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Original Investigation
June 2018

Office-Based Screening for Dementia in Parkinson DiseaseThe Montreal Parkinson Risk of Dementia Scale in 4 Longitudinal Cohorts

Author Affiliations
  • 1Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
  • 2Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
  • 3Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
  • 4Division of Neurology, Department of Brain and Neurosciences, Tottori University, Tottori, Japan
  • 5Centre d’Études Avancées en Médecine du Sommeil, Hôpital du Sacré-Coeur de Montréal, Montréal, Quebec, Canada
  • 6Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada
JAMA Neurol. 2018;75(6):704-710. doi:10.1001/jamaneurol.2018.0254
Key Points

Question  How reliably can dementia be predicted in patients with Parkinson disease (PD) with a screening tool made up of clinical predictors?

Findings  In this 4.4-year prospective study on 4 cohorts totaling 607 patients with PD, 70 had a diagnosis converted to dementia. The risk of developing PD dementia was 14-fold for a cutoff point of 4 or greater compared with a negative screen result, and the high-risk group had a 14.9% annual risk of dementia.

Meaning  With simple measures that are assessable in a single office visit, this risk score rapidly and accurately screens for dementia risk in PD.

Abstract

Importance  Parkinson disease dementia dramatically increases mortality rates, patient expenditures, hospitalization risk, and caregiver burden. Currently, predicting Parkinson disease dementia risk is difficult, particularly in an office-based setting, without extensive biomarker testing.

Objective  To appraise the predictive validity of the Montreal Parkinson Risk of Dementia Scale, an office-based screening tool consisting of 8 items that are simply assessed.

Design, Setting, and Participants  This multicenter study (Montreal, Canada; Tottori, Japan; and Parkinson Progression Markers Initiative sites) used 4 diverse Parkinson disease cohorts with a prospective 4.4-year follow-up. A total of 717 patients with Parkinson disease were recruited between May 2005 and June 2016. Of these, 607 were dementia-free at baseline and followed-up for 1 year or more and so were included. The association of individual baseline scale variables with eventual dementia risk was calculated. Participants were then randomly split into cohorts to investigate weighting and determine the scale’s optimal cutoff point. Receiver operating characteristic curves were calculated and correlations with selected biomarkers were investigated.

Main Outcomes and Measures  Dementia, as defined by Movement Disorder Society level I criteria.

Results  Of the 607 patients (mean [SD] age, 63.4 [10.1]; 376 men [62%]), 70 (11.5%) converted to dementia. All 8 items of the Montreal Parkinson Risk of Dementia Scale independently predicted dementia development at the 5% significance level. The annual conversion rate to dementia in the high-risk group (score, >5) was 14.9% compared with 5.8% in the intermediate group (score, 4-5) and 0.6% in the low-risk group (score, 0-3). The weighting procedure conferred no significant advantage. Overall predictive validity by the area under the receiver operating characteristic curve was 0.877 (95% CI, 0.829-0.924) across all cohorts. A cutoff of 4 or greater yielded a sensitivity of 77.1% (95% CI, 65.6-86.3) and a specificity of 87.2% (95% CI, 84.1-89.9), with a positive predictive value (as of 4.4 years) of 43.90% (95% CI, 37.76-50.24) and a negative predictive value of 96.70% (95% CI, 95.01-97.85). Positive and negative likelihood ratios were 5.94 (95% CI, 4.08-8.65) and 0.26 (95% CI, 0.17-0.40), respectively. Scale results correlated with markers of Alzheimer pathology and neuropsychological test results.

Conclusions and Relevance  Despite its simplicity, the Montreal Parkinson Risk of Dementia Scale demonstrated predictive validity equal or greater to previously described algorithms using biomarker assessments. Future studies using head-to-head comparisons or refinement of weighting would be of interest.

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