What predicts falls in Parkinson disease? Observations from the Parkinson's Foundation registry uri icon

abstract

  • Background: We undertook this study to identify patients with Parkinson disease (PD) with no or rare falls who may progress to frequent falling by their next annual follow-up visit. Methods: We analyzed data in the National Parkinson Foundation Quality Improvement Initiative database to identify factors predicting which patients with PD with no or rare falls at the baseline visit will report at least monthly falls at the annual follow-up visit. Multivariable models were constructed using logistic regression. Variables were introduced in 4 blocks: in the 1st block, variables present at or before the baseline visit were entered; in the 2nd, baseline visit assessments; in the 3rd, interventions implemented during baseline visit; and, in the 4th block, changes in comorbidities, living situation, and treatment between visits. Results: Of 3,795 eligible participants, 3,276 (86.3%) reported no or rare falls at baseline visit, and of them, 382 (11.7%) reported at least monthly falls at follow-up visit. Predictors included female sex, <90% diagnostic certainty, motor fluctuations, levodopa treatment, antidepressant treatment, prior deep brain stimulation (DBS), worse quality of life, Hoehn & Yahr stage 2 or 3, worse semantic fluency, and, between visits, addition of amantadine, referral to occupational therapy, social services, or DBS, new diagnoses of cancer or osteoarthritis, and increased emergency visits. Conclusions: This large-scale analysis identified several predictors of progression to falling in PD. Such identifiers may help target patient subgroups for falls prevention intervention. Some factors are modifiable, offering opportunities for developing such interventions.

publication date

  • 2018