It has been a quiet month on the blog, but it is a consequence of us working hard, rather than having too much leisure time 😇
In this post we are looking at some of our own work, led by Professor Anette Schrag from the PREDICT-PD study.
Here Anette and colleagues looked at routinely collected data from General Practice medical records. They identified >8000 people who had been diagnosed with Parkinson's and >46,000 people who had not been diagnosed with Parkinson's. Then they looked back in time (in the records) to build a risk score based on the various events that occurred prior to the diagnosis of Parkinson's. As we have reported previously on this blog, things like tremor, constipation, depression, and urinary problems (and many more things) contributed to the score. The scoring process worked well overall.
Of course we do something very similar in the PREDICT-PD, except here people take part online and answer questionnaires and complete online tests that all contribute to an individual score... we are actively recruiting and you can take part at predictpd.com.
- Alastair Noyce
Predicting diagnosis of Parkinson's disease: A risk algorithm based on primary care presentations.
Mov Disord. 2019 Feb 8. doi: 10.1002/mds.27616. [Epub ahead of print]
Schrag A, Anastasiou Z, Ambler G, Noyce A, Walters K.
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.
Percentage patients with Parkinson's and non-patients with various potential early symptoms.
© 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
