Literature DB >> 18829693

A systematic review and meta-analysis of studies using the STRATIFY tool for prediction of falls in hospital patients: how well does it work?

David Oliver1, Alexandra Papaioannou, Lora Giangregorio, Lehana Thabane, Katerina Reizgys, Gary Foster.   

Abstract

BACKGROUND: STRATIFY is a prediction tool developed for use in for hospital inpatients, using a 0-5 score to predict patients who will fall. It has been widely used as part of hospital fall prevention plans, but it is not clear how good its operational utility is in a variety of settings.
OBJECTIVES: (i) to describe the predictive validity of STRATIFY for identifying hospital inpatients who will fall via systematic review and descriptive analysis, based on its use in several prospective cohort studies of hospital inpatients; (ii) to describe the predictive validity of STRATIFY among inpatients in geriatric rehabilitation via meta-analysis and (iii) in turn, to help practitioners and institutions wishing to implement interventions to prevent in-hospital falls.
METHODS: a systematic literature review of prospective validation studies of STRATIFY for falls prediction in hospital inpatients. For inclusion, studies must report prospective validation cohorts, with sufficient data for calculation of sensitivity (SENS), specificity (SPEC), negative and positive predictive value (NPV and PPV), total predictive accuracy (TPA) and 95% confidence intervals (CI). We performed meta-analysis using precision-weighted fixed- and random-effects models using studies that evaluated STRATIFY among geriatric rehabilitation inpatients. MEASUREMENTS: key features of the patient population, setting, study design and numbers of falls/fallers were abstracted. SENS, SPEC, PPV, NPV, TPA and 95% CI were reported for each cohort. Pooled values and chi-squared test for homogeneity were reported for a meta-analysis of studies conducted in geriatric rehabilitation settings.
RESULTS: forty-one papers were identified by the search, with eight ultimately eligible for inclusion in the systematic review and four for inclusion in the meta-analysis. The predictive validity of STRATIFY, using a random-effects model, for the four studies involving geriatric patients was as follows: SENS 67.2 (95% CI 60.8, 73.6), SPEC 51.2 (95% CI 43.0, 59.3), PPV 23.1 (95% CI 14.9, 31.2), NPV 86.5 (95% CI 78.4, 94.6). The Q((3)) test for homogeneity was not significant for SENS at P = 0.36, but it was significant at P < 0.01 for SPEC, PPV and NPV. TPA across all four studies varied from 43.2 to 60.0.
CONCLUSION: the current study reveals a relatively high NPV and low PPV and TPA for the STRATIFY instrument, suggesting that it may not be optimal for identifying high-risk individuals for fall prevention. Further, the study demonstrates that population and setting affect STRATIFY performance.

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Year:  2008        PMID: 18829693      PMCID: PMC5104555          DOI: 10.1093/ageing/afn203

Source DB:  PubMed          Journal:  Age Ageing        ISSN: 0002-0729            Impact factor:   10.668


  17 in total

Review 1.  Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement. Quality of Reporting of Meta-analyses.

Authors:  D Moher; D J Cook; S Eastwood; I Olkin; D Rennie; D F Stroup
Journal:  Lancet       Date:  1999-11-27       Impact factor: 79.321

Review 2.  Fall risk assessment measures: an analytic review.

Authors:  K L Perell; A Nelson; R L Goldman; S L Luther; N Prieto-Lewis; L Z Rubenstein
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2001-12       Impact factor: 6.053

Review 3.  Hospital fall risk assessment tools: a critique of the literature.

Authors:  Helen Myers
Journal:  Int J Nurs Pract       Date:  2003-08       Impact factor: 2.066

4.  Use of the 'STRATIFY' falls risk assessment in patients recovering from acute stroke.

Authors:  Jane Smith; Anne Forster; John Young
Journal:  Age Ageing       Date:  2005-12-20       Impact factor: 10.668

Review 5.  Falls risk-prediction tools for hospital inpatients. Time to put them to bed?

Authors:  David Oliver
Journal:  Age Ageing       Date:  2008-05       Impact factor: 10.668

6.  A comparative study of the use of four fall risk assessment tools on acute medical wards.

Authors:  Michael Vassallo; Rachel Stockdale; Jagdish C Sharma; Roger Briggs; Stephen Allen
Journal:  J Am Geriatr Soc       Date:  2005-06       Impact factor: 5.562

7.  Evaluation of the STRATIFY falls prediction tool on a geriatric unit.

Authors:  Esther Coker; David Oliver
Journal:  Outcomes Manag       Date:  2003 Jan-Mar

8.  Fall prediction in inpatients by bedside nurses using the St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) instrument: a multicenter study.

Authors:  Koen Milisen; Nele Staelens; René Schwendimann; Leen De Paepe; Jeroen Verhaeghe; Tom Braes; Steven Boonen; Walter Pelemans; Reto W Kressig; Eddy Dejaeger
Journal:  J Am Geriatr Soc       Date:  2007-05       Impact factor: 5.562

9.  Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies.

Authors:  D Oliver; M Britton; P Seed; F C Martin; A H Hopper
Journal:  BMJ       Date:  1997-10-25

Review 10.  Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative.

Authors:  Patrick M Bossuyt; Johannes B Reitsma; David E Bruns; Constantine A Gatsonis; Paul P Glasziou; Les M Irwig; Jeroen G Lijmer; David Moher; Drummond Rennie; Henrica C W De Vet
Journal:  AJR Am J Roentgenol       Date:  2003-07       Impact factor: 3.959

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  26 in total

1.  Bayesian networks: a new method for the modeling of bibliographic knowledge: application to fall risk assessment in geriatric patients.

Authors:  Laure Lalande; Laurent Bourguignon; Chloé Carlier; Michel Ducher
Journal:  Med Biol Eng Comput       Date:  2013-01-20       Impact factor: 2.602

Review 2.  Assessment and management of falls in older people.

Authors:  Emily Kwan; Sharon E Straus
Journal:  CMAJ       Date:  2014-06-30       Impact factor: 8.262

3.  Subclinical Peroneal Neuropathy: A Common, Unrecognized, and Preventable Finding Associated With a Recent History of Falling in Hospitalized Patients.

Authors:  Louis H Poppler; Andrew P Groves; Gina Sacks; Anchal Bansal; Kristen M Davidge; Jenifer A Sledge; Heidi Tymkew; Yan Yan; Jessica M Hasak; Patricia Potter; Susan E Mackinnon
Journal:  Ann Fam Med       Date:  2016-11       Impact factor: 5.166

4.  In-hospital fall-risk screening in 4,735 geriatric patients from the LUCAS project.

Authors:  L Neumann; V S Hoffmann; S Golgert; J Hasford; W Von Renteln-Kruse
Journal:  J Nutr Health Aging       Date:  2013-03       Impact factor: 4.075

5.  World Health Organization fracture risk assessment tool in the assessment of fractures after falls in hospital.

Authors:  Shin-ichi Toyabe
Journal:  BMC Health Serv Res       Date:  2010-04-27       Impact factor: 2.655

Review 6.  Diagnostic accuracy of the STRATIFY clinical prediction rule for falls: a systematic review and meta-analysis.

Authors:  Jennifer Billington; Tom Fahey; Rose Galvin
Journal:  BMC Fam Pract       Date:  2012-08-07       Impact factor: 2.497

7.  Analyzing the problem of falls among older people.

Authors:  Yannis Dionyssiotis
Journal:  Int J Gen Med       Date:  2012-09-28

Review 8.  Instruments for assessing the risk of falls in acute hospitalized patients: a systematic review and meta-analysis.

Authors:  Marta Aranda-Gallardo; Jose M Morales-Asencio; Jose C Canca-Sanchez; Silvia Barrero-Sojo; Claudia Perez-Jimenez; Angeles Morales-Fernandez; Margarita Enriquez de Luna-Rodriguez; Ana B Moya-Suarez; Ana M Mora-Banderas
Journal:  BMC Health Serv Res       Date:  2013-04-02       Impact factor: 2.655

Review 9.  Hospital fall prevention: a systematic review of implementation, components, adherence, and effectiveness.

Authors:  Susanne Hempel; Sydne Newberry; Zhen Wang; Marika Booth; Roberta Shanman; Breanne Johnsen; Victoria Shier; Debra Saliba; William D Spector; David A Ganz
Journal:  J Am Geriatr Soc       Date:  2013-03-25       Impact factor: 5.562

10.  Detecting inpatient falls by using natural language processing of electronic medical records.

Authors:  Shin-ichi Toyabe
Journal:  BMC Health Serv Res       Date:  2012-12-05       Impact factor: 2.655

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