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Published in: Journal of Neurology 9/2018

Open Access 01-09-2018 | Original Communication

The accuracy of the clinical diagnosis of Parkinson disease. The HUNT study

Authors: Eldbjørg Hustad, Anne Heidi Skogholt, Kristian Hveem, Jan O. Aasly

Published in: Journal of Neurology | Issue 9/2018

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Abstract

Diagnostic accuracy is crucial not only for prognostic and therapeutic reasons, but also for epidemiologic studies. We aimed to study the accuracy of the clinical diagnosis of Parkinson disease (PD) for participants in The Nord-Trøndelag Health Study (HUNT), a health survey, containing data from approximately 126,000 individuals and biological material from 80,000 individuals. We included 980 participants from the HUNT study diagnosed with PD or secondary parkinsonism/related parkinsonian disorders. The participants had been diagnosed in conjunction with admission to hospitals in Trøndelag or through out-patient examination. We validated the diagnosis of PD by reviewing available Electronic Health Records (EHRs) using the MDS Clinical Diagnostic Criteria as gold standard. In total 61% (601/980) of the participants had available EHRs and were selected for validation. Out of those, 92% (550/601) had been diagnosed with PD while 8% (51/601) had been diagnosed with secondary parkinsonism/related parkinsonian disorders. The main outcome measure was the accuracy of the clinical diagnosis of PD for participants in the HUNT study. We verified PD in 65% (358/550) and excluded PD in 35% (192/550) of the participants. According to our results, the overall quality of the clinical diagnosis of PD for participants in the HUNT study is not optimal. Quality assurance of ICD codes entered into health registers is crucial before biological material obtained from these populations can be used in the search of new biomarkers for PD.
Literature
1.
go back to reference Braak H et al (2003) Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 24(2):197–211CrossRefPubMed Braak H et al (2003) Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 24(2):197–211CrossRefPubMed
2.
go back to reference Rizzo G et al (2016) Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology 86(6):566–576CrossRefPubMed Rizzo G et al (2016) Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology 86(6):566–576CrossRefPubMed
3.
go back to reference Roberts CL et al (2008) The accuracy of reporting of the hypertensive disorders of pregnancy in population health data. Hypertens Pregnancy 27(3):285–297CrossRefPubMedPubMedCentral Roberts CL et al (2008) The accuracy of reporting of the hypertensive disorders of pregnancy in population health data. Hypertens Pregnancy 27(3):285–297CrossRefPubMedPubMedCentral
5.
go back to reference Tolosa E, Wenning G, Poewe W (2006) The diagnosis of Parkinson’s disease. Lancet Neurol 5(1):75–86CrossRefPubMed Tolosa E, Wenning G, Poewe W (2006) The diagnosis of Parkinson’s disease. Lancet Neurol 5(1):75–86CrossRefPubMed
6.
go back to reference Jankovic J (2008) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79(4):368–376CrossRefPubMed Jankovic J (2008) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79(4):368–376CrossRefPubMed
7.
go back to reference Postuma RB et al (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30(12):1591–1601CrossRefPubMed Postuma RB et al (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30(12):1591–1601CrossRefPubMed
8.
go back to reference Mikkelsen G, Aasly J (2003) Narrative electronic patient records as source of discharge diagnoses. Comput Methods Programs Biomed 71(3):261–268CrossRefPubMed Mikkelsen G, Aasly J (2003) Narrative electronic patient records as source of discharge diagnoses. Comput Methods Programs Biomed 71(3):261–268CrossRefPubMed
9.
go back to reference Mikkelsen G, Aasly J (2005) Consequences of impaired data quality on information retrieval in electronic patient records. Int J Med Inform 74(5):387–394CrossRefPubMed Mikkelsen G, Aasly J (2005) Consequences of impaired data quality on information retrieval in electronic patient records. Int J Med Inform 74(5):387–394CrossRefPubMed
10.
go back to reference Mikkelsen G, Aasly J (2001) Concordance of information in parallel electronic and paper based patient records. Int J Med Inform 63(3):123–131CrossRefPubMed Mikkelsen G, Aasly J (2001) Concordance of information in parallel electronic and paper based patient records. Int J Med Inform 63(3):123–131CrossRefPubMed
11.
go back to reference Lacey JV Jr, Savage KE (2016) 50% Response rates: half-empty, or half-full? Cancer Causes Control 27(6):805–808CrossRefPubMed Lacey JV Jr, Savage KE (2016) 50% Response rates: half-empty, or half-full? Cancer Causes Control 27(6):805–808CrossRefPubMed
12.
13.
14.
go back to reference Beaulieu-Jones BK, Greene CS (2016) Semi-supervised learning of the electronic health record for phenotype stratification. J Biomed Inform 64:168–178CrossRefPubMed Beaulieu-Jones BK, Greene CS (2016) Semi-supervised learning of the electronic health record for phenotype stratification. J Biomed Inform 64:168–178CrossRefPubMed
Metadata
Title
The accuracy of the clinical diagnosis of Parkinson disease. The HUNT study
Authors
Eldbjørg Hustad
Anne Heidi Skogholt
Kristian Hveem
Jan O. Aasly
Publication date
01-09-2018
Publisher
Springer Berlin Heidelberg
Published in
Journal of Neurology / Issue 9/2018
Print ISSN: 0340-5354
Electronic ISSN: 1432-1459
DOI
https://doi.org/10.1007/s00415-018-8969-6

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