Abstract
AimMost predictive models for falls developed previously were awkward to use because of their complexity. We developed and validated a new easier-to-use predictive model for falls of adult inpatients using easily accessible information including the public ADL scale in Japan.MethodsWe retrospectively analyzed data from Japanese adult inpatients in an acute care hospital from 2012 to 2015. Two-thirds of cases were randomly extracted to the test set and one-third to the validation set. Data including age, sex, activity of daily living (ADL), public scales in Japan of ADL “bedriddenness rank,” and cognitive function in daily living, hypnotic medications, previous falls, and emergency admission were derived from hospital records. Falls during hospitalization were identified from incident reports. Two predictive models were created by multivariate analysis, each of which was assessed by area under the curve (AUC) from the validation set.ResultsA total of 7,858 adult participants were available. The AUC of model 1, using 13 factors—age, sex (male), emergency admission, use of ambulance, referral letter, admission to Neurosurgery, admission to Internal Medicine, use of hypnotic medication, permanent damage by stroke, history of falls, visual impairment, independence of eating, and bedriddenness rank—with low mutual collinearity and showing significant relationship by multivariate logistic regression analysis, was 0.789 in the validation set. The AUC of parsimonious model 2, using age and seven factors—sex (male), emergency admission, admission to Neurosurgery, use of hypnotic medication, history of falls, independence of eating, and bedriddenness rank—showing statistical significance by multivariate analysis in model 1, was 0.787 in the validation set.ConclusionsWe proposed new predictive models for inpatients’ fall using the public ADL scales in Japan, which had a higher degree of usability because of their use of simpler and fewer (8 or 13) predictors, especially parsimonious model 2.
Highlights
Falls can be devastating events leading to severe injuries [1], restriction of activities [2], or reduced activities of daily living (ADLs) [3]
Two predictive models were created by multivariate analysis, each of which was assessed by area under the curve (AUC) from the validation set
We proposed new predictive models for inpatients’ fall using the public ADL scales in Japan, which had a higher degree of usability because of their use of simpler and fewer (8 or 13) predictors, especially parsimonious model 2
Summary
Falls can be devastating events leading to severe injuries [1], restriction of activities [2], or reduced activities of daily living (ADLs) [3]. Previous community-based prospective cohort studies have identified a variety of risk factors for falls, such as history of a fall [7, 10], lower extremity weakness [7, 10], older age [6, 11, 12], female sex [11], cognitive impairment [6, 10, 11], balance problems [10, 11], use of psychotropic drugs [11], arthritis [10, 11], history of stroke [10, 12], orthostatic hypotension [10, 11], dizziness [11], syncope [13], and nocturia [14, 15] In addition to such risk factors, several predictive models for falls have been developed, including the Morse Fall Scale [16], St Thomas Risk Assessment Tool in Falling Elderly Inpatients [17], Tinetti mobility test [18], and Hendrich II Fall Risk Model (HFRM) [19]. We developed predictive models, less complicated to use and with acceptable and satisfactory accuracy, using more readily available information routinely obtained on admission to commonplace Japanese hospitals, which were subsequently validated
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