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Related Concept Videos

Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of its...
Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is to...
Parkinson Disease l: Introduction01:24

Parkinson Disease l: Introduction

Parkinson’s disease is a chronic, progressive neurodegenerative disorder that primarily affects movement. It is characterized by motor symptoms such as resting tremors, muscle rigidity, bradykinesia (slowness of movement), and postural instability. Patients may notice hand tremors at rest, stiffness during movement, or a shuffling gait. In addition to motor features, non-motor symptoms include sleep disturbances, mood and behavioral changes, constipation, and cognitive impairment, all of which...
Parkinson Disease ll: Pathophysiology01:24

Parkinson Disease ll: Pathophysiology

Parkinson disease (PD) is a progressive neurodegenerative disorder primarily affecting movement, with additional non-motor features. Its pathophysiology involves complex interactions among genetic susceptibility, environmental exposures, and cellular dysfunction, including dopaminergic neuron loss, protein aggregation, and mitochondrial impairment.Selective NeurodegenerationA key feature is the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to reduced...

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Related Experiment Video

Updated: Jun 30, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Optimizing algorithms to identify Parkinson's disease cases within an administrative database.

Nicholas R Szumski1, Eric M Cheng

  • 1Department of Neurology, UCLA, Los Angeles, California 90095, USA. nszumski@mednet.ucla.edu

Movement Disorders : Official Journal of the Movement Disorder Society
|September 26, 2008
PubMed
Summary
This summary is machine-generated.

Identifying Parkinson's disease (PD) cases in administrative databases is challenging. Algorithms using diagnostic codes, clinic specialty, and prescriptions improve accuracy over single codes, aiding research.

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Last Updated: Jun 30, 2026

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Published on: September 26, 2019

Area of Science:

  • Health Informatics
  • Neurology
  • Data Science

Background:

  • Administrative databases are crucial for health research but can misclassify diagnoses.
  • Accurate identification of Parkinson's disease (PD) cases is essential for epidemiological studies and clinical research.
  • The International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code for PD may not always reflect a true diagnosis due to data errors.

Purpose of the Study:

  • To develop and evaluate algorithms for improving the accuracy of identifying Parkinson's disease (PD) cases in administrative healthcare databases.
  • To compare the performance of different algorithmic approaches in predicting the confirmed working diagnosis of PD.

Main Methods:

  • A retrospective chart review was conducted on 577 patients with the PD diagnostic code (332.0).
  • Algorithms were developed using diagnostic codes, clinic specialty, and medication data to predict the working diagnosis.
  • A tiered consensus algorithm prioritizing specialist data was evaluated, with and without medication prescription criteria.

Main Results:

  • Chart review confirmed 75.6% of patients had PD or Possibly PD, while 24.4% did not.
  • The tiered algorithm using specialist data improved positive predictive value (PPV) to 83.2%.
  • Adding PD prescription data further increased PPV to 88.2% but decreased sensitivity.

Conclusions:

  • Algorithmic approaches significantly outperform single diagnostic codes for identifying PD cases in administrative databases.
  • Algorithm modifications can be tailored to optimize specific parameters like PPV or sensitivity based on research needs.
  • Improved case identification facilitates more precise research on Parkinson's disease using large datasets.