TY  - JOUR
PY  - 2020//
TI  - Predicting short-term risk of falls in a high-risk group with dementia
JO  - Journal of the American Medical Directors Association
A1  - Mehdizadeh, Sina
A1  - Sabo, Andrea
A1  - Ng, Kimberley-Dale
A1  - Mansfield, Avril
A1  - Flint, Alastair J.
A1  - Taati, Babak
A1  - Iaboni, Andrea
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - OBJECTIVES: To develop a prognostic model to predict the probability of a short-term fall (within the next 7 to 30 days) in older adults with dementia.     DESIGN: Prospective observational study.     SETTING AND PARTICIPANTS: Fifty-one individuals with dementia at high risk of falls from a specialized dementia inpatient unit.     METHODS: Clinical and demographic measures were collected and a vision-based markerless motion capture was used to record the natural gait of participants over a 2-week baseline. Falls were tracked throughout the length of stay. Cox proportional hazard regression analysis was used to build a prognostic model to determine fall-free survival probabilities at 7 days and at 30 days. The model's discriminative ability was also internally validated.     RESULTS: Fall history and gait stability (estimated margin of stability) were statistically significant predictors of time to fall and included in the final prognostic model. The model's predicted survival probabilities were close to observed values at both 7 and 30 days. The area under the receiver operating curve was 0.80 at 7 days, and 0.67 at 30 days and the model had a discrimination performance (the Harrel concordance index) of 0.71.     CONCLUSIONS AND IMPLICATIONS: Our short-term falls risk model had fair to good predictive and discrimination ability. Gait stability and recent fall history predicted an imminent fall in our population. This provides some preliminary evidence that the degree of gait instability may be measureable in natural everyday gait to allow dynamic falls risk monitoring. External validation of the model using a separate data set is needed to evaluate model's predictive performance.<p />  <p>Language: en</p>
LA  - en
SN  - 1525-8610
UR  - http://dx.doi.org/10.1016/j.jamda.2020.07.030
ID  - ref1
ER  -