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Keywords:

  • Accidental falls;
  • inpatient rehabilitation;
  • risk ratio

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. Implications
  8. Limitations
  9. References

Purpose

The purpose of this study was to determine if the Functional Independence Measure (FIM) is as useful as the Morse Fall Scale in determining which patients admitted to an inpatient rehabilitation facility (IRF) are at highest risk for fall.

Method

Review of the charts of all patients admitted to an IRF in calendar year 2010.

Findings

Low scores on the FIM are as useful as high scores on the MFS in suggesting that a patient is at high risk for fall.

Conclusions and Clinical Relevance

Nursing staffs that use the FIM to comply with Centers for Medicare and Medicaid Services (CMS) documentation requirements likely do not benefit by also evaluating and documenting the patients’ score on the MFS.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. Implications
  8. Limitations
  9. References

Falls are the most common cause of accidental injury in the hospital and the most common reason that nurses need to file incident reports (Perell et al., 2001; Fonda, Cook, Sandler & Bailey, 2006). Thirty percent of falls result in patient injury. Four to six percent result in serious injuries, the most common being lacerations, fractures and head injuries. Two to three percent of falls may result in hip fracture (Corsinovi et al., 2009; Oliver, Healy & Haines, 2010; Hitcho et al., 2004). The incidence of falls varies depending upon the age of the patients and the type of problem for which they are admitted to the hospital (Cina-Tschumi, Schubert, Kressing, De Geest & Schwendimann, 2009). Rehabilitation units have high rates of falls. Lee and Stokic (2008) reported that 9.5% of 1472 consecutive admissions to an inpatient rehabilitation facility (IRF) fell during their hospitalization. Studies indicate that 20%–40% of patients admitted to an IRF after a stroke fall at least once during their stay on the unit (Teasell, McRae, Foley & Bhurdig, 2002; McLean, 2004; Dromerick & Reding, 1994; Nyberg & Gustafson, 1995; Saverino, Benevolo, Ottonello, Zsirai & Sessarego, 2006).

According to the American Geriatrics Society, British Geriatrics Society Panel on Falls Prevention (2011) and the Agency for Healthcare Policy and Research (2012), the process of preventing falls begins with an assessment of each patient's risk of falling. The Morse Fall Scale (MFS) is a widely used tool to assess the risk of fall. It was developed by comparing the records of 100 patients who fell and 100 patients who did not fall and was confirmed by a prospective study of 2689 admissions to acute medical, surgical, long-term geriatric, and rehabilitation units (Morse, 1986; Morse, Black, Oberle & Donahue, 1989). The scale has six variables: history of falls (25 points), poor safety awareness (15 points), presence of at least one comorbid condition(15 points), difficulty with transfers or gait (15 or 30 points), need for ambulatory aides (15 or 30 points), and presence of an intravenous line or heparin lock (20 points). The scale is scored from 0 to 120 with higher numbers representing an increased risk of fall. It is recommended as a screening tool by the National Center for Patient Safety (2004).

The Functional Independence Measure (FIM) was developed by a task force of the American Academy of Physical Medicine and Rehabilitation (Hamilton, Granger, Sherwin, Zielezny & Tashman, 1987). It documents the amount of help that a patient needs to perform 18 tasks. These are as follows: eating, grooming, bathing, dressing upper body, dressing lower body, toileting, continence of bowel and of bladder, transfers from bed or wheelchair, transfer to commode, transfer to tub or shower, ambulation or use of wheelchair, ability to climb stairs, comprehension of language, expressive speech, social interaction, problem solving, and memory (Linacare, Heinemann, Wright, Granger & Hamilton, 1994). Each item is scored from 1 to 7 with seven meaning that the person can perform the task without assistive device and without help and one meaning that the task needs to be performed by a helper without any assistance from the patient. The minimum score is 18 and the maximum score is 126. The FIM is a reliable and valid measure of patient function (Stineman et al., 1996; Cournan, 2011). Glenny and Stolee (2009) reviewed 40 papers that assessed the reliability and validity of the FIM. They found that the test has high inter-rater reliability and that it is a valid measure of patient function in that scores on the FIM correlate well with scores on other measures of patient function such as the Barthel Index and the Functional Autonomy Measurement System. It can be used to help predict length of stay, outcome, cost of care and caregiver burden (Stineman et al., 1996; Heinemann, Linacare, Wright, Hamilton & Granger, 1994; Stineman & Williams, 1990; Forrest, Schwam & Cohen, 2002). The Centers for Medicare and Medicaid Services (CMS) require an IRF to measure each patient's function at the time of admission and at the time of discharge from the unit (Centers for Medicare & Medicaid Services, 2001). There have been studies suggesting that admission score on the FIM is inversely correlated with the likelihood that patients will fall during their stay on the IRF (Saverino et al., 2006; Zdobysz, Boradia, Ennis & Miller, 2005;Aizen, Shugaev & Lenger, 2007; Suzuki et al., 2005; Petitpierre, Trombetti, Carroll, Michel & Herrman, 2010; Gilewski, Roberts, Hirata & Riggs, 2007; Forrest et al., 2012; Kwan, Kaplan, Hudson-Mckinney, Redmen-Bently & Rosario, 2012).

The purpose of this study was to compare the usefulness of the FIM and the MFS in assessing the risk that a patient will fall.

This study was approved by the Institution Review Board of the Medical Center.

Method

  1. Top of page
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. Implications
  8. Limitations
  9. References

All patients admitted to the IRF of the Medical Center are evaluated using the MFS and the FIM on the day of admission and each day of their stay on the unit. The authors reviewed the records of every patient discharged from the IRF to home during calendar year 2010. There were 385 patients in the study. The authors evaluated the relationship between age, sex, score at admission on MFS and FIM and whether or not the patient fell during the stay on the unit. There were not enough patients who fell to allow for evaluation of the relationship between the 17 impairment groups that CMS utilizes to identify the diagnosis responsible for hospitalization and fall. Two-sample T-test and confidence interval were used to determine the relationship between age, sex, and fall. Two-sample T-test and multivariate logistic regression analysis were used to test the relationship between admission scores on MFS and FIM and falls. Receiver operating curves (ROC) identified the FIM score with the best balance of sensitivity and specificity to identify patients at risk for fall. The relationship between falls and length of stay was evaluated by Mann–Whitney Test.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. Implications
  8. Limitations
  9. References

There were 385 patients in the study. Of these, 37 fell and 348 did not fall. All of the falls could be classified as mechanical falls in that they did not result from an acute change in medical status such as syncope, or hypotension. Twenty seven fell once, seven fell twice, and three fell more than twice. The fall rate per 1,000 patient days was 12.46. There was one fall that resulted in a laceration that required suturing. There were no fractures, injuries that required change in weight bearing status or brain injury. Neither age nor sex was related to the incidence of falls (Table 1). The average score on FIM at the time of admission for the entire group was 73.7. The average score for the patients who fell was 63.4 and it was 74.8 for the patients who did not fall. This was a significant difference, p = .00 (Table 2). The average MFS score for the entire sample at admission was 57.2. The average scores for the patients who fell and who did not fall were 65.0 and 56.4. Utilizing a two-sample T–test, this difference was not quite at the level of significance, = .071 (Table 3). Analysis utilizing multivariant logistic regression analysis showed that low FIM score and high MFS score both increased the risk of fall, but the FIM score was a better predictor of risk of fall. Using both scores was not significantly better than using just the FIM (Table 4). Table 5 shows that if the groups are divided into quartiles by FIM and MFS, as the FIM scores increase the rate of falls decrease from quartile to quartile, but that relationship is not seen with the MFS (Table 5). The FIM score with the best combination of specificity and sensitivity was 68 (Figure 1). The average FIM score at discharge was significantly higher than the FIM score at admission (73.9–94.8). The MFS score was not designed as a measure of function, but there also was a significant improvement in MFS score from admission to discharge (57.2–51.6). Patients who fell had a significantly longer LOS than patients who did not fall (Table 1). The median LOS of patients who did not fall was 9 days and the median LOS of patients who fell was 21 days. Patient falls tended to be midway (48%) in their stay. At the time of fall, 32 patients had a higher FIM score than at admission, two patients had the same FIM score as at admission and only three had a lower FIM score than at admission.

Table 1. Univariate logistic regression
VariableOdds Ratio for Falling95% Odds Ratio Confidence Interval p
Age (per year of age)1.0000.979–1.023.973
Gender
Female1.0reference 
Male0.9850.500–1.941.966
LOS (per day)1.0771.052–1.104.000
Admission FIM Score0.9460.924–0.969.000
Admission MFS Score 1.0211.004–1.038.014
Table 2. Two-Sample T-Test and CI: FIM (A), fall Two-sample T for FIM (A)
FallNMeanStandard DeviationSE Mean
  1. Difference = mu (0)–mu (1)

  2. Estimate for difference: 11.4320

  3. 95% CI for difference: (6.0642, 16.7998)

  4. T-Test of difference = 0 (vs not =): T-Value = 4.30 p-value = .000 DF = 41

No34874.8 12.7 0.68
Yes 3763.415.62.6
Table 3. Two-Sample T-Test and CI: MFS (A), fall
FallNMeanStandard DeviationSE Mean
  1. Difference = mu (0)–mu (1)

  2. Estimate for difference: −8.62069

  3. 95% CI for difference: (−18.02988, 0.78850)

  4. T-Test of difference = 0 (vs not =): T-Value = −1.85 p-value = 0.071 DF = 39

Yes34856.4 18.9 1.0
No3765.027.64.5
Table 4. Multivariate logistic regression
VariableHazard Ratio95% Hazard Ratio Confidence Interval p
MODEL: LOS plus FIM
LOS1.0651.037–1.093.000
FIM0.9740.946–1.002.069
MODEL: LOS plus MFS
LOS1.0751.049–1.102.000
MFS1.0110.993–1.028.239
MODEL: MFS and FIM
FIM0.9500.927–0.973.000
MFS1.0110.994–1.028.193
Table 5. FIM and MFS scores by quartile
FIM QuartileFIM ScoreFall RatePercent
A. FIM score by Quartile
121–54 9/34 26
254–6917/89 19
370–84 6/101 6
485–106 3/75 4
MFS QuartileMFS ScoreFall RatePercent
B. MFS by Quartile
190–125 6/26 23
265–8514/119 12
340–6014–119 6
40–35 6/46 13
image

Figure 1. ROC curve. Point closest to Sens = 1 and Spec = 1 is at FIM(A) cutoff less than or equal to 68 At that point Sens is 70% and Spec is 73%. In the dataset using that cutoff there are 26 true positives, 257 true negatives, 91 false positives and 11 false negatives; accuracy is 74%.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. Implications
  8. Limitations
  9. References

Morse used 45 or above as scores that suggested that patients on a rehabilitation unit were at high risk of fall (Morse, 1997; Harrington, Luquire, Vish & Winter, 2010). She reported that the MFS has a sensitivity of 0.78 and a specificity of 0.83 (Morse, 1997) meaning that 78% of the subjects who fell had scores of 45 or above and 83% of the subjects with scores below 45 did not fall. McCollum (1995) conducted a prospective study of 458 patients and found a sensitivity of 0.96 and a specificity of 0.54. She concluded that the MFS is useful in identifying patients at risk for fall. These investigators found the MFS to be a valid measure in that it could help identify which patients were most likely to fall. Morse (1997) reported an inter-rater reliability of 96%.

Other studies have been less supportive of the validity of the MFS. Kwan et al. (2012) compared 35 patients at an IRF who fell to 35 patients who did not fall and found no significant difference in total MFS or on the score on any of the six subcomponents of the scale. O'Connell and Myers (2002) reported on 1059 consecutive admissions to an acute care hospital and found a predictive value of only 18%. Chow et al. (2007) reported a series of 954 consecutive admissions to medical, geriatric, and rehabilitation units in Hong Kong and found no significant differences in the MFS scores of patients who fell and patients who did not fall. Kim, Mordiffi, Bee, Devi and Evans (2007) used receiver operating curves (ROC) to try to determine the MFS score that would give the optimum sensitivity and specificity. They found that 55 was the best score to identify patients at high risk for fall. They could not find any score that gave good sensitivity and specificity and had a good predictive value. Schwendiman, De Geest and Milisen (2006), using ROC, also found that 55 was a better cutoff score than the 45. They found the positive predictive value of the MFS to be only 12%–24%. These values were not better than the clinical judgments of the nursing staff. Eagle et al. (1999) and Haines, Hill and Osborne (2007) in separate studies, also both reported that the MFS did not have a better predictive value than the judgment of the nursing staff. Salaman, Victory and Bobay (2012) looked at the positive predictive value (which they defined as the number of true positives divided by the number of true positives plus false positives) and the negative predictive value (false negatives divided by true negatives) and found the values too low to be helpful in predicting risk of fall of patients admitted to a rehabilitation unit.

Consideration of the elements of the MFS may put into question whether it is really well designed to separate patients admitted to an IRF into groups more and less likely to fall. Altered mental status and a history of falls are recognized risks for fall (Rubenstein, 2006). The other items in the scale do not seem appropriate to separate patients admitted to IRF into two different groups with different risk of fall. All patients in this study had multiple medical problems. The criteria for admission to an IRF make it unlikely that many, if any, patients have no difficulty in ambulating or can walk safely without ambulatory aid. There are no articles documenting that an intravenous line or heparin lock is a significant risk for fall. Chow et al. (2007) performed item-item correlation of the six items in the MFS and found the lowest correlation was between presence of an intravenous therapy and the other items in the scale.

The FIM was developed to measure the skills trained in an IRF and the patients’ abilities to perform them safely without help. Although the scale was not developed to assess the risk of falling, the abilities that it measures—transfers, walking, safe use of bathroom facilities, cognition and communication—are associated with a person's risk of fall (Fortinsky, Baker, Gottschalk, Trella & Tinetti, 2008; Rubenstein, 2006; Spoelstra, Given, You & Given, 2012). There are many studies suggesting that it can provide information about risk of fall. Forrest et al. (2012) reported that each increase in total FIM, motor FIM, and cognitive FIM reduce risk of fall by 0.955%, 0.949%, and 0.925%, respectively. Kwan et al. (2012) reported a significant inverse relationship between total FIM score and likelihood of fall. They also reported correlation between falls and low scores on many of the components including eating, grooming, bathing, dressing upper body, toileting, all transfers, stairs, receptive and expressive language, social cognition, and problem solving (Kwan et al., 2012). Gilewski et al. (2007) reported an inverse relation between mobility and problem-solving items on FIM and risk of fall. Saverino et al. (2006) documented a correlation between FIM and falls in patients with orthopedic and neurologic problems. Petitpierre et al. (2010) reviewed the admissions of 23,966 admissions to acute care units. He found that there was an inverse correlation between FIM score and rate of fall, but also reported that the predictive values were not good enough to allow the FIM to assess the risk of fall of an individual patient. Teasell et al., 2002; Zdobysz et al., 2005; Suzuki et al. (2005; Lee & Stokic, 2008) all found an inverse relation between admission FIM score and rate of falls in patients admitted to rehabilitation units after strokes. The studies that have looked at the relationship between FIM score and falls have all shown that patients with low FIM scores are more likely to fall than patients with high FIM scores.

The FIM and MFS both have questions that evaluate safety of mobility and cognition. The MFS has one measure of cognitive skill and two of mobility skills. The FIM has 13 measures affected by motor skills and 5 measures of cognitive skills. The FIM gives a more precise measure of mobility skills in that it evaluates transfers, commode transfers, gait, stairs, and endurance. It gives a more precise measure of cognition in that it looks at language, memory, problem solving and social skills. The FIM while not designed to measure risk of falls is designed and routinely used to track which factors that put the patient at risk for fall are improving and which need more attention either in terms of improving the patient's ability or providing adequate supervision and help.

Implications

  1. Top of page
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. Implications
  8. Limitations
  9. References

CMS requires that all patients admitted to IRF be scored using the FIM. All patients on a rehabilitation unit have some risk of fall. A FIM score of 68 or less may indicate an increased risk of fall compared to patients with higher scores. It is not necessary for nursing staff to spend additional time documenting patient scores on the MFS as this will not add to the predictive value of the FIM.

The FIM has a user's guide that includes a decision tree that must be followed to properly score each of the 18 items in the measure. It takes more training and more time to use the FIM than the MFS. Nurses working on units other than an IRF that are not required to use the FIM as part of the reporting process to CMS may prefer the MFS to the FIM as they may not have access to training on scoring the FIM. They may find the MFS easier and less time consuming than the FIM.

Limitations

  1. Top of page
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. Implications
  8. Limitations
  9. References

The article is limited by the nature of the problem studied. Based on the criteria necessary for admission to an IRF, all patients on the unit will be at some risk of fall and it is unlikely that a score on any scale can either preclude the risk of fall or indicate that a patient on a unit with appropriate supervision of patient safety will be more likely than not to fall.

Key Practice Points
  • A patient with a low Functional Independence Measure (FIM) score is more likely to fall than a patient with a high FIM score.
  • FIM is a more comprehensive measure of patient function than the Morse Fall Scale and for the patient population on an inpatient rehabilitation unit may be better designed to separate patients at highest risk of fall than is the Morse Fall Scale.
  • All patients who meet criteria for admission to an inpatient rehabilitation unit are at risk for fall. Patients with FIM scores of 68 or less may be at higher risk for fall than patients with scores higher than 68.
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References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Method
  5. Results
  6. Discussion
  7. Implications
  8. Limitations
  9. References
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