Accuracy of clinical diagnosis of Parkinson disease
A systematic review and meta-analysis
Giovanni Rizzo, MD, Massimiliano Copetti, PhD, Simona Arcuti, PhD, Davide Martino, MD, Andrea Fontana, MScand Giancarlo Logroscino, MD
Correspondence to Dr. Logroscino: firstname.lastname@example.org
Neurology February 9, 2016 vol. 86 no. 6 566-576
Objective: To evaluate the diagnostic accuracy of clinical diagnosis of Parkinson disease (PD) reported in the last 25 years by a systematic review and meta-analysis.
Methods: We searched for articles published between 1988 and August 2014. Studies were included if reporting diagnostic parameters regarding clinical diagnosis of PD or crude data. The selected studies were subclassified based on different study setting, type of test diagnosis, and gold standard. Bayesian meta-analyses of available data were performed.
Results: We selected 20 studies, including 11 using pathologic examination as gold standard. Considering only these 11 studies, the pooled diagnostic accuracy was 80.6% (95% credible interval [CrI] 75.2%–85.3%). Accuracy was 73.8% (95% CrI 67.8%–79.6%) for clinical diagnosis performed mainly by nonexperts. Accuracy of clinical diagnosis performed by movement disorders experts rose from 79.6% (95% CrI 46%–95.1%) of initial assessment to 83.9% (95% CrI 69.7%–92.6%) of refined diagnosis after follow-up. Using UK Parkinson’s Disease Society Brain Bank Research Center criteria, the pooled diagnostic accuracy was 82.7% (95% CrI 62.6%–93%).
Conclusion: The overall validity of clinical diagnosis of PD is not satisfying. The accuracy did not significantly improve in the last 25 years, particularly in the early stages of disease, where response to dopaminergic treatment is less defined and hallmarks of alternative diagnoses such as a typical parkinsonism may not have emerged. Misclassification rate should be considered to calculate the sample size both in observational studies and randomized controlled trials. Imaging and biomarkers are urgently needed to improve the accuracy of clinical diagnosis in vivo.