UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Predicting diagnosis of Parkinson's disease: A risk algorithm based on primary care presentations

Schrag, A; Anastasiou, Z; Ambler, G; Noyce, A; Walters, K; (2019) Predicting diagnosis of Parkinson's disease: A risk algorithm based on primary care presentations. Movement Disorders 10.1002/mds.27616. (In press). Green open access

[img]
Preview
Text
Noyce_Predicting diagnosis of Parkinson's disease. A risk algorithm based on primary care presentations.Proof.pdf - Published version

Download (401kB) | Preview

Abstract

BACKGROUND: Diagnosis of Parkinson's disease (PD) is typically preceded by nonspecific presentations in primary care. OBJECTIVES: The objective of this study was to develop and validate a prediction model for diagnosis of PD based on presentations in primary care. SETTING: The settings were general practices providing data for The Health Improvement Network UK primary care database. METHODS: Data from 8,166 patients aged older than age 50 years with incident diagnosis of PD and 46,755 controls were analyzed. Likelihood ratios, sensitivity, specificity, and positive and negative predictive values for individual symptoms and combinations of presentations were calculated. An algorithm for risk of diagnosis of PD within 5 years was calculated using multivariate logistic regression analysis. Split sample analysis was used for model validation with a 70% development sample and a 30% validation sample. RESULTS: Presentations independently and significantly associated with later diagnosis of PD in multivariate analysis were tremor, constipation, depression or anxiety, fatigue, dizziness, urinary dysfunction, balance problems, memory problems and cognitive decline, hypotension, rigidity, and hypersalivation. The discrimination and calibration of the risk algorithm were good with an area under the curve of 0.80 (95% confidence interval 0.78‐0.81). At a threshold of 5%, 37% of those classified as high risk would be diagnosed with PD within 5 years and 99% of those who were not classified as high risk would not be diagnosed with PD. CONCLUSION: This risk algorithm applied to routine primary care presentations can identify individuals at increased risk of diagnosis of PD within 5 years to allow for monitoring and earlier diagnosis of PD. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Type: Article
Title: Predicting diagnosis of Parkinson's disease: A risk algorithm based on primary care presentations
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/mds.27616
Publisher version: https://doi.org/10.1002/mds.27616
Language: English
Additional information: © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.. This is an open access article under the terms of the CreativeCommons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Keywords: algorithm, diagnosis, Parkinson's disease, prodromal, risk, risk calculator
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Primary Care and Population Health
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10067630
Downloads since deposit
75Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item