4.6 Article

Fall Prediction According to Nurses' Clinical Judgment: Differences Between Medical, Surgical, and Geriatric Wards

期刊

JOURNAL OF THE AMERICAN GERIATRICS SOCIETY
卷 60, 期 6, 页码 1115-1121

出版社

WILEY
DOI: 10.1111/j.1532-5415.2012.03957.x

关键词

accidental falls; inpatients; risk assessment; sensitivity and specificity; clinical judgment

资金

  1. Borgerhoff Award for Geriatrics, Belgium

向作者/读者索取更多资源

Objectives To assess the value of nurses' clinical judgment (NCJ) in predicting hospital inpatient falls. Design Prospective multicenter study. Setting Six Belgian hospitals. Participants Two thousand four hundred seventy participants (mean age 67.6 +/- 18.3; female, 55.7%) on four surgical (n=812, 32.9%), eight geriatric (n=666, 27.0%), and four general medical wards (n=992, 40.1%) were included upon admission. All participants were hospitalized for at least 48hours. Measurements Within 24hours after admission, nurses gave their judgment on the question Do you think your patient is at high risk for falling? Nurses were not trained in assessing fall risk. Falls were documented on a standardized incident report form. Results During hospitalization, 143 (5.8%) participants experienced one or more falls, accounting for 202 falls and corresponding to an overall rate of 7.9 falls per 1,000 patient days. NCJ of participant's risk of falling had high sensitivity (78-92%) with high negative predictive value (94-100%) but low positive predictive value (417%). Although false-negative rates were low (8-22%) for all departments and age groups, false-positive rates were high (55-74%), except on surgical and general medical wards and in participants younger than 75. Conclusion This analysis, based on multicenter data and a large sample size, suggests that NCJ can be recommended on surgical and general medical wards and in individuals younger than 75, but on geriatric wards and in participants aged 75 and older, NCJ overestimates risk of falling and is thus not recommended because expensive comprehensive fall-prevention measures would be implemented in a large number of individuals who do not need it.

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