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Age and Ageing Advance Access originally published online on August 13, 2008
Age and Ageing 2008 37(6):702-706; doi:10.1093/ageing/afn153
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© The Author 2008. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Should elderly patients be screened for their ‘falls risk’? Validity of the STRATIFY falls screening tool and predictors of falls in a large acute hospital

SIR—Falls are not uncommon in hospitals settings at rates between 1.3 and 12.2% of all admissions [1–3] in acute facilities. Approximately 6% of falls result in serious injury such as bleeding or laceration, fracture and haematoma [4, 5]. Falls in hospital may lead to prolonged stay [6] or litigation [7]. Unfortunately, studies of interventions to prevent hospital-related falls are limited or of low quality [8] and provide no conclusive evidence that falls, in acute facilities, can be reduced through falls prevention programmes [9–11].

Despite this, screening to identify patients who may be at risk of falling is widespread. For example, recent Australian guidelines [12] recommend screening and assessment of all older people for risk of falling using the St Thomas' Risk Assessment Tool (STRATIFY tool) [13]. However, published studies about the ability of the STRATIFY tool to discriminate accurately between those with and without a high risk of falling [14–18] have been contradictory (Table 1). In addition, recent systematic reviews of fall screening tools have urged caution with their use because of their tendency to over-classify patients as high risk, leading to poorly targeted interventions [19–21]. The aim of the current study was to test the validity of the tool in our own setting, before introducing it as a standard practice.


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  		Table 1. Validity of the STRATIFY falls risk assessment tool in various settings

 
Methods

Population
We included inpatients from a 982-bed general, tertiary referral teaching hospital with a number of specialities including medicine, surgery, orthopaedics, psychiatry, oncology, gynaecology and trauma services. We obtained Human Research Ethics approval to extract follow-up data from patient records.

Instrument
The STRATIFY tool consists of five items, namely, assessing previous falls, agitation, visual impairment, toileting frequency and transfer/mobility problems, using the Barthel scoring system. Each item is scored with 1 for ‘yes’ or 0 for ‘no’; loadings are not used, giving each risk equal value. A score of 2 or greater is used to determine a high risk of falling [12].

Procedure
Patients 65 years and older were assessed within 48 h of admission by trained research officers. Falls incidence was assessed from the ‘Patient Incident Reports’ database and by extracting any falls related data from the patient's case notes. Any fall-related modifications made to the patient's management were documented.

Analysis
We defined a fall as ‘an event which results in a person coming to rest inadvertently on the ground or floor or other lower level’ as the primary outcome.

Data were entered and analysed using SPSS version 15.0. A Fisher's exact (two-sided) test or an independent samples t-test was used to examine group differences in categorical and continuous data respectively. Sensitivity (the proportion of fallers correctly classified as high risk of falling), specificity (the proportion of fallers correctly classified as low risk of falling), positive predictive values (PPV, the proportion of those classified as high risk who fell) and negative predictive values (NPV, the proportion of those classified as low risk who did not fall) [8] were calculated using the recommended cut-off point of ≥2 for the STRATIFY tool analysis. We also calculated the Youden Index [22] and the total predictive accuracy; both of which measure how well the STRATIFY risk score predicts falls. For both calculations, a score close to 1 indicates high predictive accuracy. Predictors of falling were identified using binary logistic regression. The patient was used as the unit of analysis, irrespective of the number of falls.

Results

Between 17 March and 24 October 2007, 788 patients were screened for falls risk. Participants were from surgical (41.5%), medical (41.2%), oncology (7.0%), extended stay or geriatric assessment and rehabilitation unit (GARU) (5.6%) or mental health unit (4.7%). The mean age was 77.7 years [standard deviation (SD) 7.91] and the mean length of stay was 27.7 days (range 1–224 days; SD 31.68). As many as 389 (49.4%) of the participants were male; 260 (32.6%) had experienced a previous fall; 178 (22.6%) were classified as ‘agitated’; 152 (19.3%) were visually impaired; 232 (29.4%) required frequent toileting and 305 (38.7%) had a transfer/mobility risk. The STRATIFY tool classified 335 (42.5%) patients as being at ‘falls risk’.

Seventy-two (9.1%) patients had a fall; of these, 39 falls (54.2%) occurred beside the bed, 20 (27.8%) in the bathroom and 13 (18.1%) in a variety of other ward areas. Of the 335 patients classified as being ‘at risk’ for falling, 59 (17.6%) did so, compared to 13 of 453 (2.9%) who were not at risk (P < 0.001), sensitivity 0.82 (95% CI: 0.71, 0.90), specificity 0.62 (95% CI: 0.58, 0.65), PPV 0.18 (95% CI: 0.14, 0.22), NPV 0.97 (95% CI: 0.95, 0.98). Accuracy of the STRATIFY, measured by the overall total predictive accuracy and the Youden Index, was moderate (0.63 and 0.44 respectively). When accuracy was analysed by patient mix, the total predictive accuracy was highest among mental health patients (0.86) and lowest in the GARU and long-stay patients (0.52). Accuracy using the Youden Index was highest for oncology patients (64.7) and lowest for surgical patients (36.8). Crude odds ratios (ORs) and 95% confidence intervals for the five STRATIFY risk factors and three demographic factors are given in Table 2. Statistically significant factors were entered simultaneously into a binary logistic regression model predicting falls. Having a previous fall(OR: 2.95; 95% CI: 1.68, 5.19), being agitated (OR: 1.82; 95% CI: 1.02, 3.23), having a transfer and mobility risk (OR: 2.63; 95% CI: 1.47, 4.71) and length of hospital stay (OR per day: 1.019; 95% CI: 1.012, 1.025) were significant predictors of falling in this cohort (P < 0.05) (Table 2).


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  		Table 2. Predictors of falling among a cohort of patients over 65 years in an acute hospital setting

 
Discussion

This is the first study to investigate the validity of the STRATIFY falls tool in an acute, inpatient mix including medical, surgical, mental health and oncology patients. It is timely given that the tool has been recommended for use in such populations. Consistent with other reports [15, 17, 18] and several systematic reviews [19–21], we found that the STRATIFY tool is an inadequate strategy for identifying those who may be at risk of falling in hospital settings. Although the STRATIFY was able to correctly identify those who would not fall, neither the tool as a whole nor individual items were able to discriminate well between those who later fell or did not fall. This was true of all inpatient groups; even though we had expected a much higher predictive accuracy among our GARU and long-stay medical patients.

There were some limitations to the study. We did not conduct any formal inter-rater reliability testing among the four research nurses who collected data. However, there was extensive discussion and agreement about the meaning of questionnaire items, so we do not expect that this would have affected outcomes. Our follow-up processes were quite rigorous; even so, there is a possibility that falls were not recorded either on the hospital's electronic database or in the patient's medical record leading to under-reporting of the falls rate. The other limitation was the small number of patients in the clinical areas of oncology, GARU/long-stay medical and mental health. Results from these areas were similar to those from our larger cohorts, providing some confidence that these results are meaningful.

To be operationally useful, a falls screening tool would require a predictive accuracy above 80%. In our setting, the tool had a high sensitivity and negative predictive value and a moderately high specificity, providing reassurance about patients at low risk; however, the more important statistic for health-care facilities is the positive predictive value, or the ability to identify patients who will fall. In our sample, 82% of patients who were classified as high risk using the STRATIFY did not fall, which is far too high to make it clinically useful. The purpose of screening for falls risk is to identify those at high risk, so that further multi-disciplinary assessment may be made. Routine use of the STRATIFY, with such a high false positive rate, would have considerable implications for hospital resources and may lead to poorly targeted interventions.

One of the reasons for a low predictive validity of the STRATIFY in acute hospitals is that the positive predictive value is a statistic dependent on the prevalence of the reference event, and in this case, falls. Data from our hospital show this clearly in Table 1. Fall rates between speciality groups ranged between 6.1 and 15.9 and, with the exception of the mental health cohort, positive predictive values reflected these rates.

After controlling the inter-relationships among the risk factors, two of the STRATIFY variables (visual impairment and frequent toileting) failed to maintain statistical significance. Similar findings, particularly with relation to visual impairment have been reported by other investigators [14, 23]. Of the risk factors remaining predictive after adjustment, the strongest were having a history of falling, having a high transfer/mobility score and length of hospital stay. Apart from the latter, these factors are generally known on admission, without recourse to the use of a falls risk-screening tool. Perhaps, a way forward would be to fully assess all patients with a history of falling and those with a high transfer/mobility risk and ensure that they receive evidence-based interventions such as close observation [24]. There is also an urgent need to test other, novel interventions in acute hospital settings.

Conclusion

The STRATIFY falls risk tool was significantly related to incidence of accidental falls in this large cohort but was a poor predictor of falls and cannot be recommended for routine use in acute hospital settings.

Key points

  • Falls are not uncommon in acute hospital settings but only a small proportion of such falls result in serious injury.
  • Risk-screening tools are used widely to predict which patients will fall.
  • Overall accuracy of the STRATIFY falls screening tool is low in acute hospital settings and cannot be recommended for routine use.

Funding

The study was funded by grants from the Queensland Nursing Council and the Strengthening Aged Care Project.

Conflict of interest

No conflict of interest exists for any of the authors. The study was funded through a competitive grant.

Joan Webster1,2,3,*, Mary Courtney2, Peter O'Rourke4, Nicole Marsh1, Catherine Gale1, Belynda Abbott1, Prue McRae1 and Kate Mason1

1 Centre for Clinical Nursing, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia
2 Institute of Health and Biomedical innovation, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia
3 Research Centre for Clinical and Community Practice Innovation, Griffith University, Nathan, QLD 4111, Australia
4 Queensland Institute of Medical Research, University of Queensland, Herston, QLD 4029, Australia

* To whom correspondence should be addressed Email: joan_webster{at}health.qld.gov.au

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