The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting

Webster, Joan, Courtney, Mary, Marsh, Nicole, Gale, Catherine, Abbott, Belynda, MacKenzie-Ross, Anita and McRae, Prue (2010) The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting. Journal of Clinical Epidemiology, 63 1: 109-113. doi:10.1016/j.jclinepi.2009.02.003

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Author Webster, Joan
Courtney, Mary
Marsh, Nicole
Gale, Catherine
Abbott, Belynda
MacKenzie-Ross, Anita
McRae, Prue
Title The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting
Journal name Journal of Clinical Epidemiology   Check publisher's open access policy
ISSN 0895-4356
Publication date 2010-01
Year available 2009
Sub-type Article (original research)
DOI 10.1016/j.jclinepi.2009.02.003
Volume 63
Issue 1
Start page 109
End page 113
Total pages 5
Place of publication Philadelphia, PA, United States
Publisher Elsevier
Collection year 2011
Language eng
Formatted abstract
Objective: To compare the effectiveness of the STRATIFY falls tool with nurses' clinical judgments in predicting patient falls.
Study Design and Setting: A prospective cohort study was conducted among the inpatients of an acute tertiary hospital. Participants were patients over 65 years of age admitted to any hospital unit. Sensitivity, specificity, and positive predictive value (PPV) and negative predictive values (NPV) of the instrument and nurses' clinical judgments in predicting falls were calculated.
Results: Seven hundred and eighty-eight patients were screened and followed up during the study period. The fall prevalence was 9.2%. Of the 335 patients classified as being "at risk" for falling using the STRATIFY tool, 59 (17.6%) did sustain a fall (sensitivity = 0.82, specificity = 0.61, PPV = 0.18, NPV = 0.97). Nurses judged that 501 patients were at risk of falling and, of these, 60 (12.0%) fell (sensitivity = 0.84, specificity = 0.38, PPV = 0.12, NPV = 0.96). The STRATIFY tool correctly identified significantly more patients as either fallers or nonfallers than the nurses (P = 0.027).
Conclusion: Considering the poor specificity and high rates of false-positive results for both the STRATIFY tool and nurses' clinical judgments, we conclude that neither of these approaches are useful for screening of falls in acute hospital settings.
© 2010 Elsevier Inc. All rights reserved.
Keyword Accidental falls
Sensitivity and specificity
Positive and negative predictive values
References [1] Haflon P, Eggli Y, Van Melle G, Vagnair A. Risk of falls for hospitalized patients: a predictive model based on routinely available data. J Clin Epidemiol 2001;54:1258e66. [2] Passaro A, Volpato S, Romagnoni F, Manzoli N, Zuliani G, Fellin R, et al. Benzodiazepines with different half-life and falling in a hospitalized population: the GIFA study. J Clin Epidemiol 2000;53:1222e9. [3] Schwendimann R, De Geest S, Milisen K. Evaluation of the Morse Fall Scale in hospitalised patients. Age Ageing 2006;35:311e3. [4] Schwendimann R, Buhler H, De Geest S, Milisen K. Falls and consequent injuries in hospitalized patients: effects of an interdisciplinary falls prevention program. 2006; http://www.biomedcentral. com/1472-6963/6/69 [5] Fonda D, Cook J, Sandler V, Bailey M. Sustained reduction in serious fall-related injuries in older people in hospital. Med J Aust 2006;184: 379e82. [6] Vassallo M, Vignaraja R, Sharma JC, Briggs R, Allen S. The relationship of falls to injury among hospital in-patients. Int J Clin Pract 2005;59:17e20. [7] Coker E, Oliver D. Evaluation of the STRATIFY falls prediction tool on a geriatric unit. Outcomes Manag 2003;7:8e14. [8] Gowdy M, Godfrey S. Using tools to assess and prevent inpatient falls. Jt Comm J Qual Saf 2003;29:363e8. [9] Dempsey J. Falls prevention revisited: a call for a new approach. J Clin Nurs 2004;13:479e85. [10] Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ 1997;315:1049e53. [11] Chiari P, Mosci D, Fontana S. Evaluation of 2 tools for measuring the risk of falls among patients. Assist Inferm Ric 2002;21:117e24. [12] Wijnia JW, Ooms ME, van Balen R. Validity of the STRATIFY risk score of falls in nursing homes. Prev Med 2006;42:154e7. [13] Vassallo M, Stockdale R, Sharma JC, Briggs R, Allen S. A comparative study of the use of four fall risk assessment tools on acute medical wards. J Am Geriatr Soc 2005;53:1034e8. [14] Smith J, Forster A, Young J. Use of the ‘‘STRATIFY’’ falls risk assessment in patients recovering from acute stroke. Age Ageing 2006;35:138e43. [15] Webster J, Courtney M, O’Rourke P, Marsh N, Gale C, Abbott B, et al. Should elderly patients be screened for their ‘‘falls risk’’? Validity of a falls screening tool and predictors of falls in a large acute hospital. Age Ageing, 2008; doi:10.1093/ageing/afn153. [16] Eagle DJ, Salama S, Whitman D, Evans LA, Ho E, Olde J. Comparison of three instruments in predicting accidental falls in selected inpatients in a general teaching hospital. J Gerontol Nurs 1999;25: 40e5. [17] Myers H, Nikoletti S. Fall risk assessment: a prospective investigation of nurses’ clinical judgement and risk assessment tools in predicting patient falls. Int J Nurs Pract 2003;9:158e65. [18] Vassallo M, Poynter L, Sharma JC, Kwan J, Allen SC. Fall risk-assessment tools compared with clinical judgment: an evaluation in a rehabilitation ward. Age Ageing 2008;37:277e81. [19] Australian Council for Safety and Quality in Health Care. Preventing falls and harm from falls. Best practice guidelines for Australian hospitals and residential aged care facilities. Canberra: Safety & Quality Council; 2005. [20] World Health Organization. Injuries and violence prevention. Available at falls/falls1/en/ Accessed December 1, 2007. [21] Parikh R, Mathai A, Parikh S, Chandra Sekhar G, Thomas R. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol 2008;56:45e50. [22] Hendrie D, Hall SE, Arena G, Legge M. Health system costs of falls of older adults in Western Australia. Aust Health Rev 2004;28: 363e73. [23] Nielsen C, Lang RS. Principles of screening. Med Clin North Am 1999;83:1223e37. [24] Papaioannou A, Parkinson W, Cook R, Ferko N, Coker E, Adachi JD. Prediction of falls using a risk assessment tool in the acute care setting. BMC Med 2004;2:1. [25] Murray CJ, Acharya AK. Understanding DALYs (disability-adjusted life years). J Health Econ 1997;16:703e30. [26] Khanna D, Tsevat J. Health-related quality of lifedan introduction. Am J Manag Care 2007;9:S218e223.
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Additional Notes Available online 23 April 2009

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
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Created: Wed, 14 Oct 2009, 12:44:27 EST by Vicki Percival on behalf of School of Nursing, Midwifery and Social Work