discriminating high fall risk on an inpatient rehabilitation unit

234 Rehabilitation Nursing

Patients on an inpatient rehabilitation unit (IRU)

typically have multiple fall-risk factors compared

to general hospital patients or persons at risk in the

community. The presence of multiple risks makes it

difficult to discriminate those likely to fall. Many of

the measures used to assess fall risk and predict falls

have been validated for the community, skilled nurs-

ing and extended care facilities, or general hospital

settings. Generalization of instruments and scores

across settings is often limited (Oliver, Daly, Mar-

tin, & McMurdo, 2004; Perell et al., 2001). The aim

of this study was to identify a set of variables that

could discriminate those who fell on an IRU from

those who did not, despite the presence of multiple

risk factors.

Unintentional falls remain the leading cause of

nonfatal injuries in the United States (Centers for

Disease Control and Prevention [CDC], 2006). Sev-

eral recent studies provided large N overall hospital

fall rates of 2.45 to 3.73 falls per 1000 patient days

(Dunton, Gajewski, Tauton, & Moore, 2004; Halfon,

Eggli, Van Melle, & Vagnair, 2001; Hitcho et al., 2004).

Fall rates for inpatient rehabilitation have not been as

systematically estimated. Rates vary from 2.92 to 15.9

falls per 1000 patient days and reflect selected diagno-

ses, older studies with long rehabilitation lengths of

stay, data from care in multiple countries, and other

factors (Aisen, Iverson, Schwalbe, Weaver, & Aisen,

1994; Morse, 1996; Nyberg & Gustafson, 1996; Rogers,

1994; Suzuki et al., 2005; Sze, Wong, Leung, & Woo,

2001). Many studies of inpatient rehabilitation unfor-

tunately report rates using different measures, which

make comparisons difficult. It is clear that rates of

falls in inpatient rehabilitation are higher than those

of the general hospital.

Studies with IRU samples have identified multiple

medical, functional, and cognitive factors associated

with higher fall risk (e.g., Juneja, Czyrny, & Linn,

1998; Rapport, Hanks, Millis, & Deshpande, 1998;

Teasell, McRae, Foley, & Bhardwaj, 2002). Attempts

to devise a risk profile or IRU-specific measure have

resulted with complex multiple-factor indices (Ny-

berg & Gustafson, 1997; Vassallo, Sharma, Briggs, &

Allen, 2003). Olsson, Lofgren, Gustafson, and Nyberg

(2005) recently described the difficulty in trying to

replicate the validity of one such complex index in

IRU patients. An alternative to complex measurement

would be to discriminate falls in a more controlled

study.

Although studies of falls in IRU patients have

been both retrospective and prospective, random-

ized assignment to a “fall condition” is not possible.

No study, though, has applied a matched case-control

design to control for common factors known to affect

fall risk. Several patient characteristics from the lit-

erature that consistently affect fall risk in the commu-

nity, inpatient settings, and IRUs have been a person’s

diagnosis, age, and, less consistently, gender. Right-

brain cerebrovascular accident (CVA), older age, and

being female have been associated with greater fall

risk than comparison groups (e.g., Bueno-Cavanil-

las, Padilla-Ruiz, Jimenez-Moleon, Peinado-Alonso,

& Galvez-Vargas, 2000; Grant & Hamilton, 1987; Hal-

fon et al., 2001; Perell et al., 2001). In this study, IRU

patients who fell were matched with controls of the

same diagnosis, age, and gender.

Poor discrimination with the assessment compo-

nent of the hospital-wide plan was the clinical impe-

tus for the study. The hospital plan used an evidence-

based Morse Scale cutoff score of 45 to determine high

risk (Morse, 1996, 2006). Our experience had been that

75%–90% of the IRU patients were then designated as

“high risk.” Implementing the intensive hospital fall

prevention plan (designed for a much smaller propor-

tion of patients per unit) was not feasible. Our aim

was to develop an assessment tool for discriminating

The objective of this study was to identify on admission the most discriminating fall predictors for patients to an inpatient

rehabilitation unit. Medical information from 34 patients who fell over a consecutive 7-month period and 102 controls

(1:3 ratio) matched for diagnosis, age, and gender was analyzed to identify a set of best predictors. Admission mobility

and problem solving FIM™ scores accounted for 17% of variance in whether a fall occurred during the admission. After

statistically deriving optimal cutoff thresholds for decision making, high fall risk was retroactively assigned to patients.

Logistic regression revealed increased odds of having fallen by 5.1 times for poorer mobility and 2.4 times for poorer problem

solving. The practical benefits of the evidence-based risk assessment were discussed.

Discriminating High Fall Risk on an Inpatient Rehabilitation Unit

Rehabilitation NURSING

KEY WORDS

Rehabilitation Nursing

those at greatest fall risk, without discounting the

relative fall risk in all rehabilitation patients.

The first hypothesis was that by controlling for di-

agnosis, age, and gender, one or two functional char-

acteristics would be sufficient to discriminate those

who fell from matched controls. A second hypothesis

was that cutoff scores for the significant predictors

identified in the test of the first hypothesis would cre-

ate a feasible clinical tool for determining fall risk.

MethodParticipants

Thirty-four patients who fell on an acute inpa-

tient hospital rehabilitation unit over a continuous

7-month period were the target sample in this study

(fall group). A fall was operationally defined as “un-

intentionally coming to rest on the ground, floor, or

other lower level” (Buchner et al., 1993). If the patient

lost balance and was lowered to the floor by a helper

or was found on the floor, both the attended or unat-

tended situation was considered a fall. A comparison

group of 102 rehabilitation patients during the same

period were matched with the fall group on diagno-

sis (including subtype such as right- vs. left-brain

stroke), age (as close as possible), and gender, in that

order (matched comparison group). The comparison

group consisted of three patients matched to each fall

patient to enhance statistical power. Data from retro-

spective chart reviews and scores from the 1999 ver-

sion of the FIM™ (Uniform Data System for Medical

Rehabilitation, 1999) instrument were used. Length

of stay, time since onset of diagnosis, and the 18 ad-

mission-FIM™ item-scores scaled from 1 (total as-

sistance) to 7 (complete independence) were used as

potential predictors. The study received medical cen-

ter institutional review board approval prior to any

data collection from medical records. Analyses were

performed on a de-identified research data set.

Procedure. All patients received usual care, and

no interventions were performed beyond the hospi-

tal-wide fall prevention plan. The data for this study

were collected retrospectively with the existing fall

prevention plan being implemented.

Analyses. Using SPSS for Windows (Release

12.0), potential predictors were entered as indepen-

dent variables in a stepwise discriminant analysis

to predict membership in the two groups (fall vs.

comparison). This method is similar to a multiple

regression, but the aim of the analysis is accurate

classification of a categorical dependent measure. A

canonical correlation expresses the degree of associa-

tion between the sets of independent and dependent

variables in a discriminant analysis. The canonical

correlation is parallel to a multiple R in this study,

but discriminant analysis uses the alternative term

because it can correlate a set of independent variables

with a set of dependent variables. Anticipating one to

three variables that would discriminate the groups,

signal detection, or decision-making analyses (Ward,

Marx, & Barry, 2000; Zarin, 2000) would identify op-

timal cutoff scores using sensitivity (ability to detect

the fall group) and specificity (ability to detect the

comparison group). This approach involved explor-

ing the range of correct hit rates for sensitivity and

correct rejection rates for specificity, and deciding on

the optimal set of values for the two. Although high

scores on both sensitivity and specificity would be

ideal, a sensitive measure was more important with

the practical objective of identifying persons at risk

for falls. Logistic regression identified the odds ratios

associated with the retrospective prediction of fall

risk using the variables with cutoff scores. Although

discriminant and logistic regression methods overlap

in their aim, each approach had features relevant to

this study (Press & Wilson, 1978).

ResultsTable 1 lists the participant characteristics on the

matching variables. As the three statistical tests indi-

cated, there were no group differences, even though

a few individuals could not be matched exactly on all

three variables. The table also summarizes group dif-

ferences on several global outcome variables not used

as predictors. Those individuals who fell had lower

total admission and discharge FIM™ total scores,

suggesting these individuals had greater functional

impairment from the onset of rehabilitation. There

were nonsignificant trends for a shorter length of stay

and lower length of stay (LOS) efficiency for those

who fell. The LOS efficiency measure is calculated by

the total FIM™ score change divided by the length of

stay. A patient who is more efficient in rehabilitation

progress would make greater average FIM™-score

gains per day and would regain the functional inde-

pendence necessary for discharge rapidly. Thus, it is

one of the major indicators of a positive rehabilitation

outcome (e.g., Ottenbacher et al., 2004).

The resulting canonical correlation for the step-

wise discriminant analysis of the two best predic-

tive variables, mobility and problem-solving FIM™

scores, was .326 (χ 2 = 14.927, p = .001). Correct clas-

sification was 66.2%, which was significantly greater

than 50% for chance. The mean discriminant-function

scores and confidence intervals were –0.59 (95% CI =

–0.85, –0.33) and 0.20 (95% CI = –0.01, 0.41) for the fall

and comparison groups, respectively. The fall group

had significantly lower scores than the comparison

group. The confidence intervals of the means did not

overlap, with zero indicative of significant discrimi-

nation between groups. These analyses employed

predictors as continuous variables on 7-point scales,

but better practical utility could be achieved with the



cutoff scores for higher versus lower fall risk.

Signal detection analyses of the admission mo-

bility and problem-solving FIM™ scores provided

sensitivity and specificity scores relative to chance

discrimination at .50. As an additional reference

point, the discriminant function of the 7-point FIM™

scores yielded a sensitivity of .68 and specificity of

.66. The use of a cutoff of admission mobility FIM™

score as maximal or total assistance (a score of 1 or

2 versus higher scores) improved sensitivity to .88,

although specificity dropped to chance at .42. The op-

timal cutoff for the admission problem solving FIM™

score of moderate or more direction (a score of 3 or

less versus higher scores) was .62 for both sensitivity

and specificity. The use of both these cutoff scores for

admission FIM™ scores should enhance prediction

of fall risk over not using this information.

Figure 1 breaks down the sample groups by pre-

dicted fall risk from low with FIM™ scores above

cutoff levels (more independent) on both scores to

the highest combined risk with FIM™ scores below

the cutoffs (more dependent or impaired) on both

scores. A chance relationship would be 25% in each

cell. The very low number of the patients who fell in

the low-risk group (5.9%) and the high number in

the highest predicted risk group (55.9%) accounted

for the strength in the relationship. Both of these cell

frequencies were two or more standard residual units

from expectation and statistically significant.

Logistic regression with the two cutoff-score ver-

sions of mobility and problem solving coded as one

for at or below cutoff (associated with greater risk) or

zero for above cutoff (reference) tested the strength

of relationships to predict fall group membership.

Both predictors were statistically significant (p < .05).

Analysis with the variables as FIM™ scores or as di-

chotomous cutoff scores did not differ in strength

of relationship, suggesting the use of cutoff scores,

which would be more useful in clinical decision mak-

ing, did not sacrifice any loss of predictive power.

The Negelkerke estimate of R2 of .167 for predictors

as cutoff scores was better than the squared canoni-

cal correlation coefficient of .108 for the two predic-

tors as full FIM™ scale scores. The odds ratios and

95% confidence intervals for the two independent

variables as cutoff scores indicated that persons who

were maximal to total assistance on admission had

5.140 times the odds for having a fall than those in

the comparison group who did not fall (CI = 1.667,

15.848). Persons requiring moderate or more direc-

tion for problem solving on admission had 2.400

times the odds for all fall (CI = 1.049, 5.492). The odds

ratios are multiplicative, indicating 12.326 times the

odds if both factors were present. If the interaction is

Table 1. Characteristics of the Fall and Matched Comparison Groups

Group Fall Matched Comparison Group Difference

N 34 102

Characteristics that were matched between groups

Diagnostic group

CVA 41% 41%

BI-Neuro 32% 32%

SCI 15% 15%

Ortho 6% 4%

Med-Other 6% 8% X2 (4) < 1 (NS)

Age

Age [M (SD)] 63.6 (22.3) 64.6 (19.8) t (134) <1 (NS)

Gender

Men 56% 53%

Women 44% 47% X2 (1) < 1 (NS)

Means (SD) for nonmatched general outcome indicators

Length of stay (days) 19.35 (8.52) 15.98 (9.46) t (134) = 1.84 (p = .067)

Admission FIM 50.53 (16.85) 61.85 (19.80) t (134) = 2.99 (p = .003)

Discharge FIM 79.12 (21.50) 88.84 (21.47) t (134) = 2.29 (p = .024)

LOS Efficiency 1.64 (0.80) 2.09 (1.34) t (134) = 1.83 (p = .070)

Note. SD = standard deviation; LOS = length of stay


included in the logistic regression, with being above

on both cutoff scores (low risk) as reference and low

mobility only, low problem solving only or low on

both (highest risk) as independent variables, only the

interaction was significant.

DiscussionMeasuring risk in its full complexity may be coun-

terproductive when trying to discriminate or predict

those who fell or might fall from those who do not

on an IRU. Nyberg and Gustafson (1996) tested an

index based on medical and functional characteris-

tics summed to the presence of up to 11 risk factors to

predict fall risk in stroke rehabilitation patients with

a high sensitivity of .91, but low specificity of .27. Ols-

son et al. (2005) failed to replicate these findings, and

ended up with a modified scale of only three items:

impaired balance, visual hemi-inattention, and male

gender. The cumulative effect of revised risk scale

was replicated in two samples and yielded hazard

ratios (and 95% CI) of 1.8 (1.4 – 2.4) in the model fit

sample and 1.9 (1.4 – 2.7) in the validation sample.

The current study yielded similar findings to Olsson

et al. (2005) with (1) only two necessary predictors

(independent of gender, which was controlled for),

mobility and problem solving, (2) the use of widely

used FIM™ scores as predictors, (3) greater discrimi-

nation of risk (reported odds ratios, with combined

odds for fall of 12.3 with both factors present), (4)

more modest sensitivity (.88 and .62 for the mobility

and cognitive respectively), and (5) better, but still

poor specificity (.42 and .62, respectively). Thus, few-

er factors can increase correct identification of those

at high fall risk over more complex models.

Most IRU patients have many risk factors, but the

between-person variation in combinations makes

use of the complexity infeasible. As an informal

comparison of the multiple risks for the persons in

this study, participants were considered “impaired”

or not for each of the 15 ranked factors from the

Perell at al. (2001) review. Impairment was defined

as demonstrating impairment on specific tests such

as manual muscle testing or a score of 4 (minimal as-

sist) or lower when measured by FIM™ item. Using a

sum of the presence or absence of impairment on any

factor from the Perell et al. list, the two groups did

not differ (Fall: M = 12.00, SD = 1.18; Comparison: M

= 11.68, SD = 2.31; t < 1.0, NS). Over both groups, 94%

had 10 or more of the 15 risk factors.

Although the results from this study did not

provide a definitive prediction of patients admit-

ted to an IRU who are likely to fall during their stay,

patients with impaired mobility and problem solv-

ing beyond the defined cutoff levels was associated

retrospectively with greater risk. The study aim was

the utility of common, predominant factors. We

confirmed the discriminating power of mobility

(whether walking or via wheelchair) and cognition

Figure 1. Percentages of the Fall or Matched Comparison Group by Fall-Risk Group

Note. Percentages of the fall or matched comparison group by fall-risk group based on the FIM™ cutoff scores for admission problem solving (moderate or more direction) and mobility (maximal or total assistance). Lower scores (greater dependence or functional impairment) were associated with greater likelihood having fallen.


over other predictors. These results were consistent

with findings in major literature reviews and spe-

cific studies with IRU samples. The two factors are

present and usually at or near the top of fall-risk

factor lists (Bueno-Cavanillas et al., 2000; Halfon et

al., 2001; Oliver et al., 2004; Perell et al., 2001). Im-

paired mobility and cognition are also components

of more complex fall-risk indices (Morse, 1996, 2006;

Olsson et al., 2005).

Alexander (1996) discussed multiple ways in

which cognition and gait might interact, which might

explain the greater importance of the mobility and

problem solving variables over others. In the pres-

ence of cognitive impairment, gait speed is slower,

gait speed does not increase as much by time of re-

habilitation discharge, and there is increased step-to-

step variability. From the other point of view, for a

person with poorer mobility, there is slower cognitive

reaction to environmental change and poorer cor-

rect reaction to circumstances. White matter ischemic

changes (leukoaraiosis) have been associated with

both poorer gait and cognition even when other risk

factors have been controlled (Podgorska, Hier, Py-

tlewski, & Czlonkowska, 2002). Some neurological

basis of the gait-cognition interaction is likely.

Other studies have focused on specific compo-

nents of cognition that have been associated with

fall risk, most particularly attention, environ-

mental perception, and reaction time, and execu-

tive functions (Mayo, Korner-Bitensky, & Kaizer,

1990; Rapport et al., 1993, 1998). The nature of the

relationship among specific cognitive impairments,

mobility, and falls needs further study. The mobility–

cognition interaction is also consistent with descrip-

tive or epidemiological studies of the circumstances

of hospital falls (Hitcho et al., 2004, Kerzman, Chet-

rit, Brin, & Toren, 2004; Mion et al., 1989; Rogers,

1994). Fall situations typically involve a patient get-

ting up from bed, climbing over bedrails, or need

to use the toilet. Even when attended, external ob-

stacles or sudden situations requiring quick reaction

can lead to a fall.

The current study had its limitations. The sample

was small, and data were from a short time interval.

We did find the sample to be representative of the

IRU population during that period, but replication

with a larger or multisite sample would be beneficial.

The study was also retrospective. Mobility and cogni-

tion FIM™ scores technically could only discriminate

those who fell from those who did not and do not

predict in the prospective sense. The set of indepen-

dent variables was also small and perhaps unfair to

other potentially predictive factors discarded after

the preliminary analyses. The choice in the current

study was to (1) build on preliminary results from a

pilot study that evaluated the relative utility of more

than 200 measures, (2) force a result that would avoid

complexity and yield significant factors, (3) examine

potential factors that have demonstrated past fall pre-

diction, and (4) use only common measures of wide-

spread use in IRUs. That the predictors, even though

few, that were significant were also frequently high

on other risk factor lists does support the validity of

the results. Finally, even though the results are eas-

ily translated into practice, good evidence requires

replication in other samples.

Subsequent to the research study, we implemented

a change in the assessment component of the fall pre-

vention plan for the IRU at our facility (Table 2). If

both mobility and cognition were impaired on the

cutoff scores as well as a score greater than 45 on the

Morse Scale, an individual was deemed to be high

risk. Patients impaired on both cutoff scores were

always above the hospital-wide high score on the

Morse Scale. It was more feasible to adapt the ex-

isting hospital fall prevention plan than to create a

separate one for the IRU. The approved revision of

the fall prevention policy and procedure for the IRU

documented the adapted assessment of high fall risk

and rationale. The intensive fall prevention interven-

tion included nondescript orange indicators outside

a patient’s door, an orange identification wristband,

and orange signs with fall prevention reminders for

the patient in the person’s own language and for

staff. Other patient-specific recommendations other

than chart or Kardex alerts could also be used with

the orange theme. Family members were also edu-

cated to the fall plan. The orange served as a cue to

all staff for prompt response to any patient needs,

preventive toileting, and not leaving such persons

out of visible monitoring. Once on high-risk precau-

tions, only a team decision documented as part of

the weekly team conference would release the person

from high-risk status. With a month of staff awareness

training, the unit fall rate had decreased. Compared


Table 2. Revised Assessment for Designation of High Fall Risk on the Inpatient Rehabilitation Unit

High Risk for Rehab Falls

1. Admission locomotion FIM™ < 2 � Yes � No

2. Admission problem solving FIM™ < 3 � Yes � No

3. Morse Fall Scale score > 45 � Yes � No

If all three indicators are Yes, then implement high-risk fall prevention plan.

Note. The table represents the fall-risk assessment portion of the 12-hour nursing shift assessment on the Inpatient Rehabilitation Flow Sheet. The items of the Morse Fall Scale (Morse, 1996, 2006) are also rated on the flow sheet. For patients who were not determined to be at high fall risk by these criteria, nursing and other staff could still employ reasonable fall safety precautions but without using the orange-colored, high-risk alerts and other materials.


to a 3-year fall rate of 6.6 per 1,000 patient days prior

to using the new assessment tool, the fall rate for the

subsequent 12 months was 5.7. This was not statisti-

cally significant because of the large variance in falls

across the months prior, but the decreased rate with

less variability after use of the new decision tool was

a desirable outcome.

Due to a general high risk for patients in an IRU

relative to other populations, we retained “universal

fall precautions” for all rehabilitation patients. These

general precautions might include verbal or written

reminders to ask for assistance, educating family not

to assist patients in mobility unless trained, regular

checks on the patient for personal needs such as toi-

leting, and access to a call light or other notification

system. The orange theme was reserved only for those

with high risk. To attempt an intensive fall prevention

plan on virtually everyone had been prohibitive. Yet

the challenge in a rehabilitation environment is to find

a realistic alternative. On the one hand, therapy is essen-

tial for functional independence by pushing patients’

limits to strengthen muscles, increase functional abil-

ity, and improve awareness and cognition. Carryover

of techniques outside of therapy is essential for good

outcomes. On the other hand, patients sometimes get

into or put themselves into situations that challenge

them beyond their means without staff support. Falls

are one risk associated with this essential limit pushing.

The balance point of personal safety and maximal func-

tional outcome is not easy to achieve for patients with

new disabilities. The use of the new discriminative tool

for determining the highest risk did reduce the high

risk solely on the Morse Scale from 87% to 33% using

the additional criteria. With the safety of all patients in

mind, the intensive fall prevention plan became more

feasible for those assessed as highest risk.

About the Authors

Michael J. Gilewski, PhD, is assistant professor of physical medicine at Loma Linda University. Address correspondence to him at 11406 Loma Linda Drive, Loma Linda, CA 92354–3711 or [email protected].

Pamela Roberts, MSHA OTR/L, is a manager of rehabilita-tion, psychology and neurology at the Department of Reha-bilitation, Cedars-Sinai Medical Center, Los Angeles, CA.

Jodi Hirata, MPT, is a physical therapist at the Department of Rehabilitation, Cedars-Sinai Medical Center, Los Angeles, CA.

Richard Riggs, MD, is a medical director of the Department of Rehabilitation and chairman of the Department of Physical Medicine at Cedars-Sinai Medical Center, Los Angeles, CA.

AUTHOR NOTES: A preliminary version of this

paper was on the program of the annual meeting

of the American Academy of Physical Medicine and

Rehabilitation, New Orleans, September 2001. The

research study was approved by the Cedars-Sinai

Medical Center Institutional Review Board, study

number 3617.

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The Rehabilitation Nursing Foundation (RNF) offers a variety of research grants to apply for including:

Sigma Theta Tau International and RNF co-sponsor a grant that provides one recipient $4,500 in support of research related to rehabilitation nursing.

RNF offers $30,000 in the form of multiple grants for research that addresses the clinical practice, educational, or administrative dimensions of rehabilitation nursing. The New Investigator Award grants up to $10,000 for nurses who are novice researchers. Up to two Research Fellow Grants will be awarded from the remaining funds.

Research proposals must be postmarked by February 1, 2008. Funding begins January 2009 for the selected proposals. Visit www.rehabnurse.org/research/researchgrants.org for more information.

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