DOI QR코드

DOI QR Code

Predictive Validity of the STRATIFY for Fall Screening Assessment in Acute Hospital Setting: A meta-analysis

입원 환자에서 STRATIFY의 예측 타당도 메타분석

  • 박성희 (순천향대학교 간호학과) ;
  • 최윤경 (한국방송통신대학교 간호학과) ;
  • 황정해 (한양사이버대학교 보건행정학과)
  • Received : 2015.07.27
  • Accepted : 2015.10.06
  • Published : 2015.10.31

Abstract

Purpose: This study is to determine the predictive validity of the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) for inpatients' fall risk. Methods: A literature search was performed to identify all studies published between 1946 and 2014 from periodicals indexed in Ovid Medline, Embase, CINAHL, KoreaMed, NDSL and other databases, using the following key words; 'fall', 'fall risk assessment', 'fall screening', 'mobility scale', and 'risk assessment tool'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Fourteen studies were analyzed using meta-analysis with MetaDisc 1.4. Results: The predictive validity of STRATIFY was as follows; pooled sensitivity .75 (95% CI: 0.72~0.78), pooled specificity .69 (95% CI: 0.69~0.70) respectively. In addition, the pooled sensitivity in the study that targets only the over 65 years of age was .89 (95% CI: 0.85~0.93). Conclusion: The STRATIFY's predictive validity for fall risk is at a moderate level. Although there is a limit to interpret the results for heterogeneity between the literature, STRATIFY is an appropriate tool to apply to hospitalized patients of the elderly at a potential risk of accidental fall in a hospital.

Keywords

References

  1. Shorr RI, Mion LC, Chandler AM, Rosenblatt LC, Lynch D, Kessler LA. Improving the capture of fall events in hospitals: combining a service for evaluating inpatient falls with an incident report system. Journal of American Geriatric Society. 2008 Apr;56(4):701-4. http://dx.doi.org/10.1111/j.1532-5415.2007.01605.x
  2. Oliver D, Papaioannou A, Giangregorio L, Thabane L, Reizgys K, Foster G. A systematic review and meta-analysis of studies using the STRATIFY tool for prediction of falls in hospital patients: how well does it work? Age and Ageing. 2008;37(6):621-7. http://dx.doi.org/10.1093/ageing/afn203
  3. Everhart D, Schumacher JR, Duncan RP, Hall AG, Neff DF, Shorr RI. Determinants of hospital fall rate trajectory groups: a longitudinal assessment of nurse staffing and organizational characteristics. Health Care Management Review. 2014;39(4):352-60. http://dx.doi.org/10.1097/HMR.0000000000000013
  4. Yang HM, Chun BC. Falls in the general hospital inpatients: incidence, associated factors. Journal of Korean Society of Quality Assurance in Health Care. 2009;15(2):107-20.
  5. Healey F, Scobie S, Oliver D, Pryce A, Thomson R, Glampson B. Falls in English and Welsh hospitals: a national observational study based on retrospective analysis of 12 months of patient safety incident reports. Quality & Safety in Health Care. 2008;17(6):424-30. http://dx.doi.org/10.1136/qshc.2007.024695
  6. Wong C, Recktenwald A, Jones M, Waterman B, Bollini M, Dunagen W. The cost of serious fall-related injuries at three midwestern hospitals. The Joint Commission Journal on Quality and Patient Safety. 2011;37(2):81-7. https://doi.org/10.1016/S1553-7250(11)37010-9
  7. Centers for Medicare & Medicaid Services. Medicare program; listening session on hospital-acquired conditions in inpatient settings and hospital outpatient healthcare-associated conditions in outpatient settings [Internet]. Federal Register. 2008; 73(211):64618-9. [cited 2015 March 10]. Available from: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/HospitalAcqCond/downloads/1422_N_FEDERAL_REGISTER_VERSION_PUB_10_30_08_508.pdf
  8. Ganz DA, Huang C, Saliba D, et al. Preventing falls in hospitals: a toolkit for improving quality of care [Internet]. Rockville, MD: Agency for Healthcare Research and Quality, 2013 January. AHRQ Publication No.: 13-0015-EF. [cited 2015 March 10]. Available from: http://www.ahrq.gov/professionals/systems/hospital/fallpxtoolkit/index.html
  9. Korea Ministry of Government Legislation. Patient safety act [Internet]. Sejong Metropolitan Autonomous City: Korea Ministry of Government Legislation; 2015 [cited 2015 March 30]. Available from: http://law.go.kr/lsInfoP.do?lsiSeq=167782&efYd=20160729#0000
  10. Myers H, Nikoletti S. Fall risk assessment: a prospective investigation of nurses' clinical judgement and risk assessment tools in predicting patient falls. International Journal of Nursing Practice. 2003;9(3):158-65. http://dx.doi.org/10.1046/j.1440-172X.2003.00409.x
  11. The Joint Commission. 2015 Comprehensive accreditation manual for hospitals. Oak Brook, IL: Joint Commission Resources;2014.
  12. Ministry of Health & Welfare, Korea Institute for Healthcare Accreditation [KOIHA]. Guideline for healthcare accreditation [Internet]. Seoul: KOIHA. 2014 [cited 2015 January 10]. Available from: https://www.koiha.or.kr/home/data/data/doView.act
  13. The Victorian Quality Council. Minimizing the risk of falls & fall-related injuries: guidelines for acute, sub-acute and residental care setting [Internet]. Melbourn Victoria: the Metropolitan Health and Aged Care Services Division Victorian Government Department of Human Services; 2004 July. [cited 2015 March 10]. Available from: http://www.health.vic.gov.au/qualitycouncil/downloads/falls/research.pdf
  14. Morse JM. Computerized evaluation of a scale to identify the fall-prone patient. Canadian Journal of Public Health. 1986;77(suppl 1):21-5.
  15. Schmid NA. Reducing patient falls: a research-based comprehensive fall prevention program. Military Medicine. 1990;155:202-7. https://doi.org/10.1093/milmed/155.5.202
  16. Hendrich A, Nyhuuis A, Kippenbrock T, Soga ME. Hospital falls: development of a predictive model for clinical practice. Applied Nursing Research. 1995;8(3):129-39. http://dx.doi.org/10.1016/S0897-1897(95)80592-3
  17. Poe SS, Cvach M, Dawson BP, Straus H, Hill, EE. The Johns Hopkins fall risk assessment tool: postimplementation evaluation. Journal of Nursing Care Quality. 2007;22(4):293-8. http://dx.doi.org/10.1097/01.NCQ.0000290408.74027.39
  18. Kim KS, Kim JA, Choi YK, Kim YJ, Park MH, Kim HY, et al. A comparative study on the validity of fall risk assessment scales in Korean hospitals. Asian Nursing Research. 2011;5(1):28-37. http://dx.doi.org/10.1016/S1976-1317(11)60011-X
  19. Oliver D, Daly F, Martin FC, McMurdo ME. Risk factors and risk assessment tools for falls in hospital in-patients: a systematic review. Age and Ageing. 2004;33(2):122-30. http://dx.doi.org/10.1093/ageing/afh017
  20. The Joint Commission. Defining the problem of falls. In I.J. Smith (Ed.), Reducing the risk of falls in your health care organization. Oak Brook, IL: Joint Commission Resources;2012.
  21. Macaskill P, Gatsonis C, Deeks JJ, Harbord RM, Takwoingi Y. Analysing and presenting results. In: Deeks JJ, Bossuyt PM, Gatsonis C, editors. Cochrane handbook for systematic reviews of diagnostic test accuracy version 1.0 [Internet]. The Cochrane Collaboration, 2010 [cited 2013 May 30]. Available from: http://srdta.cochrane.org/
  22. Moher D, Liberati A, Tetzlaff J, Altman DG, Antes G, Atkins D, et al. Preferred reporting items for systematic reviews and meta-Analyses: The PRISMA statement. Annals of Internal Medicine. 2009;151(4):264-9. http://dx.doi.org/10.7326/0003-4819-151-4-200908180-00135
  23. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine. 2011;155(8):529-36. http://dx.doi.org/10.7326/0003-4819-155-8-201110180-00009
  24. Zamora J, Abraira V, Muriel A, Khan KS, Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. Medical Research Methodology. 2006;6:31-42. http://dx.doi.org/10.1186/1471-2288-6-31
  25. Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine. 2000;45(1-2): 23-41. https://doi.org/10.1016/S0167-5877(00)00115-X
  26. Walter SD. Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Statistics in Medicine. 2002;21(9):1237-56. http://dx.doi.org/10.1002/sim.1099
  27. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine. 2002;21(11):1539-58. http://dx.doi.org/10.1002/sim.1186
  28. Knottnerus JA. The evidence base of clinical diagnosis. Park SH, Kang CB, translator. Seoul: E-Public; 2008.
  29. Sousa MR, Ribeiro AL. Systematic review and meta-analysis of diagnostic and prognostic studies: a tutorial. Arquivos Brasileiros de Cardiologia. 2009;92(3):229-38. http://dx.doi.org/10.1590/S0066-782X2009000300013
  30. Hill K, Vrantsidis F, Jessup R, McGann A, Pearce J, Collins T. Design-related bias in hospital falls risk screening tools predictive accuracy evaluations: systematic review and meta-analysis. Australasian Journal of Pordiatric Medicine. 2004;38(2): 99-108.

Cited by

  1. Systematic Review and Meta-analysis for Usefulness of Fall Risk Assessment Tools in Adult Inpatients vol.16, pp.3, 2016, https://doi.org/10.15384/kjhp.2016.16.3.180
  2. Trends of Nursing Research on Accidental Falls: A Topic Modeling Analysis vol.18, pp.8, 2015, https://doi.org/10.3390/ijerph18083963