost model performs slightly better relative to the random forest model. The factors that strongly influenced the health utility value of patients included BMI, social activities, smoking, education level, alcohol consumption, urban/rural residence, annual income, physical activity level, and hours of sleep at night. Conclusion: Health-related quality of life in hypertensive stroke patients is influenced by a variety of factors. Health-related quality of life can be positively influenced by modifying these factors and making lifestyle adjustments. Maintaining a healthy weight, being socially active, quitting smoking, improving living conditions, increasing physical activity levels and getting enough sleep are recommended. Lifestyle changes need to be developed for each individual on a case-by-case basis and by medical advice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.