Parkinson's disease diagnosis codes are insufficiently accurate for electronic health record research and differ by race
Copyright © 2023. Published by Elsevier Ltd..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:114 |
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| Enthalten in: |
Parkinsonism & related disorders - 114(2023) vom: 07. Sept., Seite 105764 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hill, Emily J [VerfasserIn] |
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| Anmerkungen: |
Date Completed 11.09.2023 Date Revised 11.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.parkreldis.2023.105764 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 100 | 1 | |a Hill, Emily J |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Parkinson's disease diagnosis codes are insufficiently accurate for electronic health record research and differ by race |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Revised 11.09.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023. Published by Elsevier Ltd. | ||
| 520 | |a 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 | ||
| 520 | |a OBJECTIVE: To determine if excluding parkinsonism diagnoses improves accuracy of ICD-9 and -10 PD diagnosis codes | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 700 | 1 | |a Sharma, Jennifer |e verfasserin |4 aut | |
| 700 | 1 | |a Wissel, Benjamin |e verfasserin |4 aut | |
| 700 | 1 | |a Sawyer, Russell P |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Megan |e verfasserin |4 aut | |
| 700 | 1 | |a Marsili, Luca |e verfasserin |4 aut | |
| 700 | 1 | |a Duque, Kevin |e verfasserin |4 aut | |
| 700 | 1 | |a Botsford, Vanesa |e verfasserin |4 aut | |
| 700 | 1 | |a Wood, Christopher |e verfasserin |4 aut | |
| 700 | 1 | |a DeLano, Kelly |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Qin |e verfasserin |4 aut | |
| 700 | 1 | |a Kissela, Brett |e verfasserin |4 aut | |
| 700 | 1 | |a Espay, Alberto J |e verfasserin |4 aut | |
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