Background: Cost-effectiveness models in Parkinson's disease often include health states based on Hoehn and Yahr (H&Y) and time in 'off'. Few studies have investigated utilities in these health states.
Objective: The aim of this study was firstly to explore utilities in health states based on H&Y and off-time, and secondly to investigate to what extent H&Y and off-time correlated with EQ-5D dimensions.
Methods: Patients with idiopathic Parkinson's disease in the National Parkinson's Disease Patient Registry (PARKreg) in Sweden with observations of EQ-5D-3L, H&Y and off-time were included. Correlations with EQ-5D dimensions were analyzed. The relationship between the EQ-5D-3L and H&Y and off-time were estimated by a linear mixed-model with random intercept.
Results: Among patients in PARKreg, 1823 observations fulfilled inclusion criteria. The dimensions 'self-care', 'mobility' and 'usual activities' correlated moderately with H&Y (r = 0.45, r = 0.46, r = 0.45). Weak correlations were found for 'anxiety/depression' and 'pain/discomfort' (r = 0.24, r = 0.22) (p values < 0.001). All dimensions correlated weakly with off-time. The fitted model included H&Y, time in 'off', and sex. All H&Y stages were found to be significant and had large and monotonous impact on EQ-5D. Off-time was not significant, but improved the model goodness of fit. Predicted values ranged from 0.733 to - 0.106.
Conclusion: This study provides utilities for health states reflecting the current modeling practice of interventions targeting motor symptoms in Parkinson's disease. Future research should investigate patient utilities in health states that also capture non-motor symptoms of the disease, as the management of and options for treatments targeting these symptoms increases.
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http://dx.doi.org/10.1007/s40273-021-01056-z | DOI Listing |
Ann Intern Med
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Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore; and Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland (T.M.B.).
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JMIR Form Res
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View Article and Find Full Text PDFJ Med Internet Res
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Department of Epidemiology, Boston University School of Public Health, Boston University, Boston, MA, United States.
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J Med Internet Res
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Unitat de Recerca i Innovació, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain.
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J Med Internet Res
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Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
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