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A simple-to-use web-based calculator for survival prediction in Parkinson’s disease

Background: To establish and validate a nomogram and corresponding web-based calculator to predict the survival of patients with Parkinson’s disease (PD). Methods: In this cohort study, we retrospectively evaluated patients (n=497) with PD using a two-stage design, from March 2004 to November 2007 a...

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Detalles Bibliográficos
Autores principales: Tang, Yunliang, Wang, Jiao, Chen, Gengfa, Ye, Wen, Yan, Nao, Feng, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7950310/
https://www.ncbi.nlm.nih.gov/pubmed/33535176
http://dx.doi.org/10.18632/aging.202443
Descripción
Sumario:Background: To establish and validate a nomogram and corresponding web-based calculator to predict the survival of patients with Parkinson’s disease (PD). Methods: In this cohort study, we retrospectively evaluated patients (n=497) with PD using a two-stage design, from March 2004 to November 2007 and from July 2005 to July 2015. Predictive variables included in the model were identified by univariate and multiple Cox proportional hazard analyses in the training set. Results: Independent prognostic factors including age, PD duration, and Hoehn and Yahr stage were determined and included in the model. The model showed good discrimination power with the area under the curve (AUC) values generated to predict 4-, 6-, and 8-year survival in the training set being 0.716, 0.783, and 0.814, respectively. In the validation set, the AUCs of 4- and 6-year survival predictions were 0.85 and 0.924, respectively. Calibration plots and decision curve analysis showed good model performance both in the training and validation sets. For convenient application, we established a web-based calculator (https://tangyl.shinyapps.io/PDprognosis/). Conclusions: We developed a satisfactory, simple-to-use nomogram and corresponding web-based calculator based on three relevant factors to predict prognosis and survival of patients with PD. This model can aid personalized treatment and clinical decision-making.