Identification of patients at risk for falls and
fall-related injuries is important not only for hospital inpatients but also
for patients in multiple other settings, including long-term care and
community-based settings. For inpatients we look at a list of risk factors for
falls to identify which patients should have fall precautions instituted. But
in our August 4, 2009 Patient Safety Tip of the Week Faulty
Fall Risk Assessments? we cautioned that simply labeling a patient
as low-, moderate-, or high-risk for falls often fails to match them to
specific interventions needed to prevent falls for that individual patient.
Many of the risk factors for falls are not modifiable.
Therefore, a focus on potentially modifiable risk factors is needed. Wouldnt
it be great if we had a tool that easily identified such patients at risk and
identified some specifically modifiable risk factors? Well, researchers in
Boston have come up with such a system that stratifies risk for hospital
admissions for fall-related injury based upon data readily available from the
electronic medical record (Castro
2014). Moreover, since the tool weighs heavily the adverse effect burden of
medications, it points to modification of a patients medication regimen as a
potential intervention to reduce the risk of fall-related injuries.
Castro and colleagues looked at patients aged 40 and older
who were admitted to 2 academic hospitals (that also serve as community
hospitals) for reasons other than fall-related injuries. They collected
variables readily available in the EMR at discharge, including the reconciled
medication list, and looked for subsequent emergency department visits or
hospital admissions over the next two years. After derivation of the risk
stratification tool at one hospital they validated it at the second hospital.
The unique feature of their tool is their way of estimating the burden of
medications on the fall risk. Not only
is the number of medications important but they also used the frequencies of
adverse effects (from the literature) of medications, taking into account that
drugs may have more than one risk factor for falls (eg.
sedation, dizziness, gait instability, etc.). They make the tool available
online at http://clearer.mghcedd.org/.
The authors suggest that using the tool to identify the
highest risk group could lead to fall prevention interventions being applied in
the most resource-effective manner. And, since the medication adverse effect
burden is one of the most modifiable factors, re-examination and modification
of a patients medications is a logical intervention.
We like the concept here and expect that further evaluation
of the tool in multiple populations and settings will lead to more widespread
adoption of the tool.
There has also been a renewed interest in identification of
fall risk for patients presenting to the emergency department. Over a decade
ago the PROFET study (Close
1999) showed that for community-dwelling patients aged 65 years and older
who presented to an emergency department with a fall a detailed medical and
occupational-therapy assessment with referral to relevant services resulted in
a marked reduction of falls and recurrent falls. A new systematic review looked
at risk stratification tools for geriatric patients presenting to the ED with
falls (Carpenter
2014). The authors noted a paucity of validated screening tools for
identifying fall risk in ED patients. An Australian study on patients 70 years
and older presenting to the ED with a fall or a history of 2+ falls in the past
year showed that a simple 2-question screening tool predicted subsequent falls as well as that
used in the PROFET study (Tiedemann 2013). The 2
screening items were: (1) 2+ falls in the past year and (2) taking 6+
medications. On the other hand, Carpenter et al. (Carpenter
2009) looked at fall risk in elderly patients presenting to the ED with
conditions other than falls. They identified 4 risk factors independently
associated with future falls: non-healing foot ulcers, self-reported
depression, falls in the preceding year, and inability to cut ones own
toenails (a measure of self-sufficiency or functional ability). The risk of falling
was directly related to the number of these risk factors present. But in the
meta-analysis by Carpenter et al. (Carpenter
2014) neither tool accurately identified increased fall risk, though the
Carpenter tool accurately identified those geriatric patients at low risk for
falls.
It would be most interesting to see how well the tool
developed by Castro above might perform on ED patients. The same variables
present on inpatient discharges should be available for ED patients and if good
medication reconciliation is done of the ED patients
one might expect the Castro tool to work well.
While in theory we should be most successful by implementing
risk reduction strategies in those patients we identify as being at highest
risk for falls, sometimes risk reduction strategies applied across the board
may also be successful. A recent study from New Zealand has demonstrated that
relatively low-cost home modifications and repairs can lead to a substantial
reduction in the rate of injuries related to falls (Keall
2014). They randomly assigned households for home modifications to
be done immediately or delayed for 3 years. They found a 26% reduction in the
rate of injuries caused by falls at home per year in the group receiving the
home modifications compared to those waiting for them. Injuries specific to the
home-modification program were cut 39% per year exposed. The modifications were
all considered to be relatively low cost and consisted of handrails for outside
steps and internal stairs, bathroom grab rails, outside lighting, edging for
outside steps and slip-resistant surfacing for outside areas such as decks and
porches.
Particularly as we embark more on population-based
management, fall reduction strategies need to be considered not just during
inpatient hospitalizations but each time the patient interacts with the
healthcare system.
Some of our prior
columns related to falls:
References:
Castro VM, McCoy TH, Cagan A, et
al. Stratification of risk for hospital admissions for injury related to fall:
cohort study. BMJ 2014; 349: g5863 (Published 24 October 2014)
http://www.bmj.com/content/349/bmj.g5863
Clearer: Estimate adverse effect burden of a list of
medications
Close J, Ellis M, Hooper R, et al. Prevention of falls in
the elderly trial (PROFET): a randomised controlled
trial. The Lancet 1999; 353: 93-97
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736%2898%2906119-4/abstract
Carpenter CR, Avidan MS, Wildes T, et al. Predicting Geriatric Falls Following an
Episode of Emergency Department Care: A Systematic Review. Acad
Emerg Med 2014; 21(10): 10691082
http://onlinelibrary.wiley.com/doi/10.1111/acem.12488/abstract
Carpenter CR, Scheatzle MD, DAntonio JA, et al. Identification of Fall Risk Factors in
Older Adult Emergency Department Patients. Acad Emerg Med 2009; 16: 211219
http://onlinelibrary.wiley.com/doi/10.1111/j.1553-2712.2009.00351.x/pdf
Tiedemann A, Sherrington C, Orr T, et al. Identifying older
people at high risk of future falls: development and validation of a screening
tool for use in emergency departments.
Emerg Med J 2013; 30: 918-92
http://emj.bmj.com/content/30/11/918.abstract
Keall MD, Pierse N, Howden-Chapman P, et al. Home modifications to reduce
injuries from falls in the Home Injury Prevention Intervention (HIPI) study: a
cluster-randomised controlled trial. The Lancet 2014;
Early Online Publication 23 September 2014
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736%2814%2961006-0/abstract
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