MDS research criteria for prodromal Parkinson's disease
ABSTRACT This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early symptoms or signs of PD neurodegeneration are present, but classic clinical diagnosis based on fully evolved motor parkinsonism is not...
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| Vydáno v: | Movement disorders Ročník 30; číslo 12; s. 1600 - 1611 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Blackwell Publishing Ltd
01.10.2015
Wiley Subscription Services, Inc |
| Témata: | |
| ISSN: | 0885-3185, 1531-8257, 1531-8257 |
| On-line přístup: | Získat plný text |
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| Abstract | ABSTRACT
This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early symptoms or signs of PD neurodegeneration are present, but classic clinical diagnosis based on fully evolved motor parkinsonism is not yet possible. Given the lack of clear neuroprotective/disease‐modifying therapy for prodromal PD, these criteria were developed for research purposes only. The criteria are based upon the likelihood of prodromal disease being present with probable prodromal PD defined as ≥80% certainty. Certainty estimates rely upon calculation of an individual's risk of having prodromal PD, using a Bayesian naïve classifier. In this methodology, a previous probability of prodromal disease is delineated based upon age. Then, the probability of prodromal PD is calculated by adding diagnostic information, expressed as likelihood ratios. This diagnostic information combines estimates of background risk (from environmental risk factors and genetic findings) and results of diagnostic marker testing. In order to be included, diagnostic markers had to have prospective evidence documenting ability to predict clinical PD. They include motor and nonmotor clinical symptoms, clinical signs, and ancillary diagnostic tests. These criteria represent a first step in the formal delineation of early stages of PD and will require constant updating as more information becomes available. © 2015 International Parkinson and Movement Disorder Society |
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| AbstractList | This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early symptoms or signs of PD neurodegeneration are present, but classic clinical diagnosis based on fully evolved motor parkinsonism is not yet possible. Given the lack of clear neuroprotective/disease-modifying therapy for prodromal PD, these criteria were developed for research purposes only. The criteria are based upon the likelihood of prodromal disease being present with probable prodromal PD defined as ≥80% certainty. Certainty estimates rely upon calculation of an individual's risk of having prodromal PD, using a Bayesian naïve classifier. In this methodology, a previous probability of prodromal disease is delineated based upon age. Then, the probability of prodromal PD is calculated by adding diagnostic information, expressed as likelihood ratios. This diagnostic information combines estimates of background risk (from environmental risk factors and genetic findings) and results of diagnostic marker testing. In order to be included, diagnostic markers had to have prospective evidence documenting ability to predict clinical PD. They include motor and nonmotor clinical symptoms, clinical signs, and ancillary diagnostic tests. These criteria represent a first step in the formal delineation of early stages of PD and will require constant updating as more information becomes available.This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early symptoms or signs of PD neurodegeneration are present, but classic clinical diagnosis based on fully evolved motor parkinsonism is not yet possible. Given the lack of clear neuroprotective/disease-modifying therapy for prodromal PD, these criteria were developed for research purposes only. The criteria are based upon the likelihood of prodromal disease being present with probable prodromal PD defined as ≥80% certainty. Certainty estimates rely upon calculation of an individual's risk of having prodromal PD, using a Bayesian naïve classifier. In this methodology, a previous probability of prodromal disease is delineated based upon age. Then, the probability of prodromal PD is calculated by adding diagnostic information, expressed as likelihood ratios. This diagnostic information combines estimates of background risk (from environmental risk factors and genetic findings) and results of diagnostic marker testing. In order to be included, diagnostic markers had to have prospective evidence documenting ability to predict clinical PD. They include motor and nonmotor clinical symptoms, clinical signs, and ancillary diagnostic tests. These criteria represent a first step in the formal delineation of early stages of PD and will require constant updating as more information becomes available. This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early symptoms or signs of PD neurodegeneration are present, but classic clinical diagnosis based on fully evolved motor parkinsonism is not yet possible. Given the lack of clear neuroprotective/disease-modifying therapy for prodromal PD, these criteria were developed for research purposes only. The criteria are based upon the likelihood of prodromal disease being present with probable prodromal PD defined as greater than or equal to 80% certainty. Certainty estimates rely upon calculation of an individual's risk of having prodromal PD, using a Bayesian naive classifier. In this methodology, a previous probability of prodromal disease is delineated based upon age. Then, the probability of prodromal PD is calculated by adding diagnostic information, expressed as likelihood ratios. This diagnostic information combines estimates of background risk (from environmental risk factors and genetic findings) and results of diagnostic marker testing. In order to be included, diagnostic markers had to have prospective evidence documenting ability to predict clinical PD. They include motor and nonmotor clinical symptoms, clinical signs, and ancillary diagnostic tests. These criteria represent a first step in the formal delineation of early stages of PD and will require constant updating as more information becomes available. copyright 2015 International Parkinson and Movement Disorder Society This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early symptoms or signs of PD neurodegeneration are present, but classic clinical diagnosis based on fully evolved motor parkinsonism is not yet possible. Given the lack of clear neuroprotective/disease-modifying therapy for prodromal PD, these criteria were developed for research purposes only. The criteria are based upon the likelihood of prodromal disease being present with probable prodromal PD defined as ≥80% certainty. Certainty estimates rely upon calculation of an individual's risk of having prodromal PD, using a Bayesian naïve classifier. In this methodology, a previous probability of prodromal disease is delineated based upon age. Then, the probability of prodromal PD is calculated by adding diagnostic information, expressed as likelihood ratios. This diagnostic information combines estimates of background risk (from environmental risk factors and genetic findings) and results of diagnostic marker testing. In order to be included, diagnostic markers had to have prospective evidence documenting ability to predict clinical PD. They include motor and nonmotor clinical symptoms, clinical signs, and ancillary diagnostic tests. These criteria represent a first step in the formal delineation of early stages of PD and will require constant updating as more information becomes available. © 2015 International Parkinson and Movement Disorder Society ABSTRACT This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early symptoms or signs of PD neurodegeneration are present, but classic clinical diagnosis based on fully evolved motor parkinsonism is not yet possible. Given the lack of clear neuroprotective/disease‐modifying therapy for prodromal PD, these criteria were developed for research purposes only. The criteria are based upon the likelihood of prodromal disease being present with probable prodromal PD defined as ≥80% certainty. Certainty estimates rely upon calculation of an individual's risk of having prodromal PD, using a Bayesian naïve classifier. In this methodology, a previous probability of prodromal disease is delineated based upon age. Then, the probability of prodromal PD is calculated by adding diagnostic information, expressed as likelihood ratios. This diagnostic information combines estimates of background risk (from environmental risk factors and genetic findings) and results of diagnostic marker testing. In order to be included, diagnostic markers had to have prospective evidence documenting ability to predict clinical PD. They include motor and nonmotor clinical symptoms, clinical signs, and ancillary diagnostic tests. These criteria represent a first step in the formal delineation of early stages of PD and will require constant updating as more information becomes available. © 2015 International Parkinson and Movement Disorder Society This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early symptoms or signs of PD neurodegeneration are present, but classic clinical diagnosis based on fully evolved motor parkinsonism is not yet possible. Given the lack of clear neuroprotective/disease-modifying therapy for prodromal PD, these criteria were developed for research purposes only. The criteria are based upon the likelihood of prodromal disease being present with probable prodromal PD defined as ≥80% certainty. Certainty estimates rely upon calculation of an individual's risk of having prodromal PD, using a Bayesian naïve classifier. In this methodology, a previous probability of prodromal disease is delineated based upon age. Then, the probability of prodromal PD is calculated by adding diagnostic information, expressed as likelihood ratios. This diagnostic information combines estimates of background risk (from environmental risk factors and genetic findings) and results of diagnostic marker testing. In order to be included, diagnostic markers had to have prospective evidence documenting ability to predict clinical PD. They include motor and nonmotor clinical symptoms, clinical signs, and ancillary diagnostic tests. These criteria represent a first step in the formal delineation of early stages of PD and will require constant updating as more information becomes available. |
| Author | Berg, Daniela Postuma, Ronald B. Goetz, Christopher G. Chan, Piu Litvan, Irene Dubois, Bruno Olanow, C. Warren Halliday, Glenda Liepelt-Scarfone, Inga Oertel, Wolfgang Deuschl, Günther Lang, Anthony E. Poewe, Werner Joseph, Lawrence Stern, Matthew Bloem, Bastiaan R. Gasser, Thomas Obeso, José Marek, Kenneth Adler, Charles H. |
| Author_xml | – sequence: 1 givenname: Daniela surname: Berg fullname: Berg, Daniela email: ron.postuma@mcgill.ca organization: Department of Neurodegeneration, Hertie-Institute for Clinical Brain Research and German Center for Neurodegenerative Diseases, Tuebingen, Germany – sequence: 2 givenname: Ronald B. surname: Postuma fullname: Postuma, Ronald B. email: ron.postuma@mcgill.ca organization: Department of Neurology, Montreal General Hospital, Quebec, Montreal, Canada – sequence: 3 givenname: Charles H. surname: Adler fullname: Adler, Charles H. organization: The Parkinson's Disease and Movement Disorders Center, Department of Neurology, Mayo Clinic, Arizona, Scottsdale, USA – sequence: 4 givenname: Bastiaan R. surname: Bloem fullname: Bloem, Bastiaan R. organization: Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands – sequence: 5 givenname: Piu surname: Chan fullname: Chan, Piu organization: Xuanwu Hospital of Capitol of Medical University, Beijing, China – sequence: 6 givenname: Bruno surname: Dubois fullname: Dubois, Bruno organization: Hopital De La Salpetriere, Paris, France – sequence: 7 givenname: Thomas surname: Gasser fullname: Gasser, Thomas organization: Department of Neurodegeneration, Hertie-Institute for Clinical Brain Research and German Center for Neurodegenerative Diseases, Tuebingen, Germany – sequence: 8 givenname: Christopher G. surname: Goetz fullname: Goetz, Christopher G. organization: Rush University Medical Center, Illinois, Chicago, USA – sequence: 9 givenname: Glenda surname: Halliday fullname: Halliday, Glenda organization: Neuroscience Research Australia & University of NSW, Randwick, Australia – sequence: 10 givenname: Lawrence surname: Joseph fullname: Joseph, Lawrence organization: Department of Epidemiology and Biostatistics, McGill University, Quebec, Montreal, Canada – sequence: 11 givenname: Anthony E. surname: Lang fullname: Lang, Anthony E. organization: Division of Neurology, Toronto Western Hospital, Ontario, Toronto, Canada – sequence: 12 givenname: Inga surname: Liepelt-Scarfone fullname: Liepelt-Scarfone, Inga organization: Department of Neurodegeneration, Hertie-Institute for Clinical Brain Research and German Center for Neurodegenerative Diseases, Tuebingen, Germany – sequence: 13 givenname: Irene surname: Litvan fullname: Litvan, Irene organization: Department of Neurosciences, University of California San Diego, California, La Jolla, USA – sequence: 14 givenname: Kenneth surname: Marek fullname: Marek, Kenneth organization: Institute for Neurodegenerative Disorders, Connecticut, New Haven, USA – sequence: 15 givenname: José surname: Obeso fullname: Obeso, José organization: University of Navarra-FIMA, Pamplona, Spain – sequence: 16 givenname: Wolfgang surname: Oertel fullname: Oertel, Wolfgang organization: Department of Neurology, Philipps University of Marburg, Marburg, Germany – sequence: 17 givenname: C. Warren surname: Olanow fullname: Olanow, C. Warren organization: Department of Neurology, The Mount Sinai Hospital, New York, New York, USA – sequence: 18 givenname: Werner surname: Poewe fullname: Poewe, Werner organization: Department of Neurology, Innsbruck Medical University, Innsbruck, Austria – sequence: 19 givenname: Matthew surname: Stern fullname: Stern, Matthew organization: Penn Neurological Institute, Pennsylvania, Philadelphia, USA – sequence: 20 givenname: Günther surname: Deuschl fullname: Deuschl, Günther organization: Department of Neurology, Christian-Albrechts University, Kiel, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26474317$$D View this record in MEDLINE/PubMed |
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| CODEN | MOVDEA |
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This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein... This article describes research criteria and probability methodology for the diagnosis of prodromal PD. Prodromal disease refers to the stage wherein early... |
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| SubjectTerms | diagnosis Humans Movement disorders Parkinson Disease - diagnosis Parkinson's disease prodromal Prodromal Symptoms Severity of Illness Index Societies, Scientific - standards |
| Title | MDS research criteria for prodromal Parkinson's disease |
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