New predictive models for falls among inpatients using public ADL scale in Japan: A retrospective observational study of 7,858 patients in acute care setting

PloS One
Masaki TagoShu-Ichi Yamashita

Abstract

Most 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. We 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. A 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 impairm...Continue Reading

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