- Email: [email protected]
Predictive value of the Royal Melbourne Hospital Falls Risk Assessment Tool (RMH FRAT) for post-stroke patients
Journal of Clinical Neuroscience 21 (2014) 607–611
Contents lists available at ScienceDirect
Journal of Clinical Neuroscience journal homepage: www.elsevier.com/locate/jocn
Clinical Study
Predictive value of the Royal Melbourne Hospital Falls Risk Assessment Tool (RMH FRAT) for post-stroke patients Colleen Ma a, Kelly Evans b, Carin Bertmar b, Martin Krause b,⇑ a b
Sydney Medical School, The University of Sydney, NSW, Australia Royal North Shore Hospital, Reserve Road, St Leonards, NSW 2065, Australia
a r t i c l e
i n f o
Article history: Received 26 November 2012 Accepted 15 June 2013
Keywords: Accidental falls Post stroke Predictive value Risk assessment
a b s t r a c t Falls after stroke are common and carry a significant disease burden. Several scores aim to identify patients who are at risk of falls to implement primary prevention therapy. The aim of this study was to determine the validity of the commonly used Falls Risk Assessment Tool (FRAT) developed at the Royal Melbourne Hospital (RMH) in 1995 for predicting falls after a stroke. The RMH FRAT was administered within 2 weeks after discharge post-stroke. Occurrence of falls was recorded at 3 and 6 months poststroke in 202 and 152 patients, respectively. In our study 90% of patients were placed in the RMH FRAT high risk or medium risk group. In these two groups the RMH FRAT did not provide sufficient predictive value. Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved.
1. Introduction Falls are a common cause of injury in the elderly population, accounting for up to 10% of emergency room admissions [1,2]. Falls may cause serious injuries including hip fractures, head injuries and soft tissue trauma. Death may result if the injury is extreme. The adverse effects of a previous fall may also manifest psychologically. This includes depression, loss of self-esteem and a fear of falling. This can have debilitating effects and results in increased admissions to aged care institutions [3]. Stroke patients have double the fall rate to age-matched controls [4,5]. A previous study has shown that 39% of stroke patients have reported at least one fall during their rehabilitation admission, with 24% having fallen more than once [4]. The increase in the number of falls in hospital and nursing home settings is due to a variety of risk factors. Stroke patients have a higher risk of falling due to impaired motor and visual function as well as sensory inattention [6]. Age over 65 is also a risk factor for repeated falls [7,8] while depression has been found to increase the chances of falling in the 12 months following hospitalisation for stroke [8,9]. Fall-related injuries are a significant burden on the Australian health system, with an estimated annual cost of $AUD406.4 million [10]. It has been predicted that this figure will increase to $AUD788.7 million by 2021 [11]. To reduce the health burden caused by falls, primary prevention is the most cost efficient method, in that the identification of patients at risk for falls will enable health workers to target those at high risk with measures to ⇑ Corresponding author. Tel.: +61 2 9463 1731; fax: +61 2 9463 1071. E-mail address: [email protected] (M. Krause).
prevent falls and hence reduce the effects of disease after stroke. An important component of prevention involves assessment of fall risk in the elderly. To this end, several risk factors have been identified over the years and fall risk assessment tools have been developed in order to provide an efficient way of predicting the fall risk of patients. Fall risk assessment tools (FRAT) can be classified either as mono-dimensional or multi-dimensional [12], depending on the number of dimensions included, with factors associated with falling, fear of falling and physical status each making up one dimension. One mono-dimensional FRAT is the Royal Melbourne Hospital (RMH) FRAT which is the subject of this study. Originally developed over a 2 year period from 1994 to 1995 at the RMH [14] as part of their Falls Prevention Program, the RMH FRAT was created with the intention to assess fall risk in patients hospitalised due to cerebrovascular incidents [13]. Despite this fact, this FRAT has not been validated to assess the fall risk after stroke.
2. Method Patients in this study were part of the Royal North Shore Hospital (RNSH) post-acute Community Stroke Care Program (CSCP) after being admitted to the Neurology medical ward with an acute stroke or transient ischaemic attack between August 2004 and September 2011. Patients signed their consent for the program prior to discharge. The program and subsequent study was approved by the RNSH Ethics Committee. The CSCP involved a home visit by a nurse 7–14 days after discharge from hospital or rehabilitation, followed by a telephone call after 3 months and a second home visit 6 months after discharge.
0967-5868/$ - see front matter Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jocn.2013.06.018
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C. Ma et al. / Journal of Clinical Neuroscience 21 (2014) 607–611
The patient was then assigned to one of three categories depending on the total number of points in accordance with Mercer [14]: low risk (0–4 points), medium risk (5–14 points) and high risk (P15 points).
Table 1 Royal Melbourne Hospital Falls Risk Assessment Tool Category
Characteristics
Value
A B C D E F
Age > 70 Past history of falls or seizures Disorientation/Mental impairment Impaired sight Impaired coordination Medications e.g. sedatives
G
Continence status, including nocturia, urgency, etc. Less than 24 hours post-operative
5 3 10 1 3 1 for each medication 1 for each
H
2.2. Statistical analysis
1
Patients’ risk of falling was assessed using the RMH FRAT at first home visit. Numbers of actual falls (if any) were recorded at 3 month and 6 month follow-up. Two hundred and two patients were included in our data analysis at 3 months. Fifty of these patients were lost to follow-up at 6 months due to different reasons.
2.1. RMH FRAT The fall risk score given by the RMH FRAT is determined by the number of risk factors each patient possesses. These include age, sex, previous history of falls or seizures, disorientation, sensory impairment, impaired coordination, medications and continence status. Points were then given for each criterion the patient fulfilled, with a higher number of points corresponding to an increased risk of falling (Table 1).
Logistic regression and receiver operating characteristic (ROC) analysis was used to determine if risk score was correlated to the probability of falling. ROC analysis was performed to find the appropriate cut-off value for fall status. This value was then used to calculate the sensitivity, specificity, positive predictive value and negative predictive value. The data were analysed using the Statistical Package for the Social Sciences version 19 (SPSS, Chicago, IL, USA). 3. Results Patient demographics are displayed in Table 2. Patients were analysed in respect to their fall status at 3 months and 6 months after discharge. Their RMH FRAT scores at discharge (given on a scale of 0–37) were sorted into three categories: low risk (0–4), medium risk (5–14) and high risk (P15). The median risk score was 12 (interquartile range 8–15). Table 3 displays the number of actual falls at 3 and 6 month follow-up per fall risk category. Overall 49 (24.3%) and 44 (28.9%) patients had fallen at 3 and 6 months, respectively. Low risk patients made up about 10% of patients in this study and no falls occurred in these patients at 3 and 6 months. Over 70% of medium risk patients did not fall while about 60% of those categorised as high risk did not fall during the follow-up period. No patient under
Table 2 Patient demographics Sample characteristics
3 months (n = 202)
6 months (n = 152)
Age range 60 years or less 1–70 71-80 81–90 >90
21 43 58 70 10
16 (10.5%) 35 (23.0%) 47 (30.9%) 48 (31.6%) 6 (4.0%)
Sex Female Male
83 (41.1%) 119 (58.9%)
58 (38.2%) 94 (61.8%)
Previous stroke Principle diagnosis Ischaemic stroke TIA Haemorrhagic stroke
48 (23.8%) 177 (87.6%) 18 (8.9%) 7 (3.5%)
36 (23.7%) 131 (86.2%) 16 (10.5%) 5 (3.3%)
Living circumstances Alone Family Spouse Retirement village Other
58 (28.7%) 18 (8.9%) 113 (55.9%) 6 (3.0%) 7 (3.5%)
44 (28.9%) 12 (7.9%) 86 (56.6%) 5 (3.3%) 5 (3.3%)
(10.4%) (21.3%) (28.7%) (34.6% (5.0%)
TIA = transient ischaemic attack. Table 3 Fall risk category and fall status at 3 and 6 months after discharge 3 months
Low Medium High Total, n
6 months
Faller
Non-Faller
Total
Faller
Non-Faller
Total
0 (0%) 28 (57.1%) 21 (42.9%) 49
20 (13.1%) 102 (66.7%) 31 (20.2%) 153
20 (9.9%) 130 (64.4%) 52 (25.7%) 202
0 (0%) 27 (61.4%) 17 (38.6%) 44
14 (13.0%) 71 (65.7%) 23 (21.3%) 108
14 (9.2%) 98 (64.5%) 40 (26.3%) 152
Percentages calculated per column.
C. Ma et al. / Journal of Clinical Neuroscience 21 (2014) 607–611
609
Fig. 1. Receiver operating characteristic curve at 3 months after discharge from hospital or rehabilitation.
the age of 50 suffered a fall after discharge. Data at 6 months showed a positive linear relationship between age and proportion of patients who fell. A logistic regression model was used to calculate the odds ratio of falling. At 3 months, the odds ratio was found to be 1.13 (95% confidence interval: 1.07–1.20) (Fig. 1). This remained similar at 6 months (Fig. 2). In other words, for every incremental increase on the falls risk scale, there was an approximate 1.1% increased chance of falling. At 3 months, calculations yielded a sensitivity of 69.4% (indicates proportion of patients that had received a high score on the RMH FRAT and had fallen) and a specificity of 66.7% (patients with a low score that had not fallen). The positive predictive value was 40.0% and the negative predictive value was 87%. The relative lower positive predictive value indicates that the scale is a poorer predictor of whether patients will actually fall. In contrast, the high negative predictive value shows that the scale is more accurate in predicting which patients will not fall. At 6 months, sensitivity increased to 84.1% while specificity decreased to 55.6%. There was a minor increase in both the positive predictive value and negative predictive value to 43.5% and 89.6%, respectively. 4. Discussion The RMH FRAT scale is a good negative predictor of falls, identifying patients with low or no fall risk. This is consistent with the negative predictive value which is nearly 90% at both 3 and 6 months. The scale exhibits a low positive predictive value and while sensitivity does increase at 6 months, this is concerning as most preventative measures should be implemented as soon as possible upon discharge. The odds ratio also indicates a lack of predictive value, as there is only about a 10% increase in the chance of falling between patients in the low risk group (those with scores between 0–4) and those in low end of the high risk group (those with a score
of 15). This is consistent with our findings in that more than half (60%) of high risk patients had no falls. Additionally, the event rate of falls in the medium risk group was not significantly different to the high risk group and did not provide a reliable prediction of falls. The vast majority of patients (64% of the total number) were scored as medium risk, yet over 70% of the patients in this category did not fall. Therefore, the scale does not provide a sufficient predictive value for the majority of patients. However, our study did have certain limitations. One is that the occurrence of falls was self-reported by patients and not validated with medical documentation; therefore, there is a risk that the number of falls may have been under-reported. Another limitation is due to the time at which the RMH FRAT was used in assessment. Since patients had their fall risk assessed at the first home visit, it is very likely that those who scored higher would also have received recommendations on preventing falls, thus decreasing the number of falls at 3 months and 6 months. The predictive value of the RMH FRAT for a general patient population has been tested previously [13,15]. A small validity study (n = 91) regarding the individual components of the RMH FRAT was performed in 2003 at St. George Hospital in Sydney, Australia [15]. The study was a 10 week survey that included all patients that had fallen in the hospital. It was found that patients possessing three risk factors – over 75 years of age, impaired ambulatory capacity and cognitive impairment – were at a high risk of falling, which is consistent with other studies. Cognitive impairment was not differentiated by cause. Furthermore, in a review of multiple FRAT a threshold value was calculated and any patient with a score above this value was classified as high risk [15]. The threshold value for this particular scale was found to be 4. This is consistent with our findings as low risk patients have a score of 0–4. A score of 15 or above was associated with interventions. However, since approximately 90% of the patients in our study had a score above 4, the overall predictive value of this scale is in question.
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C. Ma et al. / Journal of Clinical Neuroscience 21 (2014) 607–611
Fig. 2. Receiver operating characteristic curve at 6 months after discharge from hospital or rehabilitation.
A review of the literature shows that similar mono-dimensional scales that are more widely used share the same flaws. A validity study of the St. Thomas’ Risk Assessment Tool In Falling Elderly Inpatients (STRATIFY) in stroke patients showed poor performance as a predictor of falls, with baseline assessment sensitivity of 11.3% [18]. It too demonstrated a high negative predictive value (89.5%) and low positive predictive value (25.0%). An assessment of the Morse Fall Scale likewise demonstrated comparable numbers, with negative predictive value of 99.7% and positive predictive value of 19.0% [17]. Validity testing of the Prediction of Falls in Rehabilitation Settings Tool (Predict FIRST) found that it had an odds ratio of 5.21 (95% confidence interval 1.10–24.78) at 6 weeks from the onset of stroke.[19] As well, 85.3% of the participants in the Predict FIRST trial had a score between 0 and 3 (equivalent to a score risk between 4% and 18% respectively) with the number of patients actually falling being 20.6%. For the RMH FRAT, approximately 90% of patients were classified as medium or high risk, with 24.3% actually falling. Two other mono-dimensional falls risk assessment tools are commonly used in Australia – the Peninsula Health FRAT (PH-FRAT) and Ballarat Health Services FRAT (BHS-FRAT). As with the RMH FRAT, these two scales include similar items such as fall history, medications and cognitive status. However, only the BHS-FRAT has undergone validity testing by researchers outside the initial site of development [16,17]. Testing showed high sensitivity (95%) and low specificity (9%) at 6 months, with a negative predictive value of 64% and positive predictive value of 51%. In contrast, multi-dimensional tools were found to have increased validity and reliability in predicting falls in the elderly. A study by Baetans et al. compared a general model (multi-dimensional) and a mobility model (mono-dimensional) [20]. Sensitivity for the two models was 94.1% and 76.3% respectively, showing that considering more than just mobility factors can help increase the accuracy of predicting falls risk. Individual components of the RMH FRAT have been subject to a validation study [15]. These were all risk factors that had been identified as good predictors of fall risk from the literature.
However, the authors agreed that the majority of risk factors that the RMH FRAT takes into account were not consistently present in those patients that had fallen. This includes the risk factor ‘‘past history of falls’’ which was identified by the PH-FRAT [16] as the most significant risk factor for falls but was found to be unreliable in the component validation study [15] as less than half of the patients that did fall had a history of falling. In view of the significant number of patients that did not fall in our study, further research needs to determine common factors that identify those patients that have risk factors for falls but do not fall. Conflicts of interest/disclosures The authors declare that they have no financial or other conflicts of interest in relation to this research and its publication. Acknowledgments This study was generously supported by the Staff Specialist Trust Fund Royal North Shore Hospital. We would like to acknowledge the contribution of Jillian Patterson from the Kolling Institute of Medical Research, Northern Clinical School, University of Sydney, for her statistical support. References [1] Sattin RW. Falls among older persons: a public health perspective. Annu Rev Public Health 1992;13:489–508. [2] Tinetti ME. Clinical practice. Preventing falls in elderly persons. N Engl J Med 2003;348:42–9. [3] Cumming R, Salkeld G, Thomas M, et al. Prospective study of the impact of fear of falling on activities of daily living, SF-36 scores, and nursing home admission. J Gerontol A Biol Sci Med Sci 2000;55:M299–305. [4] Nyberg L, Gustafson Y. Patient falls in stroke rehabilitation. A challenge to rehabilitation strategies. Stroke 1995;26:838–42. [5] Nyberg L, Gustafson Y. Fall prediction index for patients in stroke rehabilitation. Stroke 1997;28:716–21. [6] Campbell AJ, Borrie MJ, Spears GF. Risk factors for falls in a community-based prospective study of people 70 years and older. J Gerontol 1989;44:M112–7. [7] Czernuszenko A, Czlonkowka A. Risk factors falls in stroke patients during inpatient rehabilitation. Clin Rehabil 2009;23:176–88.
C. Ma et al. / Journal of Clinical Neuroscience 21 (2014) 607–611 [8] Uger C, Gucuyener D, Uzuner N, et al. Characteristics of falling in patients with stroke. J Neurol Neurosurg Psychiatry 2000;69:649–51. [9] Jorgenson L, Engstad T, Jacobsen BK. Higher incidence of falls in long-term stroke patients than in population controls: depressive symptoms predict falls after stroke. Stroke 2002;33:542–7. [10] Mathers C, Penn R. Health system costs of injury, poisoning and musculoskeletal disorders in Australia 1993–94. Canberra: Australian Institute of Health and Welfare; 1999. [11] Moller J. Projected costs of fall related injury to older persons due to demographic change in Australia. Adelaide: New Directions in Health and Safety; 2003. [12] Hassankhani H, Kakhki AD, Jafarabadi MA, et al. Elder fall risk predictors. Int Res J Appl Basic Sci 2012;3:1662–72. [13] Perell K, Nelson A, Goldman RL, et al. Fall risk assessment measures: an analytic review. J Gerontol A Biol Sci Med Sci 2001;56:M761–6. [14] Mercer L. Falling out of favour. Aust Nurs J 1997;4:27–9.
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[15] Donoghue J, Graham J, Gibbs J, et al. Validating components of a falls risk assessment instrument. Int J Health Care Qual Assur 2003;16:21–8. [16] Stapleton C, Hough P, Oldmeadow L, et al. Four-item fall risk screening tool for subacute and residential aged care: the first step in fall prevention. Australas J Ageing 2009;28:139–43. [17] Wong Shee A, Phillips B, Hill K. Comparison of two fall risk assessment tools (FRATs) targeting falls prevention in sub-acute care. Arch Gerontol Geriatrics 2012;55:653–9. [18] Smith J, Forster A, Young J. Use of the ‘STRATIFY’ falls risk assessment in patients recovering from acute stroke. Age Ageing 2006;35:138–43. [19] Nystrom A, Hellstrom K. Fall risk six weeks from onset of stroke and the ability of the Prediction of Falls in Rehabilitation Settings Tool and motor function to predict falls. Clin Rehabil 2012;27:473–9. [20] Baetans T, De Kegel A, Calders P, et al. Prediction of falling among stroke patients in rehabilitation. J Rehabil Med 2011;43:876–83.
Contents lists available at ScienceDirect
Journal of Clinical Neuroscience journal homepage: www.elsevier.com/locate/jocn
Clinical Study
Predictive value of the Royal Melbourne Hospital Falls Risk Assessment Tool (RMH FRAT) for post-stroke patients Colleen Ma a, Kelly Evans b, Carin Bertmar b, Martin Krause b,⇑ a b
Sydney Medical School, The University of Sydney, NSW, Australia Royal North Shore Hospital, Reserve Road, St Leonards, NSW 2065, Australia
a r t i c l e
i n f o
Article history: Received 26 November 2012 Accepted 15 June 2013
Keywords: Accidental falls Post stroke Predictive value Risk assessment
a b s t r a c t Falls after stroke are common and carry a significant disease burden. Several scores aim to identify patients who are at risk of falls to implement primary prevention therapy. The aim of this study was to determine the validity of the commonly used Falls Risk Assessment Tool (FRAT) developed at the Royal Melbourne Hospital (RMH) in 1995 for predicting falls after a stroke. The RMH FRAT was administered within 2 weeks after discharge post-stroke. Occurrence of falls was recorded at 3 and 6 months poststroke in 202 and 152 patients, respectively. In our study 90% of patients were placed in the RMH FRAT high risk or medium risk group. In these two groups the RMH FRAT did not provide sufficient predictive value. Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved.
1. Introduction Falls are a common cause of injury in the elderly population, accounting for up to 10% of emergency room admissions [1,2]. Falls may cause serious injuries including hip fractures, head injuries and soft tissue trauma. Death may result if the injury is extreme. The adverse effects of a previous fall may also manifest psychologically. This includes depression, loss of self-esteem and a fear of falling. This can have debilitating effects and results in increased admissions to aged care institutions [3]. Stroke patients have double the fall rate to age-matched controls [4,5]. A previous study has shown that 39% of stroke patients have reported at least one fall during their rehabilitation admission, with 24% having fallen more than once [4]. The increase in the number of falls in hospital and nursing home settings is due to a variety of risk factors. Stroke patients have a higher risk of falling due to impaired motor and visual function as well as sensory inattention [6]. Age over 65 is also a risk factor for repeated falls [7,8] while depression has been found to increase the chances of falling in the 12 months following hospitalisation for stroke [8,9]. Fall-related injuries are a significant burden on the Australian health system, with an estimated annual cost of $AUD406.4 million [10]. It has been predicted that this figure will increase to $AUD788.7 million by 2021 [11]. To reduce the health burden caused by falls, primary prevention is the most cost efficient method, in that the identification of patients at risk for falls will enable health workers to target those at high risk with measures to ⇑ Corresponding author. Tel.: +61 2 9463 1731; fax: +61 2 9463 1071. E-mail address: [email protected] (M. Krause).
prevent falls and hence reduce the effects of disease after stroke. An important component of prevention involves assessment of fall risk in the elderly. To this end, several risk factors have been identified over the years and fall risk assessment tools have been developed in order to provide an efficient way of predicting the fall risk of patients. Fall risk assessment tools (FRAT) can be classified either as mono-dimensional or multi-dimensional [12], depending on the number of dimensions included, with factors associated with falling, fear of falling and physical status each making up one dimension. One mono-dimensional FRAT is the Royal Melbourne Hospital (RMH) FRAT which is the subject of this study. Originally developed over a 2 year period from 1994 to 1995 at the RMH [14] as part of their Falls Prevention Program, the RMH FRAT was created with the intention to assess fall risk in patients hospitalised due to cerebrovascular incidents [13]. Despite this fact, this FRAT has not been validated to assess the fall risk after stroke.
2. Method Patients in this study were part of the Royal North Shore Hospital (RNSH) post-acute Community Stroke Care Program (CSCP) after being admitted to the Neurology medical ward with an acute stroke or transient ischaemic attack between August 2004 and September 2011. Patients signed their consent for the program prior to discharge. The program and subsequent study was approved by the RNSH Ethics Committee. The CSCP involved a home visit by a nurse 7–14 days after discharge from hospital or rehabilitation, followed by a telephone call after 3 months and a second home visit 6 months after discharge.
0967-5868/$ - see front matter Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jocn.2013.06.018
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C. Ma et al. / Journal of Clinical Neuroscience 21 (2014) 607–611
The patient was then assigned to one of three categories depending on the total number of points in accordance with Mercer [14]: low risk (0–4 points), medium risk (5–14 points) and high risk (P15 points).
Table 1 Royal Melbourne Hospital Falls Risk Assessment Tool Category
Characteristics
Value
A B C D E F
Age > 70 Past history of falls or seizures Disorientation/Mental impairment Impaired sight Impaired coordination Medications e.g. sedatives
G
Continence status, including nocturia, urgency, etc. Less than 24 hours post-operative
5 3 10 1 3 1 for each medication 1 for each
H
2.2. Statistical analysis
1
Patients’ risk of falling was assessed using the RMH FRAT at first home visit. Numbers of actual falls (if any) were recorded at 3 month and 6 month follow-up. Two hundred and two patients were included in our data analysis at 3 months. Fifty of these patients were lost to follow-up at 6 months due to different reasons.
2.1. RMH FRAT The fall risk score given by the RMH FRAT is determined by the number of risk factors each patient possesses. These include age, sex, previous history of falls or seizures, disorientation, sensory impairment, impaired coordination, medications and continence status. Points were then given for each criterion the patient fulfilled, with a higher number of points corresponding to an increased risk of falling (Table 1).
Logistic regression and receiver operating characteristic (ROC) analysis was used to determine if risk score was correlated to the probability of falling. ROC analysis was performed to find the appropriate cut-off value for fall status. This value was then used to calculate the sensitivity, specificity, positive predictive value and negative predictive value. The data were analysed using the Statistical Package for the Social Sciences version 19 (SPSS, Chicago, IL, USA). 3. Results Patient demographics are displayed in Table 2. Patients were analysed in respect to their fall status at 3 months and 6 months after discharge. Their RMH FRAT scores at discharge (given on a scale of 0–37) were sorted into three categories: low risk (0–4), medium risk (5–14) and high risk (P15). The median risk score was 12 (interquartile range 8–15). Table 3 displays the number of actual falls at 3 and 6 month follow-up per fall risk category. Overall 49 (24.3%) and 44 (28.9%) patients had fallen at 3 and 6 months, respectively. Low risk patients made up about 10% of patients in this study and no falls occurred in these patients at 3 and 6 months. Over 70% of medium risk patients did not fall while about 60% of those categorised as high risk did not fall during the follow-up period. No patient under
Table 2 Patient demographics Sample characteristics
3 months (n = 202)
6 months (n = 152)
Age range 60 years or less 1–70 71-80 81–90 >90
21 43 58 70 10
16 (10.5%) 35 (23.0%) 47 (30.9%) 48 (31.6%) 6 (4.0%)
Sex Female Male
83 (41.1%) 119 (58.9%)
58 (38.2%) 94 (61.8%)
Previous stroke Principle diagnosis Ischaemic stroke TIA Haemorrhagic stroke
48 (23.8%) 177 (87.6%) 18 (8.9%) 7 (3.5%)
36 (23.7%) 131 (86.2%) 16 (10.5%) 5 (3.3%)
Living circumstances Alone Family Spouse Retirement village Other
58 (28.7%) 18 (8.9%) 113 (55.9%) 6 (3.0%) 7 (3.5%)
44 (28.9%) 12 (7.9%) 86 (56.6%) 5 (3.3%) 5 (3.3%)
(10.4%) (21.3%) (28.7%) (34.6% (5.0%)
TIA = transient ischaemic attack. Table 3 Fall risk category and fall status at 3 and 6 months after discharge 3 months
Low Medium High Total, n
6 months
Faller
Non-Faller
Total
Faller
Non-Faller
Total
0 (0%) 28 (57.1%) 21 (42.9%) 49
20 (13.1%) 102 (66.7%) 31 (20.2%) 153
20 (9.9%) 130 (64.4%) 52 (25.7%) 202
0 (0%) 27 (61.4%) 17 (38.6%) 44
14 (13.0%) 71 (65.7%) 23 (21.3%) 108
14 (9.2%) 98 (64.5%) 40 (26.3%) 152
Percentages calculated per column.
C. Ma et al. / Journal of Clinical Neuroscience 21 (2014) 607–611
609
Fig. 1. Receiver operating characteristic curve at 3 months after discharge from hospital or rehabilitation.
the age of 50 suffered a fall after discharge. Data at 6 months showed a positive linear relationship between age and proportion of patients who fell. A logistic regression model was used to calculate the odds ratio of falling. At 3 months, the odds ratio was found to be 1.13 (95% confidence interval: 1.07–1.20) (Fig. 1). This remained similar at 6 months (Fig. 2). In other words, for every incremental increase on the falls risk scale, there was an approximate 1.1% increased chance of falling. At 3 months, calculations yielded a sensitivity of 69.4% (indicates proportion of patients that had received a high score on the RMH FRAT and had fallen) and a specificity of 66.7% (patients with a low score that had not fallen). The positive predictive value was 40.0% and the negative predictive value was 87%. The relative lower positive predictive value indicates that the scale is a poorer predictor of whether patients will actually fall. In contrast, the high negative predictive value shows that the scale is more accurate in predicting which patients will not fall. At 6 months, sensitivity increased to 84.1% while specificity decreased to 55.6%. There was a minor increase in both the positive predictive value and negative predictive value to 43.5% and 89.6%, respectively. 4. Discussion The RMH FRAT scale is a good negative predictor of falls, identifying patients with low or no fall risk. This is consistent with the negative predictive value which is nearly 90% at both 3 and 6 months. The scale exhibits a low positive predictive value and while sensitivity does increase at 6 months, this is concerning as most preventative measures should be implemented as soon as possible upon discharge. The odds ratio also indicates a lack of predictive value, as there is only about a 10% increase in the chance of falling between patients in the low risk group (those with scores between 0–4) and those in low end of the high risk group (those with a score
of 15). This is consistent with our findings in that more than half (60%) of high risk patients had no falls. Additionally, the event rate of falls in the medium risk group was not significantly different to the high risk group and did not provide a reliable prediction of falls. The vast majority of patients (64% of the total number) were scored as medium risk, yet over 70% of the patients in this category did not fall. Therefore, the scale does not provide a sufficient predictive value for the majority of patients. However, our study did have certain limitations. One is that the occurrence of falls was self-reported by patients and not validated with medical documentation; therefore, there is a risk that the number of falls may have been under-reported. Another limitation is due to the time at which the RMH FRAT was used in assessment. Since patients had their fall risk assessed at the first home visit, it is very likely that those who scored higher would also have received recommendations on preventing falls, thus decreasing the number of falls at 3 months and 6 months. The predictive value of the RMH FRAT for a general patient population has been tested previously [13,15]. A small validity study (n = 91) regarding the individual components of the RMH FRAT was performed in 2003 at St. George Hospital in Sydney, Australia [15]. The study was a 10 week survey that included all patients that had fallen in the hospital. It was found that patients possessing three risk factors – over 75 years of age, impaired ambulatory capacity and cognitive impairment – were at a high risk of falling, which is consistent with other studies. Cognitive impairment was not differentiated by cause. Furthermore, in a review of multiple FRAT a threshold value was calculated and any patient with a score above this value was classified as high risk [15]. The threshold value for this particular scale was found to be 4. This is consistent with our findings as low risk patients have a score of 0–4. A score of 15 or above was associated with interventions. However, since approximately 90% of the patients in our study had a score above 4, the overall predictive value of this scale is in question.
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C. Ma et al. / Journal of Clinical Neuroscience 21 (2014) 607–611
Fig. 2. Receiver operating characteristic curve at 6 months after discharge from hospital or rehabilitation.
A review of the literature shows that similar mono-dimensional scales that are more widely used share the same flaws. A validity study of the St. Thomas’ Risk Assessment Tool In Falling Elderly Inpatients (STRATIFY) in stroke patients showed poor performance as a predictor of falls, with baseline assessment sensitivity of 11.3% [18]. It too demonstrated a high negative predictive value (89.5%) and low positive predictive value (25.0%). An assessment of the Morse Fall Scale likewise demonstrated comparable numbers, with negative predictive value of 99.7% and positive predictive value of 19.0% [17]. Validity testing of the Prediction of Falls in Rehabilitation Settings Tool (Predict FIRST) found that it had an odds ratio of 5.21 (95% confidence interval 1.10–24.78) at 6 weeks from the onset of stroke.[19] As well, 85.3% of the participants in the Predict FIRST trial had a score between 0 and 3 (equivalent to a score risk between 4% and 18% respectively) with the number of patients actually falling being 20.6%. For the RMH FRAT, approximately 90% of patients were classified as medium or high risk, with 24.3% actually falling. Two other mono-dimensional falls risk assessment tools are commonly used in Australia – the Peninsula Health FRAT (PH-FRAT) and Ballarat Health Services FRAT (BHS-FRAT). As with the RMH FRAT, these two scales include similar items such as fall history, medications and cognitive status. However, only the BHS-FRAT has undergone validity testing by researchers outside the initial site of development [16,17]. Testing showed high sensitivity (95%) and low specificity (9%) at 6 months, with a negative predictive value of 64% and positive predictive value of 51%. In contrast, multi-dimensional tools were found to have increased validity and reliability in predicting falls in the elderly. A study by Baetans et al. compared a general model (multi-dimensional) and a mobility model (mono-dimensional) [20]. Sensitivity for the two models was 94.1% and 76.3% respectively, showing that considering more than just mobility factors can help increase the accuracy of predicting falls risk. Individual components of the RMH FRAT have been subject to a validation study [15]. These were all risk factors that had been identified as good predictors of fall risk from the literature.
However, the authors agreed that the majority of risk factors that the RMH FRAT takes into account were not consistently present in those patients that had fallen. This includes the risk factor ‘‘past history of falls’’ which was identified by the PH-FRAT [16] as the most significant risk factor for falls but was found to be unreliable in the component validation study [15] as less than half of the patients that did fall had a history of falling. In view of the significant number of patients that did not fall in our study, further research needs to determine common factors that identify those patients that have risk factors for falls but do not fall. Conflicts of interest/disclosures The authors declare that they have no financial or other conflicts of interest in relation to this research and its publication. Acknowledgments This study was generously supported by the Staff Specialist Trust Fund Royal North Shore Hospital. We would like to acknowledge the contribution of Jillian Patterson from the Kolling Institute of Medical Research, Northern Clinical School, University of Sydney, for her statistical support. References [1] Sattin RW. Falls among older persons: a public health perspective. Annu Rev Public Health 1992;13:489–508. [2] Tinetti ME. Clinical practice. Preventing falls in elderly persons. N Engl J Med 2003;348:42–9. [3] Cumming R, Salkeld G, Thomas M, et al. Prospective study of the impact of fear of falling on activities of daily living, SF-36 scores, and nursing home admission. J Gerontol A Biol Sci Med Sci 2000;55:M299–305. [4] Nyberg L, Gustafson Y. Patient falls in stroke rehabilitation. A challenge to rehabilitation strategies. Stroke 1995;26:838–42. [5] Nyberg L, Gustafson Y. Fall prediction index for patients in stroke rehabilitation. Stroke 1997;28:716–21. [6] Campbell AJ, Borrie MJ, Spears GF. Risk factors for falls in a community-based prospective study of people 70 years and older. J Gerontol 1989;44:M112–7. [7] Czernuszenko A, Czlonkowka A. Risk factors falls in stroke patients during inpatient rehabilitation. Clin Rehabil 2009;23:176–88.
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