Abstract
Hospital in-patient falls constitute a prominent problem in terms
of costs and consequences. Geriatric institutions are most often
affected, and common screening tools cannot predict in-patient falls
consistently. Our objectives are to derive comprehensible fall risk
classification models from a large data set of geriatric in-patients'
assessment data and to evaluate their predictive performance (aim#1),
and to identify high-risk subgroups from the data (aim#2).A data
set of n = 5,176 single in-patient episodes covering 1.5 years of
admissions to a geriatric hospital were extracted from the hospital's
data base and matched with fall incident reports (n = 493). A classification
tree model was induced using the C4.5 algorithm as well as a logistic
regression model, and their predictive performance was evaluated.
Furthermore, high-risk subgroups were identified from extracted classification
rules with a support of more than 100 instances.The classification
tree model showed an overall classification accuracy of 66\%, with
a sensitivity of 55.4\%, a specificity of 67.1\%, positive and negative
predictive values of 15\% resp. 93.5\%. Five high-risk groups were
identified, defined by high age, low Barthel index, cognitive impairment,
multi-medication and co-morbidity.Our results show that a little
more than half of the fallers may be identified correctly by our
model, but the positive predictive value is too low to be applicable.
Non-fallers, on the other hand, may be sorted out with the model
quite well. The high-risk subgroups and the risk factors identified
(age, low ADL score, cognitive impairment, institutionalization,
polypharmacy and co-morbidity) reflect domain knowledge and may be
used to screen certain subgroups of patients with a high risk of
falling. Classification models derived from a large data set using
data mining methods can compete with current dedicated fall risk
screening tools, yet lack diagnostic precision. High-risk subgroups
may be identified automatically from existing geriatric assessment
data, especially when combined with domain knowledge in a hybrid
classification model. Further work is necessary to validate our approach
in a controlled prospective setting.
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