Clinical Features Observed in General Practice Associated With the Subsequent Diagnosis of Progressive Supranuclear Palsy.

Autor:	Kwasny MJ; Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States., Oleske DM; Global Epidemiology, AbbVie Inc., North Chicago, IL, United States., Zamudio J; Global Medical Affairs, AbbVie Inc., North Chicago, IL, United States., Diegidio R; Global Epidemiology, AbbVie Inc., North Chicago, IL, United States., Höglinger GU; Department of Neurology, Hannover Medical School, Hannover, Germany.; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
Jazyk:	angličtina
Zdroj:	Frontiers in neurology [Front Neurol] 2021 Apr 22; Vol. 12, pp. 637176. Date of Electronic Publication: 2021 Apr 22 (Print Publication: 2021).
DOI:	10.3389/fneur.2021.637176
Abstrakt:	Background: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disorder that is difficult for primary care physicians to recognize due to its progressive nature and similarities to other neurologic disorders. This case-control study aimed to identify clinical features observed in general practice associated with a subsequent diagnosis of PSP. Methods: We analyzed a de-identified dataset of 152 PSP cases and 3,122 matched controls from electronic medical records of general practices in Germany. We used a random forests algorithm based on machine learning techniques to identify clinical features (medical conditions and treatments received) associated with pre-diagnostic PSP without using an a priori hypothesis. We then assessed the relative effects of the features with the highest importance scores and generated multivariate models using clustered logistic regression analyses to identify a subset of clinical features associated with subsequent PSP diagnosis. Results: Using the random forests approach, we identified 21 clinical features associated with pre-diagnostic PSP (odds ratio ≥2.0 in univariate analyses). From these, we constructed a multivariate model comprising 9 clinical features with ~90% likelihood of identifying a subsequent PSP diagnosis. These features included known PSP symptoms, common misdiagnoses, and 2 novel associations, diabetes mellitus and cerebrovascular disease, which are possible modifiable risk factors for PSP. Conclusion: In this case-control study using data from electronic medical records, we identified 9 clinical features, including 2 previously unknown factors, associated with the pre-diagnostic stage of PSP. These may be used to facilitate recognition of PSP and reduce time to referral by primary care physicians. Competing Interests: MJK has received honoraria from KAI Research in conjunction with the National Institutes of Health Award R01-AR071057, and consulting fees from AbbVie and Actualize Therapy, LLC. DMO and JZ are full-time employees of AbbVie and may hold stock and/or stock options in AbbVie. RD is a contractor with AbbVie. GUH has ongoing research collaborations with Prothena; has served as a consultant for AbbVie, Alzprotect, Asceneuron, Biogen, Biohaven, Lundbeck, Novartis, Roche, Sanofi, and UCB; has received honoraria for scientific presentations from AbbVie, Biogen, Bristol Myers Squibb, Roche, Teva, UCB, and Zambon; has received research support from CurePSP, the German Academic Exchange Service (DAAD), the German Ministry of Education and Research (BMBF), the German Parkinson's Disease Foundation (DPG), the German PSP Association (PSP Gesellschaft), the German Research Foundation (DFG), International Parkinson's Funds (IPF), VolkswagenStiftung/Lower Saxony Ministry for Science/Petermax-Müller Foundation (Etiology and Therapy of Synucleinopathies and Tauopathies); and has received institutional support from the German Center for Neurodegenerative Diseases (DZNE). The authors declare that this study received funding from AbbVie Inc. The funder contributed to the study design, research, and interpretation of the data, and to the writing, review, and approval of the publication. AbbVie also provided funding to IQVIA for licensing of the electronic medical records database sourced by the IMS® Disease Analyzer and funding for editorial support. (Copyright © 2021 Kwasny, Oleske, Zamudio, Diegidio and Höglinger.)
Databáze:	MEDLINE
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