  Identification of risk factors associated with falls in people with
Parkinson's Disease (PD) is important due to their high risk of falling. In
this study, various ways of utilizing the Unified Parkinson's Disease Rating
Scale (UPDRS) were assessed for the identification of risk factors and for the
prediction of falls. Three statistical methods for classification were
considered:decision trees, random forests, and logistic regression. UPDRS
measurements on 51 participants with early stage PD, who completed monthly
falls diaries for 12 months of follow-up were analyzed. All classification
methods applied produced similar results in regards to classification accuracy
and the selected important variables. The highest classification rates were
obtained from model with individual items of the UPDRS with 80% accuracy (85%
sensitivity and 77% specificity), higher than in any previous study. A
comparison of the independent performance of the four parts of the UPDRS
revealed the comparably high classification rates for Parts II and III of the
UPDRS. Similar patterns with slightly different classification rates were
observed for the 6- and 12-month of follow-up times. Consistent predictors for
falls selected by all classification methods at two follow-up times are:
thought disorder for UPDRS I, dressing and falling for UPDRS II, hand
pronate/supinate for UPDRS III, and sleep disturbance and symptomatic
orthostasis for UPDRS IV. While for the aggregate measures, subtotal 2 (sum of
UPDRS II items) and bradykinesia showed high association with fall/non-fall.
Fall/non-fall occurrences were more associated with individual items of the
UPDRS than with the aggregate measures. UPDRS parts II and III produced
comparably high classification rates for fall/non-fall prediction. Similar
results were obtained for modelling data at 6-month and 12-month follow-up
times.
