A multidisciplinary assessment instrument to predict fall risk in hospitalized patients: A prospective matched pair case study

Heather Chung, Aida Coralic


Objective: To identify fall risk for hospitalized patients utilizing a multidisciplinary assessment tool based on patients’ physical, mental, pharmacological, and metabolic data.

Methods: A prospective case-control design comparing 48 patients who fell (incidence group) and 48 patients who did not experience fall (control group), based on patients’ age, gender, and hospital unit location. The study was conducted over an 8-month period at a large academic hospital. Setting: The Methodist Hospital, tertiary care academic referral center with 824 operating beds in Houston, TX. Participants: One hundred and twenty patients, sixty patients who fell, and sixty control subjects. Main Outcome Measures: The sensitivity and specificity of variables identified in logistic regression are able to distinguish patients who fell from patients who did not fall.

Results: Logistic regression results identified six variables (2 summary variables and 4 individual variables) that correctly classified patients with 90% sensitivity (patients who fell) and 90% specificity (patients who did not fall). The first variable was an 11-item summary variable that included history, weakness or balance problem, altered mental status or confusion, visual impairment, dizziness or vertigo, urinary tract infection or abnormal urinary analysis (UA), diuretics/IV drips, continence, acute renal failure (ARF), antihypertensives and narcotics. The second variable represented the combination of 3 medication classes: neuroleptic, anticonvulsant and antidepressant. The third variable that had a negative impact on fall risk was the presence of a therapeutic anticoagulant. The other 3 significant variables were hypoglycemia, vital sign abnormality, and low hemoglobin.

Conclusions: A multidisciplinary fall-risk assessment tool that screens combinations of physiological, pharmacological and metabolic patient factors improves the probability of correctly distinguishing patients who were more likely to fall from those patients who were less likely to fall.


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DOI: https://doi.org/10.5430/jnep.v6n6p1

Journal of Nursing Education and Practice

ISSN 1925-4040 (Print)   ISSN 1925-4059 (Online)

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