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

Predictive validity of the Hendrich fall risk model II in an
acute geriatric unit
Dhurata Ivziku a , Maria Matarese b, *, Claudio Pedone a
a Campus Bio-Medico University Hospital, Rome, Italy
b School of Nursing, Campus Bio-Medico University, Rome, Italy
ARTICLE INFO
Article history:
Received 12 March 2010
Received in revised form 31 July 2010
Accepted 4 September 2010
Keywords:
Assessment tool
Elderly
Falls
Geriatric unit
Risk factors
Sensitivity and specificity
International Journal of Nursing Studies 48 (2011) 468–474
ABSTRACT
* Corresponding author.
E-mail addresses: d.ivziku@unicampus.it (D. Ivziku),
m.matarese@unicampus.it (M. Matarese), c.pedone@unicampus.it
(C. Pedone).
Contents lists available at ScienceDirect
International Journal of Nursing Studies
0020-7489/$ – see front matter ß 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ijnurstu.2010.09.002
journal homepage: www.elsevier.com/ijns
Background: Falls are the most common adverse events reported in acute care hospitals, and
older patients are the most likely to fall. The risk of falling cannot be completely eliminated,
but it can be reduced through the implementation of a fall prevention program. A major
evidence-based intervention to prevent falls has been the use of fall-risk assessment tools.
Many tools have been increasingly developed in recent years, but most instruments have not
been investigated regarding reliability, validity and clinical usefulness.
Objectives: This study intends to evaluate the predictive validity and inter-rater reliability
of Hendrich fall risk model II (HFRM II) in order to identify older patients at risk of falling in
geriatric units and recommend its use in clinical practice.
Design: A prospective descriptive design was used.
Setting: The study was carried out in a geriatric acute care unit of an Italian University
hospital.
Participants: All over 65 years old patients consecutively admitted to a geriatric acute care
unit of an Italian University hospital over 8-month period were enrolled.
Methods: The patients enrolled were screened for the falls risk by nurses with the HFRM II
within 24 h of admission. The falls occurring during the patient’s hospital stay were
registered. Inter-rater reliability, area under the ROC curve, sensitivity, specificity, positive
and negative predictive values and time for the administration were evaluated.
Results: 179 elderly patients were included. The inter-rater reliability was 0.87 (95% CI
0.71–1.00). The administration time was about 1 min. The most frequently reported risk
factors were depression, incontinence, vertigo. Sensitivity and specificity were respectively
86% and 43%. The optimal cut-off score for screening at risk patients was 5 with an
area under the ROC curve of 0.72. The risk factors more strongly associated with falls were
confusion and depression.
Conclusions: As falls of older patients are a common problem in acute care settings it is
necessary that the nurses use specific validate and reliable fall risk assessment tools in
order to implement the most effective prevention measures. Our findings provided
supporting evidence to the choice of the HFRM II to screen older patients at risk of falling in
acute care settings.
ß 2010 Elsevier Ltd. All rights reserved.
What is already known about the topic?
Falls are the most common adverse events reported in
acute care hospitals, especially in the aged.
The risk of falling cannot be completely eliminated, but it
can be reduced through the implementation of effective
fall prevention programs.

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

470
developmental study (Hendrich et al., 2003). In a following
study Kim and colleagues compared it with other two risk
assessment tools (Morse fall scale and STRATIFY) and
found that the HFRM II had a higher values of sensibility
and specificity (respectively 70% and 61.5%) than the other
two tools (Kim et al., 2007).
The risk factors included in this tool are particularly
significant for hospitalized older adults: in fact the risk of
falling is greatly influenced by acute illnesses that often
have, albeit temporary, an impact on physical and
cognitive functions, cause incontinence or require use of
drugs, such as the psychotropics (Scott et al., 2007).
The HFRM II has not been tested specifically on geriatric
population so far, but it was applied on general inpatients
population (medical–surgical, oncology, orthopedic and
gynecology units). We do not know if it can be considered a
valid and reliable tool for the fall risk assessment of elderly
inpatients and if its use can be recommended in geriatric
care units to screen elderly inpatients at risk of falling.
As widely suggested by literature, we decided to test
the predictive validity (psychometric values), reliability,
and feasibility of the HFRM II, before introducing this tool
in the routine clinical practice of a geriatric acute care unit
of an Italian Teaching Hospital. We decided not to compare
it with other existing tools as no one of those tested so far
had proved to be valid and reliable so that it can be
considered as gold standard.
2. Methods
2.1. Study design
To evaluate the psychometric properties of the tool we
used a descriptive prospective study, as it is the
quantitative methodology employed for the validation of
a tool. The study went on from June 1, 2008 to January 31,
2009.
2.2. Instruments
As the original version of HFRM II was in English, and no
official Italian version was available so far, we translated it
in Italian. We requested the authorization for the use as
well for the translation to the author, Ann Hendrich, who
also gave us detailed instructions regarding its use and
nurse training. We followed the forward–backward
translation method: the tool was translated in Italian by
two translators whose mother tongue was Italian but were
fluent in English. These two versions were then merged
into a first consensus version. The back translation from
Italian to English was made by a third person who was
fluent in English as well as in Italian, and this version was
sent to Ann Hendrich who approved the back translation.
The face validity of the Italian version was verified by a
staff nurse, a nurse researcher and a physician; consensus
on wording was achieved.
The original HFRM II, and its Italian translation, consists
of eight items: confusion/disorientation/impulsivity,
symptomatic depression, altered elimination, dizziness
or vertigo, male sex, prescribed antiepileptics, prescribed
benzodiazepines, and ‘‘get up and go’’ test, assessing the
D. Ivziku et al. / International Journal of Nursing Studies 48 (2011) 468–474
ability to stand up from a sitting position to a standing one.
Each item is assigned a specific score and the items are
weighted in different way according to different likelihood
of each of them to cause a fall. The score ranges from 0 to
16, where 16 represents the highest risk. The patient is
considered at high risk of falling if the score is 5 or higher
(Hendrich, 2007). To facilitate the compilation for the
Italian nurses, we listed the trade name of the most used
antiepileptic and benzodiazepine drugs instead of the
substance names, as in Italian hospital the trade names are
more commonly used to identify the drug prescriptions.
To register the fall during the patient hospital stay a
patient incident report form was introduced in which we
requested nurses to report information regarding all the
falls occurred in the unit (time and place of fall, fall
dynamic, consequences, days of patient hospital stay
before falling, etc.), as in the Teaching Hospital the fall
event was usually reported only if it caused physical
consequences. In this way it was possible to learn of all the
occurred fall events and relate them to the patient’s risk
scores.
2.3. Setting and population
The study was carried out at a geriatric unit of a
Teaching Hospital located in Rome. The geriatric unit had
12 beds reserved to patients over 65 years. The geriatric
patients admitted were treated for acute illnesses that had
caused the hospitalization. All the nurses employed in the
unit were qualified nurses with a bachelor degree in
Nursing. The nursing care delivery model used in the unit
was the total patient care model (Barros et al., 2007) and
each registered nurse took care daily of 6–8 patients.
All the patients aged 65 years or over, with or without
cognitive impairment, admitted to the geriatric unit during
the study period were included into the study. We
included also cognitive impairment patients because the
data could be collected easily through medical records or
patient relatives always present at the admission. We
excluded only the patients that were unconscious or
confined to bed as the full assessment with the tool could
not be completed (i.e. the get up and go test).
2.4. Data collection
All the 12 qualified nurses working in the unit were
involved in the study as each of them could assess patients
at the admission and the tool form was included in the
nursing assessment record of all geriatric patients. Specific
training of the nurses was carried out by the principal
investigator who worked as clinical nurse specialist in the
unit. The training consisted in the explanations of the
study aim, the meaning of each item and the score system,
according to the written instructions provided by the tool
author. The use of the fall incident report form that had to
be filled in after each patients’ fall was illustrated as well.
The principal investigator was present a few hours daily
in the unit during the period of the research to clarify and
discuss any doubt and collect the HFRM and the fall
incident report forms from the nursing record once they
were filled in. As the principal investigator moved in

another geriatric unit of the Teaching Hospital at the end of
January and the research protocol could not be followed as
previously decided, the study stopped after 8 months, even
if a longer survey period had been planned.
The patients were screened with HFRM II at the
moment of, or at the most within 24 h upon, admission
at the geriatric unit by the trained nurses.
The patients were followed-up from their admission
until their first fall, discharge, death or transfer to another
unit, and falls were reported on the appropriate fall
incident report form. We defined a fall as ‘‘an event which
results in a person coming to rest inadvertently on the
ground or floor or other lower level’’ (Hauer et al., 2003).
The inter-rater reliability was evaluated in the first
period of the research. Two nurses applied consequently
and independently the Hendrich II Model to a convenience
sample of older patients admitted to the unit during the
first 2 weeks of the study.
The time of administration was evaluated asking all
nurses to estimate the mean time needed to fill in the risk
assessment tool after the routine assessment of the
patients at the admission.
2.5. Statistic analysis
Descriptive statistics were used for demographic data.
To measure the reproducibility of results by more than one
rater, the inter-rater agreement was calculated using
kappa index at 95% CI. The likelihood of older patients to
fall was examined creating a 2 2 table to record patients
with high and low risk who fell or did not fall and the
predictive validity was tested calculating sensitivity,
specificity, predictive positive (PPV) and negative values
(NPV). The sensitivity was calculated dividing the numbers
of patients with high risk scores who fell by the total
number of patients who fell, and specificity dividing the
numbers of patients with low risk scores who did not fall
by the total number of patients who did not experience a
fall. The PPV was calculated dividing the number of older
patients with high risk score by the total number of
patients with high risk scores, while the NPV dividing the
number of patients with low-risk scores who did not fall by
the number of patients with low risk score. Associated 95%
CIs were also reported.
A receiver operating characteristic (ROC) analysis was
also carried out to examine the relationship between
sensitivity and specificity of the tool for different cut points
in order to determine the optimal cutoff point. Moreover
we calculated the incidence rate ratio during the period of
study, and the fall index incidence ratio for days of hospital
stay; to evaluate the risk factors that mainly affected the
falls we calculated the odds ratio for each risk factor
considered in the tool.
The data was analyzed using SAS V9.0 for Windows
(SAS Inc., Cary, NC, USA) and P < 0.05 was considered
statistically significant.
2.6. Ethical considerations
We asked the authorization to perform the study to the
Teaching Hospital General Manager. The study protocol
D. Ivziku et al. / International Journal of Nursing Studies 48 (2011) 468–474 471
was reviewed and approved by the University Hospital
Review Board. At admission nurses gave patients, or their
relatives if the patients were unable to decide for
themselves, a brief description of the aim of the study,
its risk and benefits, assuring that, independently of
participation consent or tool scores, the best nursing care
was going to be provided during their hospital stay. As the
main aim of the study was a tool validation and posed only
minimal risk for patients only oral consent was requested,
as approved by review board. Anonymity and confidentiality
was guaranteed, all the patients’ forms were retrieved
in the unit daily by principal researcher and stored in a
closed box. During the data analysis each patient was
identified with a progressive number and only the main
researcher could go back to the identification of the
patients.
3. Results
3.1. Inter-rater reliability
During the first 2 weeks of the study 24 elderly patients
were admitted and the inter-rater reliability was estimated
on them. The kappa index was 0.87 (95% CI 0.71–
1.00).
Disagreements mainly concerned the classification of
patients as depressed, the presence of dizziness, and the
use of benzodiazepines (used at home, but not during the
first 24 h of hospital stay).
3.2. Time of administration
The average time needed to collect the data to fill in the
tool was about 1 min, as estimated by each nurse: in fact,
some information could be retrieved easily from the
clinical record or asking the patients or relatives during the
nursing assessment at the admission. The time needed to
perform the get up and go test was variable, depending on
the clinical general conditions of the patients and
associated diseases. Sometimes was not performed specifically,
because the nurse observed the patients during the
examination and how she/he moves in the room.
3.3. Population
A total of 179 patients met the inclusion criteria and
gave consent to participate during the 8 months of the
study. Of the 179 patients, 74 were male and 105 female.
The mean age was 79.47 years (SD 9.5), with female older
than male (81.7 vs 79.8).
3.4. Validity
A total of 14 falls were reported on 179 patients, with a
cumulative incidence of 7.8%, and a fall index of 7.5 per
1000 days of hospital stay.
The mean score of the HFRM II was 5.78 (SD 3.52, range
0–15). Among the risk factors evaluated by the tool, vertigo
(49%), incontinence (48%), depression (46%) and confusion
(32%) were the most prevalent risk factors screened on the
elderly (Table 1), consistently with the results of other

472
Table 1
Frequency of risk factors on the studied population.
Items N %
Vertigo 87 49
Incontinence 86 48
Depression 82 46
Sex (male) 74 41
Confusion 58 32
Benzodiazepines 50 28
Get up and go (score 3) 36 20
Antiepileptics 13 0.7
Table 2
Psychometric properties of HFRM II.
Values CI 95%
Sensitivity 0.86 0.67–1.04
Specificity 0.43 0.34–0.51
PPV 0.11 0.051–0.17
NPV 0.97 0.94–1.01
studies that reported these factors more prevalent in the
hospitalized elderly population (Oliver et al., 2004).
Of 179 patients included in the study, 106 were
classified at risk (HFRM II 5) and 73 not at risk of falling.
Of 106 patients classified at risk 12 fell (11%), and of 73
classified not at risk 2 older people fell (2.7%).
87.5% patients fell within the first 10 days of
hospitalization, indicating that the elderly recovering from
acute illnesses are more frail and subject to fall-prone in
the first days of the hospitalization.
The mean age of the fallers was 81 (SD 8.5); the ratio of
males and females was equal in the fallers (7 females and 7
males) with a quite similar mean age (males 81.14 and
females 80.7).
The sensitivity of HFRM II was 86% and the specificity
43%. The positive predictive value was 11% identifying 12
patients that fell on 106 at risk patients. The negative
predictive value was 97.26% and allowed to identify 71/73
of the patients that did not fall. The 95% confidence
intervals showed a narrower range for the specificity
(0.34–0.51) and a broader range for sensitivity (0.67–1.04).
In Table 2 the psychometric properties of the Hendrich II
model are described in detail.
The analysis of ROC curve has been carried out to find the
best cutoff and best sensitivity and specificity values. The
best cutoff point was found for values equal or higher than 5,
comparable to that indicated in the development study
(Hendrich et al., 2003) and in Kim’s study (Kim et al., 2007).
The area under the curve (AUC) was 0.71 (95% CI 0.60–
0.85), showing a moderate discriminating power of the
tool (Fig. 1), similarly to the values found in Kim et al.’s
study (0.73, 95% CI 67–80).
3.5. Risk factors in hospitalized elderly
To evaluate the risk factors that affected the falls in our
population we calculated the odds ratio for each risk factor
(Table 3).
The only two risk factors that were significantly related
to falls in our study were confusion (OR: 4.26; 95% CI 1.35–
D. Ivziku et al. / International Journal of Nursing Studies 48 (2011) 468–474
13.36) and depression (OR: 3.22; 95% CI 0.97–10.71),
consistently with weight attributed to these risk factors in
the tool: in fact Hendrich recognized a score of 4 to the
confusion and of 2 to the depression.
4. Discussion
In the present study the predictive properties as well as
reliability and feasibility of the HFRM II were assessed in a
geriatric unit of a Teaching Hospital.
Our results showed that the time needed to complete
the HFRM II was about 1 min, as indicated by Hendrich
(2007). The nurses found it easy and quick to use because
no additional data and time were needed to assess the risk
at the patients admission: the tool could be compiled using
the data collected at the moment of admission in the
nursing assessment record and observing the patient
movements in the room, not increasing the nursing
workload, already heavy in geriatric units, and not adding
burden to the patient. In acute care hospitals these factors
are important if we want that fall risk assessment tools be
extensively used in clinical practice, as the time dedicated
to the patient assessment is affected by severity of patients
admitted to the unit, number of nurses in staff and
complexity of nursing administration duties, such as the
bulk of nursing records and other documentations to write
out (Dempsey, 2004).
Compared to Hendrich et al. (2003) and Kim studies
(2007), we found higher values of sensitivity (86% vs. 74.9%
and 70%) and lower values of specificity (43% vs. 73.9% and
61%) (Table 4).
Table 3
Relative risks of the risk factors.
Fig. 1. ROC curve.
Risk factors OR CI P value
Confusion 4.26 1.35–13.36 0.009
Depression 3.22 0.97–10.71 0.03
Incontinence 1.08 0.36–3.24 0.21
Vertigo 0.77 0.25–2.34 0.19
Sex (male) 1.46 0.49–4.36 0.17
Benzodiazepines 1.03 0.30–3.46 0.23
Antiepileptics 0.98 0.11–8.14 0.39
Get up and Go (score 3) 1.95 0.64–5.87 0.10

Table 4
Psychometric values of Hendrich II Model in different studies.
Authors Hendrich
et al. (2003)
Kim
et al. (2007)
Sensitivity 0.75 0.70 0.86
Specificity 0.74 0.61 0.43
PPV ne 0.02 0.11
NPV
ne, not evaluated.
ne 0.95 0.97
Our higher sensitivity could be related to the fact that
the risk factors included in the Hendrich fall risk model are
highly relevant for the elderly comparing to a general
hospitalized population admitted to medical–surgical
units or persons younger than 65 years old. In fact giving
higher scores to confusion and depression (4 and 2
respectively), can help to predict falls in geriatric patients
as these conditions are more frequent in elderly admitted
to hospital and are recognized as important risk factors for
hospitalized elderly patients (Rubenstein, 2006; Scott
et al., 2007).
This study was the first one to test this assessment tool
specifically on geriatric patients so far and we could not
know if this discriminative value depends on characteristics
of population admitted to our unit.
Compared to a relatively high specificity of the
Hendrich study, in our study the specificity was quite
low, with proportion of false-positive fallers (risk-score
patients who did not fall) of 57%. This was not completely
unexpected since lower specificity values have been found
frequently by researchers other than those who developed
the risk assessment tools because of settings and population
changes (Oliver et al., 2004).
According to Myers, on the accuracy calculations two
confounders can have impact: treatment paradox and
ward prevention measures. Treatment paradox occurs
when nurses are aware of the risk scores and implement
fall prevention measures for high risk patients and not for
low risk patients. For this reason it is important that nurses
remain blind to the results of the assessment when testing
the accuracy of a tool. Even if nurses are blind to the risk
assessment it is likely that some type of fall prevention
protocol is in place in the hospital setting. Fall might
therefore be prevented by normal ward practices. This
issue is difficult to counter as it would be unethical to ask
nursing staff not to implement fall prevention measures
(Myers, 2003).
This could be occurred also in our study as, even if in the
geriatric unit do not exist a formal protocol for preventing
falls, the nurses plan and implement fall preventive
interventions when they judge that a patient is at high
risk on the basis of their clinical nursing assessment at the
patient admission. For example, if the older patient is
confused or agitated they usually ask one of the relatives to
stay by the older patients during the day and/or the night;
the nurses check more and more times patients considered
at risk of falling during their shifts; moreover the relatives
employ family assistants to help their elderly during the
hospital stay whether they have some physical limitations.
These interventions can be effective and reduce the risk of
falling in some patients at risk.
D. Ivziku et al. / International Journal of Nursing Studies 48 (2011) 468–474 473
Ivziku et al.
(current study)
One more explanation could be that severely impaired
patient and patient’s family may become more aware of
the risk of falling in hospital settings and consequently
better protected those at risk applying some precautions.
In addition, the patient health conditions can improve
during the hospital stay and consequently the risk scores
can change, so the admission score, that we used in our
research to assess the risk, might not represent the risk of
the patients during his/her hospital stay.
The weak specificity of a fall risk assessment tool is of
some relevant concern when evaluating its clinical utility,
because many patients who do not fall are identified at
high risk. This has implications for the implementation of
fall prevention interventions that should be mainly
targeted at those at high risk. Fall prevention programs
may lose some of their significance if staff perceives that
too many patients are identified at high risk for falls.
We found an inter-rater reliability very similar to that
calculated in the Kim et al.‘s study (k = 0.81 vs 0.87). The
analysis of the reasons of rater disagreement indicated the
need to include in the training program more examples and
explanations about particular clinical situations (for example
how to score the patient with prescription of benzodiazepine
at occurrence, patients with cognitive
impairment, temporary or permanent vertigo at admission).
5. Study limitations
Even though the inter-rater reliability in our study was
quite high, the range of CI at 95% of 0.71–1.00 indicated
that it should have been necessary to test a higher sample
size in order to have a power at least of 0.80 with an alpha
of 0.05 as the literature suggests (Papaioannou et al., 2004).
Moreover this study presents a low number of patients
studied due to the small number of beds in our geriatric
unit and to the short period of study. Even if the total fall
rate of 7.5 per 1000 patient days is consistent with the
range of the rates reported in the literature, the number of
falls recorded (n = 14) is very limited and do not permit to
generalize our results. In addition, as the research was
carried out in one single geriatric unit, further validation
studies will be necessary to extend the use in other
geriatric units or on the general elderly population
admitted to hospital.
We did not reassess the patients when the clinical
conditions changed to not burden the nurses working in
the unit. We do not know if the fall risk score remained the
same or changed just prior to the fall. Elderly patients
should be reassessed during their hospital stay as their
health conditions can change rapidly, and new medications
can be prescribed (Hendrich et al., 2003).
We did not compare the HFRM with other fall risk
assessment tools to evaluate the different discriminative
power and the effectiveness of different tools. A comparison
with other tools could be useful to verify the power of
other risk factors and tools in our population.
6. Conclusions
The use of quick, reliable and valid fall risk tools to
identify high risk patients and to elicit further fall related

474
assessments and interventions is recognized important in
all clinical practice settings. The international research has
not identified one optimal fall risk assessment tool for the
acute care settings, and in particular for elderly inpatients.
We aimed with our research to contribute to the search
of a valid and reliable risk assessment tool by evaluating
the predictive validity of the HFRM II in a geriatric unit. We
found that this tool is easy to apply, the data can be
collected simply, the score is quick to calculate, and has
good sensitivity but low specificity.
Hence we can moderately recommend the routine use
of HFRM II to assess the fall risk in the geriatric unit of our
Teaching Hospital.
Acknowledgements
The authors wish to acknowledge the nursing staff of
the Geriatric Unit of the Campus Bio-Medico University
Hospital for the collaboration. The authors also extend
their acknowledgements to Ann Hendrich, PhD, author of
the Hendrich II Fall Risk Model ß , for her invaluable advice
and support.
Conflict of interest: None declared.
Funding: We do not have any sources of funding for our
research.
Ethical approval: This study was approved by the ethical
Committee of the Campus Bio-Medico University Hospital.
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