Dissertation/Thesis Abstract

Towards development of a fall injury prediction model
by Hester, Amy Lynn, Ph.D., University of Arkansas for Medical Sciences, 2015, 106; 3704005
Abstract (Summary)

Although there has been significant progress of evidence based practice in the last decade, falls remain the most commonly reported adverse events in hospitalized adults. A majority of research has focused on fall prevention and fall prediction with far fewer studies focusing on injurious falls specifically. While previous research has resulted in validated tools used to predict falls, no tools that specifically predict the risk for injurious falls have been scientifically developed. The specific aims of this study were to perform a secondary data analysis to 1) identify patient factors that are associated with injurious falls in hospitalized adults and 2) to determine if these factors are predictors for falls resulting in injury.

The setting of this study was an academic medical center in the south central United States. A total of 1,369 falls were included of which 381 (27.8%) resulted in some form of injury. Predictor variables examined in this study included age, gender, fall history, use of diuretics, use of central nervous system (CNS) medications, cognitive impairment (CI), primary discharge diagnosis, abnormal laboratory values, impaired mobility and body mass index (BMI).

Bivariate analysis revealed a positive association between injurious falls and primary discharge diagnoses that included symptoms, signs and ill-defined conditions (p=0.019). In logistic regression analysis, having a primary discharge diagnosis of symptoms, signs and ill-defined conditions was the only statistically significant predictor for injurious falls (OR 1.74; 95%CI= 1.03-2.94; P=.037). Findings from this study may assist clinicians to direct injury prevention interventions to patients most at risk for falls resulting in injury.

Indexing (document details)
Advisor: Tsai, Pao-Feng
Commitee: Hogan, William, Lefler, Leanne, Mitchell, Anita, Rettiganti, Malik
School: University of Arkansas for Medical Sciences
Department: Nursing Science
School Location: United States -- Arkansas
Source: DAI-B 76/09(E), Dissertation Abstracts International
Subjects: Nursing
Keywords: Falls, Injury, Prediction
Publication Number: 3704005
ISBN: 978-1-321-76147-4
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