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Predictors of normal and abnormal outcome in clinical brain dopamine transporter imaging

Joutsa J; Jaakkola E; Kaasinen V

Predictors of normal and abnormal outcome in clinical brain dopamine transporter imaging

Joutsa J
Jaakkola E
Kaasinen V
Katso/Avaa
Final draft (266.7Kb)
Lataukset: 

doi:10.1007/s00702-015-1495-0
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021042714201
Tiivistelmä


Brain dopamine transporter (DAT) imaging with [123I]FP-CIT SPECT can be used to evaluate the integrity of the mesostriatal dopaminergic system in patients with clinically uncertain parkinsonism. To evaluate whether scanning a patient is clinically necessary, it is vital to understand possible factors that affect the scanning result. Therefore, we investigated an unselected sample of 538 consecutively scanned patients from a 6-year period, and the demographic data and indications for DAT SPECT were recorded. After scanning, the patients were divided into groups according to the scanning outcome. Multivariate binary logistic regression analyses were performed to investigate whether the pre-imaging variables had independent associations with the outcome of the scan. Three hundred and three (56.3 %) patients had abnormal scans showing a dopaminergic deficit. The independent factors associated with abnormal scans were older age (p = 0.002), asymmetry of motor symptoms (p = 0.005) and shorter symptom duration (p < 0.001). Re-evaluation of the previously established Parkinson’s disease diagnosis was associated with a higher probability of an abnormal scan (74.4 % abnormal, p = 0.004), whereas the possibility of medication-induced parkinsonism was associated with a higher probability of a normal scan (35.4 %, p = 0.036). The probability of an abnormal outcome in clinical brain DAT imaging increases with known risk factors of neurodegenerative parkinsonism. However, a long duration of uncertain motor symptoms and suspicion of medication-induced parkinsonism are associated with a higher probability of a normal outcome. The findings reflect epidemiological factors in parkinsonism together with referral biases that may be used to improve the clinical use of DAT imaging.

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