Xuehan Ren1, Jeffrey Lin2, Glenn T Stebbins3, Christopher G Goetz3, Sheng Luo4. 1. Department of Biostatistics, Gilead Sciences, Foster City, California, USA. 2. Department of Biostatistics, University of Texas Health Science Center at Houston, Houston, Texas, USA. 3. Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA. 4. Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Abstract
BACKGROUND: Predicting Parkinson's disease (PD) progression may enable better adaptive and targeted treatment planning. OBJECTIVE: Develop a prognostic model using multiple, easily acquired longitudinal measures to predict temporal clinical progression from Hoehn and Yahr (H&Y) stage 1 or 2 to stage 3 in early PD. METHODS: Predictive longitudinal measures of PD progression were identified by the joint modeling method. Measures were extracted by multivariate functional principal component analysis methods and used as covariates in Cox proportional hazards models. The optimal model was developed from the Parkinson's Progression Marker Initiative (PPMI) data set and confirmed with external validation from the Longitudinal and Biomarker Study in PD (LABS-PD) study. RESULTS: The proposed prognostic model with longitudinal information of selected clinical measures showed significant advantages in predicting PD temporal progression in comparison to a model with only baseline information (iAUC = 0.812 vs. 0.743). The modeling results allowed the development of a prognostic index for categorizing PD patients into low, mid, and high risk of progression to HY 3 that is offered to facilitate physician-patient discussion on prognosis. CONCLUSION: Incorporating longitudinal information of multiple clinical measures significantly enhances predictive performance of prognostic models. Furthermore, the proposed prognostic index enables clinicians to classify patients into different risk groups, which could be adaptively updated as new longitudinal information becomes available. Modeling of this type allows clinicians to utilize observational data sets that inform on disease natural history and specifically, for precision medicine, allows the insertion of a patient's clinical data to calculate prognostic estimates at the individual case level.
BACKGROUND: Predicting Parkinson's disease (PD) progression may enable better adaptive and targeted treatment planning. OBJECTIVE: Develop a prognostic model using multiple, easily acquired longitudinal measures to predict temporal clinical progression from Hoehn and Yahr (H&Y) stage 1 or 2 to stage 3 in early PD. METHODS: Predictive longitudinal measures of PD progression were identified by the joint modeling method. Measures were extracted by multivariate functional principal component analysis methods and used as covariates in Cox proportional hazards models. The optimal model was developed from the Parkinson's Progression Marker Initiative (PPMI) data set and confirmed with external validation from the Longitudinal and Biomarker Study in PD (LABS-PD) study. RESULTS: The proposed prognostic model with longitudinal information of selected clinical measures showed significant advantages in predicting PD temporal progression in comparison to a model with only baseline information (iAUC = 0.812 vs. 0.743). The modeling results allowed the development of a prognostic index for categorizing PD patients into low, mid, and high risk of progression to HY 3 that is offered to facilitate physician-patient discussion on prognosis. CONCLUSION: Incorporating longitudinal information of multiple clinical measures significantly enhances predictive performance of prognostic models. Furthermore, the proposed prognostic index enables clinicians to classify patients into different risk groups, which could be adaptively updated as new longitudinal information becomes available. Modeling of this type allows clinicians to utilize observational data sets that inform on disease natural history and specifically, for precision medicine, allows the insertion of a patient's clinical data to calculate prognostic estimates at the individual case level.
Authors: Jane S Paulsen; Jeffrey D Long; Christopher A Ross; Deborah L Harrington; Cheryl J Erwin; Janet K Williams; Holly James Westervelt; Hans J Johnson; Elizabeth H Aylward; Ying Zhang; H Jeremy Bockholt; Roger A Barker Journal: Lancet Neurol Date: 2014-11-03 Impact factor: 44.182
Authors: Angelo Antonini; Paolo Barone; Roberto Marconi; Letterio Morgante; Salvatore Zappulla; Francesco Ernesto Pontieri; Silvia Ramat; Maria Gabriella Ceravolo; Giuseppe Meco; Giulio Cicarelli; Massimo Pederzoli; Michela Manfredi; Roberto Ceravolo; Marco Mucchiut; Giampiero Volpe; Giovanni Abbruzzese; Edo Bottacchi; Luigi Bartolomei; Giuseppe Ciacci; Antonino Cannas; Maria Giovanna Randisi; Alfredo Petrone; Mario Baratti; Vincenzo Toni; Giovanni Cossu; Paolo Del Dotto; Anna Rita Bentivoglio; Michele Abrignani; Rossana Scala; Franco Pennisi; Rocco Quatrale; Rosa Maria Gaglio; Alessandra Nicoletti; Michele Perini; Tania Avarello; Antonio Pisani; Augusto Scaglioni; Paolo Emilio Martinelli; Francesco Iemolo; Laura Ferigo; Pasqualino Simone; Paola Soliveri; Biagio Troianiello; Domenico Consoli; Alessandro Mauro; Leonardo Lopiano; Giuseppe Nastasi; Carlo Colosimo Journal: J Neurol Date: 2012-06-19 Impact factor: 4.849
Authors: Samuel Iddi; Dan Li; Paul S Aisen; Michael S Rafii; Irene Litvan; Wesley K Thompson; Michael C Donohue Journal: Neurodegener Dis Date: 2018-08-08 Impact factor: 2.977
Authors: Jeanne C Latourelle; Michael T Beste; Tiffany C Hadzi; Robert E Miller; Jacob N Oppenheim; Matthew P Valko; Diane M Wuest; Bruce W Church; Iya G Khalil; Boris Hayete; Charles S Venuto Journal: Lancet Neurol Date: 2017-09-25 Impact factor: 44.182