An Exploration of the Association of Patient Characteristics and Pharmacological Treatments to Inpatient Falls among Patients At-risk for Falling during Hospitalization

Abstract

Introduction/Background: Falls may be the most commonly reported incidents in the acute care setting, and a frequent cause of harm in the hospital. Studies have focused on identifying risk factors for falls and interventions aimed at reducing the risk of falling. The purpose of this study was to describe and compare patient characteristics and pharmacological treatments between patients who fell and patients who did not fall, among a sample of patients deemed to be at-risk for falling during hospitalization. Additionally, the study aimed to identify independent predictors of falls among patients at-risk for falls during hospitalization. Methods: An observational, cross-sectional study involving the analysis of retrospective patient records. A convenience sample of all patients with a Morse Fall Scale of >45 over a 1-year period, was extracted from electronic medical records. Descriptive statistics of demographic characteristics and medication classes were generated to compare those who fell to those who did not fall. To examine significant predictors of falls, logistic regression (univariate and multivariable) were employed. Results/Findings: The sample consisted of 4,978 valid patient records. White non-Hispanics constituted 60% of the falls group but only 24% of the non-falls group. A larger proportion of those who fell received antiemetics or insulin compared to those who did not fall. Univariate regression analysis found that race and 39 medication classes were independently associated with falls. Multivariable regression analysis showed that race and 11 medication classes were associated with the odds of falling. Conclusions: White patients were more likely to fall than patients of other races. New associations were found between the odds of falling and antiprotozoals, diagnostic agents, and gastrointestinal agents. Prospective studies are needed to determine the predictive accuracy of these factors. Bedside practitioners should understand the mechanism and onset of action of medications so that individualized safety precautions may be implemented. By including classes of medications as part of fall-risk assessment, patient safety may be optimized and falls avoided in this high risk population.