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

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