TY - JOUR
T1 - A prediction model of falls for patients with neurological disorder in acute care hospital
AU - Yoo, Sung Hee
AU - Kim, Sung Reul
AU - Shin, Yong Soon
N1 - Publisher Copyright:
© 2015 Elsevier B.V.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2015/9/15
Y1 - 2015/9/15
N2 - Abstract For the prevention of falls, individual fall risk assessment is the necessary first step. Thus, we attempted to identify independent risk factors for falls and develop a prediction model using a scoring system for patients with neurological disorders in acute hospital settings. This study was a secondary analysis of a previous study performed to compare the reliability and validity of three well-known fall assessment tools in patients with neurological disorders. We considered comorbid diseases and potential medications in addition to variables included in the three tools. Multiple logistic regression analysis was used to develop a prediction model for falls. Predictive scores were calculated using the proportional odds ratio (OR) of each predictor. The discriminative power of this model was evaluated by receiver-operating characteristic (ROC) area under the curve (AUC) analysis. A total of 32 falls were noted among 1018 patients. History of falls (OR, 4.01; 95% CI, 1.61-9.98; p =.003), cerebrovascular disease (CVD) (OR, 2.61; 95% CI, 1.11-6.14; p =.028), severe impaired gait (OR, 7.28; 95% CI, 2.45-21.65; p <.001), and overestimate of one's own gait ability (OR, 9.14; 95% CI, 3.89-21.45; p <.001) were identified as meaningful predictors for falling after adjusting for age, diabetes, confusion or disorientation, up-and-go test, altered elimination, and antipsychotics by univariate analysis. The discriminative power of fall risk score calculated by the prediction model was 0.904 of AUC (p <.001). Our results suggest that in addition to fall history and the presence of CVD, neurological assessment for gait and insight into gait ability are imperative to predict falls in patients with neurological disorders.
AB - Abstract For the prevention of falls, individual fall risk assessment is the necessary first step. Thus, we attempted to identify independent risk factors for falls and develop a prediction model using a scoring system for patients with neurological disorders in acute hospital settings. This study was a secondary analysis of a previous study performed to compare the reliability and validity of three well-known fall assessment tools in patients with neurological disorders. We considered comorbid diseases and potential medications in addition to variables included in the three tools. Multiple logistic regression analysis was used to develop a prediction model for falls. Predictive scores were calculated using the proportional odds ratio (OR) of each predictor. The discriminative power of this model was evaluated by receiver-operating characteristic (ROC) area under the curve (AUC) analysis. A total of 32 falls were noted among 1018 patients. History of falls (OR, 4.01; 95% CI, 1.61-9.98; p =.003), cerebrovascular disease (CVD) (OR, 2.61; 95% CI, 1.11-6.14; p =.028), severe impaired gait (OR, 7.28; 95% CI, 2.45-21.65; p <.001), and overestimate of one's own gait ability (OR, 9.14; 95% CI, 3.89-21.45; p <.001) were identified as meaningful predictors for falling after adjusting for age, diabetes, confusion or disorientation, up-and-go test, altered elimination, and antipsychotics by univariate analysis. The discriminative power of fall risk score calculated by the prediction model was 0.904 of AUC (p <.001). Our results suggest that in addition to fall history and the presence of CVD, neurological assessment for gait and insight into gait ability are imperative to predict falls in patients with neurological disorders.
KW - Acute care
KW - Fall
KW - Fall risk assessment
KW - Gait
KW - Neurological disorder
KW - Prediction model
UR - http://www.scopus.com/inward/record.url?scp=84939256786&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84939256786&partnerID=8YFLogxK
U2 - 10.1016/j.jns.2015.06.027
DO - 10.1016/j.jns.2015.06.027
M3 - Article
C2 - 26104568
AN - SCOPUS:84939256786
SN - 0022-510X
VL - 356
SP - 113
EP - 117
JO - Journal of the Neurological Sciences
JF - Journal of the Neurological Sciences
IS - 1-2
M1 - 13856
ER -