1. Parkinson's disease diagnosis codes are insufficiently accurate for electronic health record research and differ by race.
- Author
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Hill EJ, Sharma J, Wissel B, Sawyer RP, Jiang M, Marsili L, Duque K, Botsford V, Wood C, DeLano K, Sun Q, Kissela B, and Espay AJ
- Subjects
- Humans, Electronic Health Records, International Classification of Diseases, Databases, Factual, Parkinson Disease diagnosis, Parkinson Disease epidemiology, Parkinsonian Disorders
- Abstract
Background: There are no evidence-based guidelines for data cleaning of electronic health record (EHR) databases in Parkinson's disease (PD). Previous filtering criteria have primarily used the 9th International Statistical Classification of Diseases and Related Health Problems (ICD) with variable accuracy for true PD cases. Prior studies have not excluded atypical or drug-induced parkinsonism, and little is known about differences in accuracy by race., Objective: To determine if excluding parkinsonism diagnoses improves accuracy of ICD-9 and -10 PD diagnosis codes., Methods: We included ≥2 instances of an ICD-9 and/or -10 code for PD. We removed any records with at least one code indicating atypical or drug-induced parkinsonism first in all races, and then in Non-Hispanic White and Black patients. We manually reviewed 100 randomly selected charts per group before and after filtering, and performed a test of proportion (null hypothesis 0.5) for confirmed PD., Results: 5633 records had ≥2 instances of a PD code. 2833 remained after filtering. The rate of true PD cases was low before and after filtering to remove parkinsonism codes (0.55 vs. 0.51, p = 0.84). Accuracy was lowest in Black patients before filtering (0.48, p = 0.69), but filtering had a greater (though modest) impact on accuracy (0.68, p < 0.001)., Conclusions: There was inadequate accuracy of PD diagnosis codes in the largest study of ICD-9 and -10 codes. Accuracy was lowest in Black patients but improved the most with removing other parkinsonism codes. This highlights the limitations of using current real-world EHR data in PD research and need for further study., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Acknowledgements and Financial Disclosures: Dr. Hill was supported by the CT2 scholarship through the CCTST at the University of Cincinnati. The CCTST is funded by the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program, grant UL1TR001425. The CTSA program is led by the NIH’s National Center for Advancing Translational Sciences (NCATS). The content of this website is solely the responsibility of the CCTST and does not necessarily represent the official views of the NIH. Dr. Sawyer was supported by institutional grants from the University of Cincinnati (Center for Environmental Genetics, Clinical Research Feasibility Fund/Schubert Research Clinic, University of Cincinnati College of Medicine Institute Pilot Program). Dr. Kissela has received research support from NIH/NINDS and NCATS. Dr. Espay has received personal compensation as a consultant/scientific advisory board member for Neuroderm, Neurocrine, Amneal, Acadia, Acorda, Bexion, Kyowa Kirin, Sunovion, Supernus (formerly, USWorldMeds), Avion Pharmaceuticals, and Herantis Pharma. He is co-owner of a patent that covers synthetic soluble nonaggregating peptide analogs as replacement treatments in proteinopathies. Dr. Marsili has received honoraria from the International Association of Parkinsonism and Related Disorders (IAPRD) Society for social media and web support. Drs. Sharma, Wissel, Duque, Botsford, and Wood as well as Ms. Delano, Ms. Sun, and Ms. Jiang report no disclosures., (Copyright © 2023. Published by Elsevier Ltd.)
- Published
- 2023
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