Factors Influencing Falls in High- and Low-Risk Patients in a Tertiary Hospital in Korea

Abstract

Objective The purpose of this study was to explore the characteristics and predictors of falls in high- and low-risk inpatients in a tertiary hospital in Korea. Methods Fallers' data were extracted from quality improvement reports and electronic health records from June 1, 2014, to May 31, 2015. Data on nonfallers matched by the length of hospitalization and medical departments of fallers were extracted from electronic health records. Participants were classified into a high- or a low-risk group based on their Morse Fall Scale score, fall risk–related symptoms, and medications known to increase fall risk. Characteristics of falls and risk factors were analyzed using descriptive statistics and logistic regression analysis, respectively. Results In the high-risk group, education, surgery, department, impaired mobility, intravenous catheter placement, use of ambulatory aid, gait disturbance, and some medications were significantly different between the fallers and nonfallers. From these variables, education, operation, department, intravenous catheter placement, gait disturbance, and use of narcotics, vasodilators, antiarrhythmics, and hypnotics were statistically significant factors for falls. In the low-risk group, sex, age, length of hospitalization, surgery, department, diagnosis, and mental status were significantly different between the fallers and nonfallers. From these, sex, age, length of hospitalization, surgery, and liver-digestive diseases were statistically significant factors for falls. Conclusions Characteristics and risk factors for falls differed between the risk groups. Fall prevention strategies need to be tailored to the risk groups and fall risk assessment tools need to be revised accordingly.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…For the topic of falls, we identified 24 studies that met inclusion criteria. Of these studies, eight used a retrospective cohort design 40 41 42 43 44 45 46 47 ; seven used a prospective cohort design 48 49 50 51 52 53 54 ; six were secondary analyses of research data obtained from prospective, retrospective, and cross-sectional studies 55 56 57 58 59 60 ; one used mixed methods wherein data from a public dataset were used in conjunction with measurements collected from sensors 61 ; and one was a meta-analysis of prospective cohort and observational studies. 62 Ten of the studies used health records as a source of data but in two of these studies, 44 47 it was not clear whether the records were electronic when they were obtained.…”

“…Several of the studies, including two of the secondary analyses, incorporated data from mobility and gait sensors. 48 49 51 53 55 60 61 Registries and administrative datasets were used in eight studies, 40 41 42 43 45 46 50 56 while questionnaires or surveys were a source of data for four studies. 49 51 57 60 With the exception of one study that employed sensor data from 17-year-old persons, 55 all study participants were community dwelling, inpatient, and outpatient adults.…”

“…In several studies, the fall was self-reported, 49 51 57 60 63 but if it occurred while the participant was in an inpatient setting or being tracked in an outpatient setting, it was often documented in medical records, registries, or administrative databases used to track adverse events. 40 41 42 43 44 45 46 47 48 50 52 56 These differences in measuring the outcome are important, in that predictive models may then be more or less accurate, merely because of the accuracy or inaccuracy of the outcome measurement. The types of predictors across studies were quite consistent, nevertheless.…”

“…Diagnoses and/or symptoms of the participants were tested as predictors in most of the studies. 40 41 42 43 44 45 46 47 49 50 51 52 54 56 57 58 59 60 62 63 Several categories of predictors were noteworthy, including strength, balance, and gait test scores 40 46 47 48 49 50 51 52 53 54 55 56 57 59 60 61 62 63 and nutritional status. 42 56 59 In 15 studies, prediction models were developed and evaluated with regression models.…”

“…42 56 59 In 15 studies, prediction models were developed and evaluated with regression models. 40 42 43 44 45 46 47 51 52 56 57 59 60 62 63 Data science methods also leveraged supervised and unsupervised ML methods, including neural networks for developing risk prediction models, improving prediction of fall risk, and automating selection of data from electronic records for use in fall risk prediction algorithms. 41 45 46 48 49 50 52 53 54 55 58 61…”