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

Sotirios A. Parashos, Bastiaan R. Bloem, Nina M. Browner, Nir Giladi, Tanya Gurevich, Jeffrey M. Hausdorff, Ying He, Kelly E. Lyons, Zoltan Mari, John Christopher Morgan, Bart Post, Peter N. Schmidt, Catherine L. Wielinski, Samuel S. Wu

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)214-222
Number of pages9
JournalNeurology: Clinical Practice
Volume8
Issue number3
DOIs
StatePublished - Jun 1 2018

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Accidental Falls
Parkinson Disease
Registries
Deep Brain Stimulation
Amantadine
Occupational Therapy
Levodopa
Quality Improvement
Social Work
Semantics
Osteoarthritis
Antidepressive Agents
Comorbidity
Emergencies
Therapeutics
Referral and Consultation
Logistic Models
Quality of Life
Databases
Neoplasms

ASJC Scopus subject areas

  • Clinical Neurology

Cite this

Parashos, S. A., Bloem, B. R., Browner, N. M., Giladi, N., Gurevich, T., Hausdorff, J. M., ... Wu, S. S. (2018). What predicts falls in Parkinson disease? Observations from the Parkinson's Foundation registry. Neurology: Clinical Practice, 8(3), 214-222. https://doi.org/10.1212/CPJ.0000000000000461

What predicts falls in Parkinson disease? Observations from the Parkinson's Foundation registry. / Parashos, Sotirios A.; Bloem, Bastiaan R.; Browner, Nina M.; Giladi, Nir; Gurevich, Tanya; Hausdorff, Jeffrey M.; He, Ying; Lyons, Kelly E.; Mari, Zoltan; Morgan, John Christopher; Post, Bart; Schmidt, Peter N.; Wielinski, Catherine L.; Wu, Samuel S.

In: Neurology: Clinical Practice, Vol. 8, No. 3, 01.06.2018, p. 214-222.

Research output: Contribution to journalArticle

Parashos, SA, Bloem, BR, Browner, NM, Giladi, N, Gurevich, T, Hausdorff, JM, He, Y, Lyons, KE, Mari, Z, Morgan, JC, Post, B, Schmidt, PN, Wielinski, CL & Wu, SS 2018, 'What predicts falls in Parkinson disease? Observations from the Parkinson's Foundation registry', Neurology: Clinical Practice, vol. 8, no. 3, pp. 214-222. https://doi.org/10.1212/CPJ.0000000000000461
Parashos, Sotirios A. ; Bloem, Bastiaan R. ; Browner, Nina M. ; Giladi, Nir ; Gurevich, Tanya ; Hausdorff, Jeffrey M. ; He, Ying ; Lyons, Kelly E. ; Mari, Zoltan ; Morgan, John Christopher ; Post, Bart ; Schmidt, Peter N. ; Wielinski, Catherine L. ; Wu, Samuel S. / What predicts falls in Parkinson disease? Observations from the Parkinson's Foundation registry. In: Neurology: Clinical Practice. 2018 ; Vol. 8, No. 3. pp. 214-222.
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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.",
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AU - Gurevich, Tanya

AU - Hausdorff, Jeffrey M.

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AU - Lyons, Kelly E.

AU - Mari, Zoltan

AU - Morgan, John Christopher

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AU - Schmidt, Peter N.

AU - Wielinski, Catherine L.

AU - Wu, Samuel S.

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N2 - 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.

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