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Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables.

Publication ,  Journal Article
Karabayir, I; Butler, L; Goldman, SM; Kamaleswaran, R; Gunturkun, F; Davis, RL; Ross, GW; Petrovitch, H; Masaki, K; Tanner, CM; Tsivgoulis, G ...
Published in: J Parkinsons Dis
2022

BACKGROUND: Parkinson's disease (PD) is a chronic, disabling neurodegenerative disorder. OBJECTIVE: To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests. METHODS: Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995-2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density. RESULTS: The study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76-0.89) or 5 years (AUC 0.77, 95%CI 0.71-0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (r = -0.57, p < 0.01). CONCLUSION: Machine learning can identify persons likely to develop PD during the prodromal period using questionnaires and simple non-invasive tests. Correlation with neuropathology suggests that true model accuracy may be considerably higher than estimates based solely on clinical diagnosis.

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Published In

J Parkinsons Dis

DOI

EISSN

1877-718X

Publication Date

2022

Volume

12

Issue

1

Start / End Page

341 / 351

Location

United States

Related Subject Headings

  • Risk Factors
  • Prospective Studies
  • Prodromal Symptoms
  • Parkinson Disease
  • Machine Learning
  • Humans
  • 3209 Neurosciences
  • 1109 Neurosciences
  • 0601 Biochemistry and Cell Biology
 

Citation

APA
Chicago
ICMJE
MLA
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Karabayir, I., Butler, L., Goldman, S. M., Kamaleswaran, R., Gunturkun, F., Davis, R. L., … Akbilgic, O. (2022). Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables. J Parkinsons Dis, 12(1), 341–351. https://doi.org/10.3233/JPD-212876
Karabayir, Ibrahim, Liam Butler, Samuel M. Goldman, Rishikesan Kamaleswaran, Fatma Gunturkun, Robert L. Davis, G Webster Ross, et al. “Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables.J Parkinsons Dis 12, no. 1 (2022): 341–51. https://doi.org/10.3233/JPD-212876.
Karabayir I, Butler L, Goldman SM, Kamaleswaran R, Gunturkun F, Davis RL, et al. Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables. J Parkinsons Dis. 2022;12(1):341–51.
Karabayir, Ibrahim, et al. “Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables.J Parkinsons Dis, vol. 12, no. 1, 2022, pp. 341–51. Pubmed, doi:10.3233/JPD-212876.
Karabayir I, Butler L, Goldman SM, Kamaleswaran R, Gunturkun F, Davis RL, Ross GW, Petrovitch H, Masaki K, Tanner CM, Tsivgoulis G, Alexandrov AV, Chinthala LK, Akbilgic O. Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables. J Parkinsons Dis. 2022;12(1):341–351.

Published In

J Parkinsons Dis

DOI

EISSN

1877-718X

Publication Date

2022

Volume

12

Issue

1

Start / End Page

341 / 351

Location

United States

Related Subject Headings

  • Risk Factors
  • Prospective Studies
  • Prodromal Symptoms
  • Parkinson Disease
  • Machine Learning
  • Humans
  • 3209 Neurosciences
  • 1109 Neurosciences
  • 0601 Biochemistry and Cell Biology