Predictive validity of the Hendrich fall risk model II in an acute ...

A major strategy of the fall prevention programs
supported by evidence has been the use of a fall-risk
assessment tool to identify patients highly at risk of
falling on which to allocate the resources and concentrate
the healthcare efforts for prevention.
Fall risk assessment tools have been increasingly
developed in recent years, but most instruments have
not been evaluated for reliability, validity and clinical
usefulness.
The Hendrich fall risk model II (HFRM II) has shown to
have good predictive values with sensitivity of 74.9% and
specificity of 73.9% in the pilot study. Also the fall risk
factors included in the tool show a statistically significant
correlation with falls.
What this paper adds
The original version of Hendrich fall risk model II was
translated into Italian.
The reliability, validity and clinical feasibility of HFRM II
were tested and evaluated in an Italian geriatric unit.
Our study confirms previous research indicating that the
HFRM II is a reliable and valid tool to screen elderly
inpatients at risk of falling.
1. Introduction
Falls are the most common adverse events reported in
acute care hospitals. Approximately 2–12% of patients
experience at least one fall during their hospital stay
depending on ward type and hospital setting and population
(Coussement et al., 2008). Literature reporting
hospital falls shows that older patients are the most likely
to fall with great variability in the incidence of falls (2.2–
17.1 falls per 1000 patient days). In the elderly falls can
cause physical, psychological as well as social consequences.
The most common physical consequences are
bruises and minor injuries (28%), severe wounds of the soft
tissues (11.4%) and bone fractures (5%) (Kannus et al.,
2005). Moreover falls may produce psychological and
social consequences such as anxiety, loss of confidence,
impaired rehabilitation and function, increased costs for
the healthcare system because of the prolonged patient
stay and of the treatments for the physical consequences
(Rubenstein and Josephson, 2001; Oliver et al., 2004).
In addition, inpatients falls may result in feelings of
guilt and anxiety for healthcare personnel, complaint or
litigation from patients’ relatives who feel that falls are
unacceptable in a setting that should be safe for their
family members (Oliver, 2007).
The risk of falling cannot be completely eliminated in
acute care settings, but it can be reduced through the
implementation of effective fall prevention programs
(Gillespie et al., 2003; Rubenstein, 2006). A major strategy
of the fall prevention programs supported by evidence has
been the use of a fall-risk assessment tool to identify
patients at high risk of falling on which to allocate the
resources and concentrate the healthcare personnel
preventive efforts (Rubenstein and Josephson, 2001;
D. Ivziku et al. / International Journal of Nursing Studies 48 (2011) 468–474 469
Gillespie et al., 2003). Such assessment tools typically
consist of a rating or scoring system designed to reflect the
cumulative effect of known risk factors (Scott et al., 2007).
Several tools have been developed and tested in
hospital settings in the last years. Often the choice of
which is left to the nursing personnel who will use it in the
clinical practice. It is important that nurses select the most
appropriate fall-risk assessment tool for the patients in
their settings as the lack of accuracy of such tool can lead to
inappropriate distribution of fall prevention resources,
contributing to failure of fall prevention programs (Oliver,
2007; Perrell et al., 2001).
Perrell et al. believe that the risk assessment tools
should be selected on the basis of certain criteria: have
good psychometric properties (identify correctly high risk
population/true positive and not at risk population-true
negative), should be tested in a similar population in which
it is intended to be used, require not much time for the
administration and should have a cutoff score indicating
the need for intervention (Perrell et al., 2001).
A literature research on Medline and CINHAL databases,
using the search terms ‘‘accidental falls, risk assessment,
tools or scales, hospital or inpatients, older adult and
validation study’’, has allowed the identification of 4
literature reviews (Perrell et al., 2001; Myers, 2003; Oliver
et al., 2004; Scott et al., 2007) which compared the
reliability and validity of screening fall risk tools tested in
acute care settings in order to assist clinical nurses in
selecting the most appropriate assessment tools for the
risk profile on the intended population.
Perrell et al. recognized in their review 14 assessment
tools tested in acute care settings, but only two of them, the
STRATIFY and the Schmid, presented good sensitivity and
specificity values, however none of them has been tested
so far in other publications (Perrell et al., 2001).
Likewise Myers, who analyzed 47 articles published
from 1981 to 2001 testing more than 30 different falls risk
assessment tools, arrived to the conclusions that although
many tools have been developed, few have undergone
validity and reliability testing, and even the most
promising tools when tested by other researchers in
different clinical settings showed a decreased in specificity
(Myers, 2003).
Oliver et al. in the examined literature found only two
risk assessment tools (Morse fall scale and STRATIFY) that
had undergone a rigorous prospective validation study
(Oliver et al., 2004).
Scott et al. came to the same conclusion that there is no
tool which can be applied reliably across different settings
to predict risk of falling in the elderly as they have not been
validated in more than one setting and have no strong
predictive values and recommend further research to
develop new valid and reliable tools (Scott et al., 2007).
It follows that at present there are no valid and reliable
tools which can be recommended in a hospital setting for
elderly patients.
Recently a new promising tool has been developed, the
Hendrich fall risk model II (HFRM II), a modified version of
a previous tool developed by the same author in 1995. It
has been shown to have good predictive values with
sensitivity of 74.9% and specificity of 73.9% in the