# Refining the diagnostic accuracy of Parkinsonian disorders using metaphenomic annotation of the clinicopathological literature

## TL;DR

This study improves the accuracy of diagnosing Parkinsonian disorders by analyzing clinicopathological data and building a probabilistic model.

## Contribution

The study introduces a probabilistic model and harmonized dataset to better diagnose Parkinsonian disorders using clinical observations.

## Key findings

- Diagnostic accuracy is highest for multiple system atrophy (92.8%) and lowest for dementia with Lewy bodies (82.1%).
- MSA and progressive supranuclear palsy are often misdiagnosed as Parkinson’s disease.
- Likelihood ratios for clinical phenotypes can refine diagnostic accuracy.

## Abstract

The diagnostic precision of Parkinsonian disorders is not accurate enough. Even in expert clinics, up to one in five diagnoses are incorrect. Gold standard diagnosis is post-mortem confirmation of the underlying proteinopathy; however, many clinicopathological studies focus on either a single disease or frame analyses in one temporal direction that may underestimate the true extent of mis- and missed diagnoses. We identified 125 published clinicopathological studies since 1992, extracted phenotype information for ~9200 post-mortem cases, curated the data in a standardised machine-readable format and used this to develop a probabilistic model to quantify diagnostic likelihood based on clinical observations. We found diagnostic accuracy was highest for multiple system atrophy (MSA, 92.8%) and lowest for dementia with Lewy bodies (DLB, 82.1%). MSA and progressive supranuclear palsy were most frequently mis-labelled as Parkinson’s disease (PD) in life (7.2% and 8.3% of cases), whereas the most common PD misdiagnosis was Alzheimer’s (~7% cases). We calculated likelihood ratios for a large range of clinical phenotypes and demonstrated how these can be used to help refine and improve diagnostic accuracy. This work delivers a harmonised, open-source dataset representing over 30 years of published results and represents a key foundation for flexible predictive models that leverage different sources of information to better discriminate Parkinsonian disorders during the early and prodromal phases of the illness.