Caídas en personas con enfermedad de Parkinson: ¿Los síntomas no motores importan tanto como los síntomas motores?

Arq Neuropsiquiatr anp Arquivos de Neuro-Psiquiatria Arq. Neuro-Psiquiatr. 0004-282X 1678-4227 Academia Brasileira de Neurologia - ABNEURO RESUMEN Las caídas son frecuentes entre las personas con Parkinson (EP). La predicción de caídas es compleja ya que existen contribuyentes genéricos y específicos. El papel de los síntomas no motores ha sido menos estudiado. Objetivo: Identificar el papel de los factores no motores en caídas en personas con EP (PcP). Métodos: Estudio transversal en PcP reclutadas en una clínica de trastornos del movimiento. Se incluyeron datos clínicos y demográficos. Todos los PcP se evaluaron con la Escala Unificada de Enfermedad de Parkinson modificada por la Sociedad Internacional de Trastornos del Movimiento (MDS-UPDRS) y la Escala de Síntomas No Motores (NMSS). Se incluyeron variables significativas en un modelo de regresión logística. Resultados: Se incluyeron un total de 179 PcP El 16.8% había presentado una caída en los últimos doce meses y el 53.3% de forma recurrente. El número medio de caídas mensuales fue de 2.5 ± 3.3. Los factores asociados con la caída en el análisis bivariado fueron la duración de la enfermedad, Hoehn e Yahr, MDS-UPDRS parte I y II, subtipo de alteración de la marcha/inestabilidad postural (PIGD), dominio urinario del NMSS, dominio misceláneo del NMSS y carga de severidad no motora (todos los valores de p < 0.05). Después del análisis multivariado, solo la duración de la enfermedad (p = 0.03) y PIGD (p = 0.03) permanecieron como un factor de riesgo independiente. Conclusión: La duración de la enfermedad y PIGD se identificaron como factores de riesgo para caídas. Los síntomas no motores parecen tener un papel menos relevante en las caídas. Falls occur frequently among persons with Parkinson's disease (PD). The reported frequency varies across studies but is estimated to be around 60%. In addition, recurrent fallers represent up to 39%1. Falls are also correlated with a worse quality of life, reduced life expectancy, and loss of independence2. Predicting risk of falling is complex due to its multifactorial nature. Contributing factors have been divided into generic and PD-specific, with very varied results3. The generic risk factors mainly include age, gender, visual impairment, cardiovascular disease and other comorbidities. The PD-specific factors include disease severity and are focused on motor symptoms such as slow mobility, freezing of gait, axial rigidity and posture instability. On the other hand, fewer studies specifically addressing the role of non-motor symptoms (NMS) have been carried out. Most of these risk factors are known predictors of falls in the elderly, such as depression and cognitive impairment. However, the role of the full spectrum of NMS present in persons with PD (PwP) as risk factors for fall has not been addressed. As a consequence, the contribution of each independent motor and non-motor risk factor, as well as their interactions, remain only partially understood. The objective of this study was to assess the role of NMS in the risk of falling in PwP along with motor and clinical factors. METHODS A cross-sectional study was carried out, including consecutive PwP attending the movement disorders clinic at the National Institute of Neurology and Neurosurgery in Mexico City. The PwP were eligible according to accepted criteria4. Clinical and demographic data were collected, including disease duration and fall occurrence in the past 12 months. The levodopa equivalent daily dose was calculated, as published elsewhere5. All examining neurologists had experience in the assessment of movement disorders. The PwP were classified as fallers (at least one fall in the past 12 months) or non-fallers. Recurrent fallers were defined as having two or more falls in the last year. The following clinical tools were applied: the Hoehn and Yahr scale (HY)6, Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS)7, and Non-Motor Symptoms Scale (NMSS)8,9. Also, a tremor score and a postural instability/gait disturbance (PIGD) score were used to determine the motor subtype, as published by Stebbins et al.9. All sections of the MDS-UPDRS were applied to evaluate non-motor experiences of daily living (part I), motor experiences of daily living (part II), motor examination (part III) and motor complications (part IV). The NMSS was used to assess both the presence and severity of non-motor symptoms. This scale evaluates 30 items grouped into nine relevant domains: cardiovascular (two items); sleep/fatigue (four items); mood/ cognition (six items); perceptual problems/hallucinations (three items); attention/memory (three items); gastrointestinal tract (three items); urinary (three items); sexual function (two items); and miscellaneous items evaluating pain, olfactory alterations, weight loss, and excessive sweating. The score for each item is based on a multiple of the severity score (from 0 to 3) and the frequency score (from 1 to 4). Burden levels on the NMSS were defined for both severity (defined by the NMSS total score) and load (defined by the number of NMS declared by the PwP). Regarding NMS severity burden levels, the PwP were classified as: no NMS (score of 0), mild (score 1-20), moderate (score 21-40), severe (score 41-70) and very severe (score 71 or more)10. The PwP were also classified according to the NMS load burden as no NMS (0 symptoms), mild (1-5 NMS), moderate (6-9 NMS), severe (10-13 NMS) and very severe (14 or more NMS)11. The study was approved by the Institutional Review Board. All participants provided full written consent for participation in this study. Statistical analysis Normal distribution of all variables was evaluated according to the Shapiro-Wilk test. Comparisons among groups of fallers and non-fallers were conducted. Quantitative data was analyzed using the independent Student's t-test or a oneway analysis of variance (or nonparametric equivalent), as needed. Qualitative variables were compared using the X2 test or the Fisher's Exact test, as appropriate. Variables with statistically significant differences in the bivariate analyses were put into a logistic regression model with the presence of falls in the past 12 months as the dependent variable. Variables were assessed for multicollinearity using variation inflation factors. The Hosmer-Lemeshow test was used for goodness of fit. Variance explained by the model was assessed using the Nagelkerke R-squared. A p-value of < 0.05 was considered significant. Statistical analyses were performed using the SPSS, version 17 (SPSS, Inc., Chicago, IL). RESULTS A total of 179 PwP were included in the study. The mean age was 64.6 ± 12.2 years and the mean disease duration was 10.4 ± 7.7 years. Distribution for disease severity was 65.4% for HY 1-2, 30.2% for HY 3 and 4.5% for HY 4 -5. The mean MDS-UPDRS part III was 26.8 ± 13.4. Regarding the motor subtype, 59.8% were classified as having PIGD, 25.7% with tremor dominant subtype, and 14.5% with indeterminate subtype. The mean levodopa equivalent daily dose was 807.1 ± 515.1 mg. Overall, 16.8% of PwP had experienced at least one fall in the past 12 months. The mean number of falls per month was 2.5 ± 3.3, with 53.3% being recurrent fallers. A statistically significantly difference between fallers and non-fallers was found with the following variables: disease duration, disease severity (HY mild and moderate stages), MDS-UPDRS parts I, II and total scores, motor subtype (PIGD for fallers and tremor dominant for non-fallers). A full comparison of clinical and demographic variables is shown in Table 1. Table 1 Comparison of clinical and demographic data between fallers and non-fallers. Clinical variablesa Fallers (n = 30) Non-fallers (n = 149) p-value Age (years) 66.7 ± 12.1 64.2 ± 12.3 0.29 Male, n (%) 19 (63.3) 91 (61.1) 0.84 Disease duration (years) 12.8 ± 8.4 74 ± 5.6 < 0.001 HY stage 2.7 ± 0.9 2.3 ± 0.7 0.01 HY 1-2, n (%) 13 (43.3) 104 (69.8) 0.01 HY 3, n (%) 14 (46.7) 40 (26.8) 0.03 HY 4-5, n (%) 3 (10) 5 (3.4) 0.10 MDS-UPDRS I 13.3 ± 71 9.7 ± 5.6 < 0.001 MDS-UPDRS II 18.4 ± 9.6 13.2 ± 10 0.01 MDS-UPDRS III 31.0 ± 13.7 25.9 ± 13.2 0.06 MDS-UPDRS IV 0.9 ± 2.5 1.23 ± 2.53 0.50 MDS-UPDRS Total 63.6 ± 22.7 50.1 ± 23 < 0.05 Motor subtype TD, n (%) 1 (3.3) 45 (30.2) 0.01 Motor subtype indeterminate, n (%) 1 (3.3) 25 (16.8) 0.06 Motor subtype PIGD n (%) 28 (93.3) 79 (53) < 0.001 Levodopa, n (%) 27 (90) 120 (80.5) 0.29 Levodopa equivalent daily dose, mg 954.90 ± 652.50 777.33 ± 479.99 0.08 HY: Hoehn and Yahr; MDS-UPDRS: Movement Disorders Society Unified Parkinson's Disease Rating Scale; TD: tremor dominant; PIGD: postural instability and gait disorder. Regarding severity of NMS, a statistical difference was found in the NMSS total score with fallers scoring higher. When analyzing by individual domain, fallers had a higher score in the NMSS urinary domain as well as in the NMSS miscellaneous domain. As shown in Table 2, no differences were found in the remaining domains. Table 2 Comparison of non-motor symptoms between groupsa. Variables Fallers (n = 30) Non-fallers (n = 149) p-value NMSS Cardiovascular 1.9 ± 4.5 0.9 ± 2.4 0.23 NMSS Sleep/fatigue 11 ± 13.5 6.1 ± 10.6 0.07 NMSS Mood/cognition 12.0 ± 19.0 6.7 ± 13.4 0.16 NMSS Perceptual/hallucinations 3.1 ± 9.0 0.8 ± 2.9 0.18 NMSS Attention/memory 6.2 ± 11.4 3.0 ± 6.7 0.15 NMSS Gastrointestinal 5.3 ± 5.5 4.0 ± 5.9 0.25 NMSS Urinary 10.8 ± 12.6 5.7 ± 8.9 0.04 NMSS Sexual function 0.8 ± 4.4 1.8 ± 11.2 0.65 NMSS Miscellaneous 5.0 ± 5.4 2.4 ± 5.2 0.02 NMSS Total Score 56.1 ± 47.0 31.5 ± 33.1 0.01 NMS Load burden No NMS 0 0 -- Mild (1-5) 2(6.7%) 19(12.8%) 0.14 Moderate (6-9) 3(10.0%) 33(22.1%) 0.13 Severe (10-13) 10(33.3%) 40(26.9%) 0.47 Very severe (> 13) 15(50.0%) 57(38.2%) 0.23 NMS Severity burden No NMS 0 0 -- Mild (1-20) 8 (26.7%) 73 (49%) 0.02 Moderate (21-40) 9 (30.0%) 39 (26.2%) 0.66 Severe (41-70) 1 (3.3%) 13 (8.7%) 0.32 Very Severe (> 70) 12 (40.0%) 24 (16.1%) 0.01 NMSS: Non-Motor Symptom Scale; NMS: non-motor symptoms. a Data are mean ± SD; or absolute numbers and percentages After categorizing by NMS severity burden, fallers had a “very severe” burden while non-fallers had a “mild” burden (Table 2). On the other hand, no differences were found when comparing the NMS load burden between groups (17.4 ± 8.7 among fallers, 14.8 ± 9.3 among non-fallers; p = 0.16). A logistic regression model was designed using variables with differences in the bivariate analyses. The MDS-UPDRS total score was not included in the model to avoid multicollinearity (variation inflation factors = 5.1) with MDS-UPDRS parts I and II scores. In addition, a new MDS-UPDRS part I score, subtracting items 1.9 (pain) and item 1.10 (urinary problems), was calculated to avoid collinearity with the NMSS domains evaluating these symptoms. This new MDSUPDRS part I partial score maintained a statistically significant difference between groups (p = 0.01). Similarly, the NMSS total was also excluded due to multicollinearity (variation inflation factors = 5.03) with the genitourinary and miscellaneous domains. No significant multicollinearity was found between the remaining variables. The variables included in the model were disease duration, HY stage, motor subtype, MDS-UPDRS part I score, MDSUPDRS part II score, NMSS genitourinary score, NMSS miscellaneous score, and NMS severity burden. After the regression analysis, only the disease duration and PIGD subtype remained significant. Data derived from multivariate analysis are shown in Table 3. The Hosmer-Lemeshow test showed goodness of fit (χ2 = 7.12, df = 8, p = 0.524) and the Nagelkerke R-squared was 0.32. This multivariate model correctly classified the outcome for 84.9% of the cases. Table 3 Multivariate logistic regression model for predicting falls in people with Parkinson's disease. Variable B Exp (B) 95% CI p-value Disease duration (years) 0.06 1.06 1.01 - 1.11 0.03 MDS-UPDRS I partial score * 0.09 1.09 0.98 - 1.23 0.12 MDS-UPDRS II -0.03 0.97 0.93 - 1.03 0.32 HY stage 0.48 1.62 0.90 - 2.9 0.11 PIGD Subtype 2.33 10.25 1.25 - 83 0.03 TD subtype 0.18 1.19 0.06 - 24.6 0.91 NMSS urinary 0.02 1.02 0.97 - 1.07 0.50 NMSS miscellaneous 0.05 1.05 0.98 - 1.14 0.19 Mild NMS severity burden 0.14 1.15 0.21 - 6.44 0.88 Very severe NMS severity burden 0.14 0.87 0.16 - 1.49 0.88 MDS-UPDRS: Movement Disorders Society Unified Parkinson's Disease Rating Scale; HY: Hoehn and Yahr stage; PIGD: postural instability and gait disorder; TD: tremor dominant; NMSS: Non-Motor Symptoms Scale; NMS: non-motor symptoms; * MDS-UPDRS part I score minus items 1.9 and 1.10. DISCUSSION The proportion of fallers among our sample was very low (16.8%). Prospective studies have reported falling frequency at three months follow-up to range between 36% and 59%12, whereas retrospective studies have reported 31% and 32.9% in the previous month and year, respectively13,14. Interestingly, some studies have assessed falling frequency in both prospective and retrospective ways with mixed results. In one study the incidence of falls reported by anamnesis (retrospective analysis) did not differ from the prospective assessment after one year15. Another study found that 79.7% of participants fell over 54 months compared with 26.2% of participants who reported retrospective falls at baseline16. The low proportion of fallers in our sample could partially be explained by under-representation of advanced forms of the disease, with only 4.5% being at HY 4-5 stages. The difficulty designing accurate fall prediction models highlights the complexity of fall risk. Generic age-related risk factors are well known and include age, female sex, polypharmacy, autonomic dysfunction, arthrosis, visual impairment, and depression, among others3. Many prediction models have been proposed with varying results. A meta-analysis of six prospective studies (n = 473) found that the strongest predictor of falling was prior falls in the preceding year. Although relevant, recurrent falling is of limited use for prevention models and was not further evaluated in our study. This large meta-analysis also failed to identify disease severity as a significant predictor of falls12. In our study, the severity of motor symptoms failed to predict falling. This finding is consistent with other studies that have failed to show motor variables as independent predictors of falls13,17. The only independent predictor statistically significant at the multivariate level was the disease duration and the PIGD subtype. The PIGD as a risk factor has already been reported18 and is consistent with other studies that have identified slow mobility19, freezing of gait, posture, postural instability20, and axial rigidity21 as significant predictors. Conversely, the PIGD subtype has been associated with greater severity of nondopaminergic (mainly cholinergic) symptoms22 and greater cognitive impairment23, which may account for an increased risk of falling. The frequency of the PIGD subtype in our study was almost 60%, which is in line with the 64% reported by Stebbins et al.9. Nevertheless, it is relevant to consider that it has been proven that the motor subtype is not stable and tremor dominant/PIGD subtypes can shift category over time24. Moreover, Alvarado-Franco et al. reported a shift from tremor dominant to PIGD subtype in Mexican PwP, increasing from 21% to 42% over a six-year follow-up25. After multivariate analysis, both the disease duration and PIGD subtype remained as statistically significant predictors but this finding needs further confirmation through a prospective design. In addition, the disease duration in our sample was around eight years and should be considered when interpreting the results. Motor fluctuations are among the least-consistent predictors. Some authors have suggested an association between dyskinesia and falls17, whereas others failed to prove this. The same was true for wearing off, with some suggesting association26 and others with inconclusive results17. In our sample, no difference was found in the MDS-UPDRS part IV, which assesses both wearing off and dyskinesia. On the other hand, the association of NMS and falling risk has scarcely been explored. Some studies have associated falling risk with cognitive impairment19,27, rapid eye movement sleep behavioral disorders13,28, autonomic dysfunction29, depression30, cardiovascular comorbidity31 and urinary incontinence14. In our study, in the NMSS genitourinary domain, miscellaneous and total scores were higher among fallers, although none of these variables were shown to be independent predictors within the regression model. One possible explanation for the association between urinary symptoms and falls is through dysautonomia, which may lead to falls. However, in our study, the NMSS cardiovascular domain was not associated with falling. Another possible explanation that needs further study is nocturia, which is often the prevailing factor leading to nighttime falls32. The miscellaneous domain evaluates several NMS, including pain, change in taste or smell, change in weight and excessive sweating. Of these items, excessive sweating may be associated with dysautonomia. Weight change also has some theoretical foundation, since a higher body mass index has been linked to falling risk33. Unfortunately, the heterogeneity of this domain does not allow strong conclusions. A major limitation of our study was the fact that the patients were not evaluated according to specific recommended tools34,35,36 for all NMS, particularly in the case of neuropsychiatric symptoms; therefore screening may not have been ideal. To the best of our knowledge, this is the first study to explore the NMSS severity and load burdens as potential predictors of falls. We theorized that the number and severity of accumulating NMS could significantly contribute to falling risk. Interestingly, the NMSS severity burden was higher among fallers but did not prove to be an independent predictor after multivariate analysis. Our study has other limitations. A retrospective assessment of previous history of falls may not be reliable for identifying fall predictors and is subject to recall bias. Nonetheless, similar estimates of fall incidences have been reported using both retrospective and prospective methodologies15. As mentioned before, a referral bias was present with the under-representation of PwP in the more severe stages of the disease; therefore, our results may not be reproducible in patients with advanced stages of the disease. This is particularly relevant as fall frequency is thought to have an inverted U-shaped curve mediated by ambulatory activity that decreases as disease severity increases37. Lastly, our model showed an adequate goodness of fit but found that the variance was lower than that reported by other authors38. This may be due to the complex multifactorial nature of falling prediction. Other models with higher predictive values included different variables including physical activity39, past falling and fear of falling40, the retropulsion test and tandem gait41. These variables were not individually assessed although the retropulsion test and gait were included in the MDS-UPDRS part III and used to classify PwP within the motor subtypes. In conclusion, our study identified several motor and non-motor symptoms as factors associated with falls in PwP, but only the disease duration and PIGD remained as independent predictors after multivariate analysis. These findings suggest a more intensive approach in fall prevention among PwP with this subtype. On the other hand, longitudinal studies with PwP in early disease stages are warranted. References 1 Allen NE Schwarzel AK Canning CG Recurrent falls in Parkinson's disease: a systematic review Parkinsons Dis 2013 2013 906274 906274 https://doi.org/10.1155/2013/906274 1. Allen NE, Schwarzel AK, Canning CG. Recurrent falls in Parkinson's disease: a systematic review. Parkinsons Dis. 2013;2013:906274. https://doi.org/10.1155/2013/906274 2 Thurman DJ Stevens JA Rao JK Practice parameter: assessing patients in a neurology practice for risk of falls (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology Neurology 2008 02 70 6 473 479 https://doi.org/10.1212/01.wnl.0000299085.18976.20 2. Thurman DJ, Stevens JA, Rao JK. Practice parameter: assessing patients in a neurology practice for risk of falls (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2008 Feb;70(6):473-9. https://doi.org/10.1212/01.wnl.0000299085.18976.20 3 Marck MA Klok MP Okun MS Giladi N Munneke M Bloem BR Consensus-based clinical practice recommendations for the examination and management of falls in patients with Parkinson's disease Parkinsonism Relat Disord 2014 04 20 4 360 369 https://doi.org/10.1016/j.parkreldis.2013.10.030 3. Marck MA, Klok MP, Okun MS, Giladi N, Munneke M, Bloem BR. Consensus-based clinical practice recommendations for the examination and management of falls in patients with Parkinson's disease. Parkinsonism Relat Disord. 2014 Apr;20(4):360-9. https://doi.org/10.1016/j.parkreldis.2013.10.030 4 Postuma RB Berg D Stern M Poewe W Olanow CW Oertel W MDS clinical diagnostic criteria for Parkinson's disease Mov Disord 2015 10 30 12 1591 1601 https://doi.org/10.1002/mds.26424 4. Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, et al. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord. 2015 Oct;30(12):1591-601. https://doi.org/10.1002/mds.26424 5 Tomlinson CL Stowe R Patel S Rick C Gray R Clarke CE Systematic review of levodopa dose equivalency reporting in Parkinson's disease Mov Disord 2010 11 25 15 2649 2653 https://doi.org/10.1002/mds.23429 5. Tomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE. Systematic review of levodopa dose equivalency reporting in Parkinson's disease. Mov Disord. 2010 Nov;25(15):2649-53. https://doi.org/10.1002/mds.23429 6 Hoehn MM Yahr MD Parkinsonism: onset, progression, and mortality. 1967 Neurology 1998 02 50 2 318 318 https://doi.org/10.1212/WNL.50.2.318 6. Hoehn MM, Yahr MD. Parkinsonism: onset, progression, and mortality. 1967. Neurology. 1998 Feb;50(2):318. https://doi.org/10.1212/WNL.50.2.318 7 Martinez-Martin P Rodriguez-Blazquez C Alvarez-Sanchez M Arakaki T Bergareche-Yarza A Chade A Expanded and independent validation of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) J Neurol 2013 01 260 1 228 236 https://doi.org/10.1007/s00415-012-6624-1 7. Martinez-Martin P, Rodriguez-Blazquez C, Alvarez-Sanchez M, Arakaki T, Bergareche-Yarza A, Chade A, et al. Expanded and independent validation of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). J Neurol. 2013 Jan;260(1):228-36. https://doi.org/10.1007/s00415-012-6624-1 8 Chaudhuri KR Martinez-Martin P Brown RG Sethi K Stocchi F Odin P The metric properties of a novel non-motor symptoms scale for Parkinson's disease: results from an international pilot study Mov Disord 2007 10 22 13 1901 1901 https://doi.org/10.1002/mds.21596 8. Chaudhuri KR, Martinez-Martin P, Brown RG, Sethi K, Stocchi F, Odin P, et al. The metric properties of a novel non-motor symptoms scale for Parkinson's disease: results from an international pilot study. Mov Disord. 2007 Oct;22(13):1901-1 1. https://doi.org/10.1002/mds.21596 9 Stebbins GT Goetz CG Burn DJ Jankovic J Khoo TK Tilley BC How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson's disease rating scale: comparison with the unified Parkinson's disease rating scale Mov Disord 2013 05 28 5 668 670 https://doi.org/10.1002/mds.25383 9. Stebbins GT, Goetz CG, Burn DJ, Jankovic J, Khoo TK, Tilley BC. How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson's disease rating scale: comparison with the unified Parkinson's disease rating scale. Mov Disord. 2013 May;28(5):668-70. https://doi.org/10.1002/mds.25383 10 Ray Chaudhuri K Rojo JM Schapira AH Brooks DJ Stocchi F Odin P A proposal for a comprehensive grading of Parkinson's disease severity combining motor and non-motor assessments: meeting an unmet need PLoS One 2013 8 2 e57221 https://doi.org/10.1371/journal.pone.0057221 10. Ray Chaudhuri K, Rojo JM, Schapira AH, Brooks DJ, Stocchi F, Odin P, et al. A proposal for a comprehensive grading of Parkinson's disease severity combining motor and non-motor assessments: meeting an unmet need. PLoS One. 2013;8(2):e57221. https://doi.org/10.1371/journal.pone.0057221 11 Chaudhuri KR Sauerbier A Rojo JM Sethi K Schapira AH Brown RG The burden of non-motor symptoms in Parkinson's disease using a self-completed non-motor questionnaire: a simple grading system Parkinsonism Relat Disord 2015 03 21 3 287 291 https://doi.org/10.1016/j.parkreldis.2014.12.031 11. Chaudhuri KR, Sauerbier A, Rojo JM, Sethi K, Schapira AH, Brown RG, et al. The burden of non-motor symptoms in Parkinson's disease using a self-completed non-motor questionnaire: a simple grading system. Parkinsonism Relat Disord. 2015 Mar;21(3):287-91. https://doi.org/10.1016/j.parkreldis.2014.12.031 12 Pickering RM Grimbergen YA Rigney U Ashburn A Mazibrada G Wood B A meta-analysis of six prospective studies of falling in Parkinson's disease Mov Disord 2007 10 22 13 1892 1900 https://doi.org/10.1002/mds.21598 12. Pickering RM, Grimbergen YA, Rigney U, Ashburn A, Mazibrada G, Wood B, et al. A meta-analysis of six prospective studies of falling in Parkinson's disease. Mov Disord. 2007 Oct;22(13):1892-900. https://doi.org/10.1002/mds.21598 13 Schrag A Choudhury M Kaski D Gallagher DA Why do patients with Parkinson's disease fall? A cross-sectional analysis of possible causes of falls NPJ Parkinsons Dis 2015 06 1 1 15011 15011 https://doi.org/10.1038/npjparkd.2015.11 13. Schrag A, Choudhury M, Kaski D, Gallagher DA. Why do patients with Parkinson's disease fall? A cross-sectional analysis of possible causes of falls. NPJ Parkinsons Dis. 2015 Jun;1(1):15011. https://doi.org/10.1038/npjparkd.2015.11 14 Balash Y Peretz C Leibovich G Herman T Hausdorff JM Giladi N Falls in outpatients with Parkinson's disease: frequency, impact and identifying factors J Neurol 2005 11 252 11 1310 1315 https://doi.org/10.1007/s00415-005-0855-3 14. Balash Y, Peretz C, Leibovich G, Herman T, Hausdorff JM, Giladi N. Falls in outpatients with Parkinson's disease: frequency, impact and identifying factors. J Neurol. 2005 Nov;252(11):1310-5. https://doi.org/10.1007/s00415-005-0855-3 15 Rudzińska M Bukowczan S Stozek J Zajdel K Mirek E Chwata W The incidence and risk factors of falls in Parkinson disease: prospective study Neurol Neurochir Pol 2013 Sep-Oct 47 5 431 437 https://doi.org/10.5114/ninp.2013.38223 15. Rudzińska M, Bukowczan S, Stozek J, Zajdel K, Mirek E, Chwata W, et al. The incidence and risk factors of falls in Parkinson disease: prospective study. Neurol Neurochir Pol. 2013 Sep-Oct;47(5):431-7. https://doi.org/10.5114/ninp.2013.38223 16 Lord S Galna B Yarnall AJ Morris R Coleman S Burn D Natural history of falls in an incident cohort of Parkinson's disease: early evolution, risk and protective features J Neurol 2017 11 264 11 2268 2276 https://doi.org/10.1007/s00415-017-8620-y 16. Lord S, Galna B, Yarnall AJ, Morris R, Coleman S, Burn D, et al. Natural history of falls in an incident cohort of Parkinson's disease: early evolution, risk and protective features. J Neurol. 2017 Nov;264(11):2268-76. https://doi.org/10.1007/s00415-017-8620-y 17 Contreras A Grandas F Risk of falls in Parkinson's disease: a cross-sectional study of 160 patients Parkinsons Dis 2012 2012 362572 362572 https://doi.org/10.1155/2012/362572 17. Contreras A, Grandas F. Risk of falls in Parkinson's disease: a cross-sectional study of 160 patients. Parkinsons Dis. 2012;201 2:362572. https://doi.org/10.1155/2012/362572 18 Hiorth YH Alves G Larsen JP Schulz J Tysnes OB Pedersen KF Long-term risk of falls in an incident Parkinson's disease cohort: the Norwegian ParkWest study J Neurol 2017 02 264 2 364 372 https://doi.org/10.1007/s00415-016-8365-z 18. Hiorth YH, Alves G, Larsen JP, Schulz J, Tysnes OB, Pedersen KF. Long-term risk of falls in an incident Parkinson's disease cohort: the Norwegian ParkWest study. J Neurol. 2017 Feb;264(2):364-72. https://doi.org/10.1007/s00415-016-8365-z 19 Latt MD Lord SR Morris JG Fung VS Clinical and physiological assessments for elucidating falls risk in Parkinson's disease Mov Disord 2009 07 24 9 1280 1289 https://doi.org/10.1002/mds.22561 19. Latt MD, Lord SR, Morris JG, Fung VS. Clinical and physiological assessments for elucidating falls risk in Parkinson's disease. Mov Disord. 2009 Jul;24(9):1280-9. https://doi.org/10.1002/mds.22561 20 Keus SH Bloem BR Hendriks EJ Bredero-Cohen AB Munneke M Practice Recommendations Development Group. Evidence-based analysis of physical therapy in Parkinson's disease with recommendations for practice and research Mov Disord 2007 03 22 4 451 460 https://doi.org/10.1002/mds.21244 20. Keus SH, Bloem BR, Hendriks EJ, Bredero-Cohen AB, Munneke M; Practice Recommendations Development Group. Evidence-based analysis of physical therapy in Parkinson's disease with recommendations for practice and research. Mov Disord. 2007 Mar;22(4):451-60. https://doi.org/10.1002/mds.21244 21 Jankovic J Parkinson's disease: clinical features and diagnosis J Neurol Neurosurg Psychiatry 2008 04 79 4 368 376 https://doi.org/10.1136/jnnp.2007.131045 21. Jankovic J. Parkinson's disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008 Apr;79(4):368-76. https://doi.org/10.1136/jnnp.2007.131045 22 Heeden JF Marinus J Martinez-Martin P Rodriguez-Blazquez C Geraedts VJ van Hilten JJ Postural instability and gait are associated with severity and prognosis of Parkinson disease Neurology 2016 06 86 24 2243 2250 https://doi.org/10.1212/WNL.0000000000002768 22. Heeden JF, Marinus J, Martinez-Martin P, Rodriguez-Blazquez C, Geraedts VJ, van Hilten JJ. Postural instability and gait are associated with severity and prognosis of Parkinson disease. Neurology. 2016 Jun;86(24):2243-50. https://doi.org/10.1212/WNL.0000000000002768 23 Arie L Herman T Shema-Shiratzky S Giladi N Hausdorff JM Do cognition and other non-motor symptoms decline similarly among patients with Parkinson's disease motor subtypes? Findings from a 5-year prospective study J Neurol 2017 10 264 10 2149 2157 https://doi.org/10.1007/s00415-017-8605-x 23. Arie L, Herman T, Shema-Shiratzky S, Giladi N, Hausdorff JM. Do cognition and other non-motor symptoms decline similarly among patients with Parkinson's disease motor subtypes? Findings from a 5-year prospective study. J Neurol. 2017 Oct;264(10):2149-57. https://doi.org/10.1007/s00415-017-8605-x 24 Simuni T Caspell-Garcia C Coffey C Lasch S Tanner C Marek K How stable are Parkinson's disease subtypes in de novo patients: analysis of the PPMI cohort? Parkinsonism Relat Disord 2016 07 28 62 67 https://doi.org/10.1016Zj.parkreldis.2016.04.027 24. Simuni T, Caspell-Garcia C, Coffey C, Lasch S, Tanner C, Marek K. How stable are Parkinson's disease subtypes in de novo patients: analysis of the PPMI cohort? Parkinsonism Relat Disord. 2016 Jul;28:62-7. https://doi.org/10.1016Zj.parkreldis.2016.04.027 25 Alvarado-Franco NL Olguín-Ramírez L Eisinger RS Ramírez-Zamorra A Cervantes-Arriaga A Rodríguez-Valente M Analysis of Parkinson's disease motor subtypes: Mexican Registry of Parkinson - ReMePARK Rev Mex Neuroci 2018 19 5 3 8 25. Alvarado-Franco NL, Olguín-Ramírez L, Eisinger RS, Ramírez-Zamorra A, Cervantes-Arriaga A, Rodríguez-Valente M, et al. Analysis of Parkinson's disease motor subtypes: Mexican Registry of Parkinson - ReMePARK. Rev Mex Neuroci. 2018;19(5):3-8. 26 Almeida LR Valenca GT Negreiros NN Pinto EB Oliveira-Filho J Predictors of recurrent falls in people with Parkinson's disease and proposal for a predictive tool J Parkinsons Dis 2017 7 2 313 324 https://doi.org/10.3233/JPD-160934 26. Almeida LR, Valenca GT, Negreiros NN, Pinto EB, Oliveira-Filho J. Predictors of recurrent falls in people with Parkinson's disease and proposal for a predictive tool. J Parkinsons Dis. 2017;7(2):313-24. https://doi.org/10.3233/JPD-160934 27 Kim JS Jang W Cho JW Ahn JY Kim HT Bedside cognitive assessments and falls risk in Parkinson's disease Neurol Sci 2013 01 34 1 75 78 https://doi.org/10.1007/s10072-011-0906-8 27. Kim JS, Jang W, Cho JW, Ahn JY, Kim HT. Bedside cognitive assessments and falls risk in Parkinson's disease. Neurol Sci. 2013 Jan;34(1):75-8. https://doi.org/10.1007/s10072-011-0906-8 28 Sixel-Döring F Trautmann E Mollenhauer B Trenkwalder C Associated factors for REM sleep behavior disorder in Parkinson disease Neurology 2011 09 77 11 1048 1054 https://doi.org/10.1212/WNL.0b013e31822e560e 28. Sixel-Döring F, Trautmann E, Mollenhauer B, Trenkwalder C. Associated factors for REM sleep behavior disorder in Parkinson disease. Neurology. 2011 Sep;77(11):1048-54. https://doi.org/10.1212/WNL.0b013e31822e560e 29 Williams DR Watt HC Lees AJ Predictors of falls and fractures in bradykinetic rigid syndromes: a retrospective study J Neurol Neurosurg Psychiatry 2006 04 77 4 468 473 https://doi.org/10.1136/jnnp.2005.074070 29. Williams DR, Watt HC, Lees AJ. Predictors of falls and fractures in bradykinetic rigid syndromes: a retrospective study. J Neurol Neurosurg Psychiatry. 2006 Apr;77(4):468-73. https://doi.org/10.1136/jnnp.2005.074070 30 Bryant MS Rintala DH Hou JG Rivas SP Fernandez AL Lai EC The relation of falls to fatigue, depression and daytime sleepiness in Parkinson's disease Eur Neurol 2012 67 6 326 330 https://doi.org/10.1159/000335877 30. Bryant MS, Rintala DH, Hou JG, Rivas SP, Fernandez AL, Lai EC, et al. The relation of falls to fatigue, depression and daytime sleepiness in Parkinson's disease. Eur Neurol. 2012;67(6):326-30. https://doi.org/10.1159/000335877 31 Parashos SA Wielinski CL Giladi N Gurevich T Falls in Parkinson disease: analysis of a large cross-sectional cohort J Parkinsons Dis 2013 3 4 515 522 https://doi.org/10.3233/JPD-130249 31. Parashos SA, Wielinski CL, Giladi N, Gurevich T. Falls in Parkinson disease: analysis of a large cross-sectional cohort. J Parkinsons Dis. 2013;3(4):515-22. https://doi.org/10.3233/JPD-130249 32 Soliman Y Meyer R Baum N Falls in the elderly secondary to urinary symptoms Rev Urol 2016 18 1 28 32 32. Soliman Y, Meyer R, Baum N. Falls in the elderly secondary to urinary symptoms. Rev Urol. 2016;18(1):28-32. 33 Mitchell RJ Lord SR Harvey LA Close JC Associations between obesity and overweight and fall risk, health status and quality of life in older people Aust N Z J Public Health 2014 02 38 1 13 18 https://doi.org/10.1111/1753-6405.12152 33. Mitchell RJ, Lord SR, Harvey LA, Close JC. Associations between obesity and overweight and fall risk, health status and quality of life in older people. Aust N Z J Public Health. 2014 Feb;38(1):13-8. https://doi.org/10.1111/1753-6405.12152 34 Schrag A Barone P Brown RG Leentjens AF McDonald WM Starkstein S Depression rating scales in Parkinson's disease: critique and recommendations Mov Disord 2007 06 22 8 1077 1092 https://doi.org/10.1002/mds.21333 34. Schrag A, Barone P, Brown RG, Leentjens AF, McDonald WM, Starkstein S, et al. Depression rating scales in Parkinson's disease: critique and recommendations. Mov Disord. 2007 Jun;22(8):1077-92. https://doi.org/10.1002/mds.21333 35 Leentjens AF Dujardin K Marsh L Martinez-Martin P Richard IH Starkstein SE Anxiety rating scales in Parkinson's disease: critique and recommendations Mov Disord 2008 10 23 14 2015 2025 https://doi.org/10.1002/mds.22233 35. Leentjens AF, Dujardin K, Marsh L, Martinez-Martin P, Richard IH, Starkstein SE, et al. Anxiety rating scales in Parkinson's disease: critique and recommendations. Mov Disord. 2008 Oct;23(14):2015-25. https://doi.org/10.1002/mds.22233 36 Fernandez HH Aarsland D Fénelon G Friedman JH Marsh L Tröster AI Scales to assess psychosis in Parkinson's disease: critique and recommendations Mov Disord 2008 03 23 4 484 500 https://doi.org/10.1002/mds.21875 36. Fernandez HH, Aarsland D, Fénelon G, Friedman JH, Marsh L, Tröster AI, et al. Scales to assess psychosis in Parkinson's disease: critique and recommendations. Mov Disord. 2008 Mar;23(4):484-500. https://doi.org/10.1002/mds.21875 37 Mactier K Lord S Godfrey A Burn D Rochester L The relationship between real world ambulatory activity and falls in incident Parkinson's disease: influence of classification scheme Parkinsonism Relat Disord 2015 03 21 3 236 242 https://doi.org/10.1016/j.parkreldis.2014.12.014 37. Mactier K, Lord S, Godfrey A, Burn D, Rochester L. The relationship between real world ambulatory activity and falls in incident Parkinson's disease: influence of classification scheme. Parkinsonism Relat Disord. 2015 Mar;21(3):236-42. https://doi.org/10.1016/j.parkreldis.2014.12.014 38 Kerr GK Worringham CJ Cole MH Lacherez PF Wood JM Silburn PA Predictors of future falls in Parkinson disease Neurology 2010 07 75 2 116 124 https://doi.org/10.1212/WNL.0b013e3181e7b688 38. Kerr GK, Worringham CJ, Cole MH, Lacherez PF, Wood JM, Silburn PA. Predictors of future falls in Parkinson disease. Neurology. 2010 Jul;75(2):116-24. https://doi.org/10.1212/WNL.0b013e3181e7b688 39 Custodio N Lira D Herrera-Perez E Montesinos R Castro-Suarez S Cuenca-Alfaro J Predictive model for falling in Parkinson disease patients eNeurologicalSci 2016 11 5 20 24 https://doi.org/10.1016/j.ensci.2016.11.003 39. Custodio N, Lira D, Herrera-Perez E, Montesinos R, Castro-Suarez S, Cuenca-Alfaro J, et al. Predictive model for falling in Parkinson disease patients. eNeurologicalSci. 2016 Nov;5:20-4. https://doi.org/10.1016/j.ensci.2016.11.003 40 Wood BH Bilclough JA Bowron A Walker RW Incidence and prediction of falls in Parkinson's disease: a prospective multidisciplinary study J Neurol Neurosurg Psychiatry 2002 06 72 6 721 725 https://doi.org/10.1136/jnnp.72.6J21 40. Wood BH, Bilclough JA, Bowron A, Walker RW. Incidence and prediction of falls in Parkinson's disease: a prospective multidisciplinary study. J Neurol Neurosurg Psychiatry. 2002 Jun;72(6):721-5. https://doi.org/10.1136/jnnp.72.6J21 41 Lindholm B Nilsson MH Hansson O Hagell P External validation of a 3-step falls prediction model in mild Parkinson's disease J Neurol 2016 12 263 12 2462 2469 https://doi.org/10.1007/s00415-016-8287-9 41. Lindholm B, Nilsson MH, Hansson O, Hagell P External validation of a 3-step falls prediction model in mild Parkinson's disease. J Neurol. 2016 Dec;263(12):2462-9. https://doi.org/10.1007/s00415-016-8287-9