De Geest, S.

Milisen, K.

Schwendimann, R.

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Research letters311Evaluation of the Morse Fall Scale in hospitalised patientsSir—Several risk factors associated with falls in hospitalisedpatients have been identified [1, 2]. Although a substantialnumber of assessment instruments for identifying hospital-ised patients at risk of falling exists [3], their generalisabilityis limited [4] because only a few [2, 5] have been tested insettings other than those in which they were originallydeveloped. The Morse Fall Scale (MFS) has been evaluatedin different hospital settings [6–9] and has been used in avariety of patient populations [10–16]. In search of anappropriate tool to identify admitting patients for risk offalling, the MFS appears to be most elaborate in view of itsextensive development and testing in different hospitalpopulations compared with others [3, 4]. Its easy applicationin clinical practice supported this choice.However, no investigation to date has reported resultsof different cut-off scores of the scale. This study aimed toevaluate the diagnostic value of different MFS cut-offs todetermine which score would be most useful in identifyingin-hospital patients at risk of falls.MethodsThis prospective cohort study utilised baseline data col-lected during a four-month falls intervention study per-formed at two units in the department of internal medicineof a 300-bed urban public hospital in Switzerland. The datawere collected on consecutively admitted adult patients(18 years and older, >48 hours in hospital) who presentedwith a wide range of medical conditions.Because the study hospital is situated in the German-speaking part of Switzerland, the MFS was translated intoGerman (MFS-G) and piloted with six registered nurses todetermine their understanding of wording of items. Inter-rater reliability was examined and the level of agreement was84% (K = 0.68). The scale consists of six items reflectingrisk factors of falling such as: (i) history of falling, (ii) sec-ondary diagnosis, (iii) ambulatory aids, (iv) intravenoustherapy, (v) type of gait and (vi) mental status. The totalscore ranges between 0 and 125 [17, 18]. For further detailsof the scale, please see Appendix 1 in the supplementarydata on the journal website (http://www.ageing.oxford-journals. org/).All registered nurses on the designated study unitsreceived a 30-minute group instruction on the use of theMFS-G as part of the implementation of the in-hospital fallrisk screening programme. The primary nurses completedthe MFS-G for each newly hospitalised patient within24 hours of admission. Patient falls during hospitalisationwere registered with a standardised fall incident report formthat had been implemented earlier in this hospital [19].A fall was defined as ‘an incident in which a patient sud-denly and involuntarily came to rest upon the ground or sur-face’ [20]. Patient demographics and clinical characteristics(i.e. gender, age, length of stay and medical diagnosis) wereextracted from the hospital administrative patient data base.The study was approved by the local ethics committee.Descriptive statistics such as frequencies, per cent as well asmean and standard deviations were calculated for demo-graphic and clinical characteristics of the patients.The diagnostic value of the MFS-G scores ranging from 20to 70 was explored using receiver operating characteristic(ROC) curves, with an area under the curve (AUC) analysisbased on admission MFS-G scores, and using patients who fellwhile hospitalised as the ‘gold standard’. Sensitivity analysis—including specificity, positive predictive value (PPV) and nega-tive predictive value (NPV) and accuracy—was performed forthe different cut-off scores of the MFS-G. Chi square statisticswere calculated for the estimation of risk of falling with oddratios and 95% confidence intervals. All data were analysedwith SPSS for Windows, version 12.0 (SPSS Inc., Chicago, IL).ResultsA total of 386 patients (female: 59.6%) with a mean age of70.3 (SD: 18.5) years, and a mean length of stay of 11.3 (SD:8.9) days were included in the study. Forty-seven (12.2%)patients experienced a total of 69 falls. For patient demo-graphics, clinical characteristics including primary medicaldiagnosis and risk factors for falls (MFS-G items), please seeAppendix 2 in the supplementary data on the journal web-site (http://www.ageing.oxfordjournals. org/).The percentage of the patients identified as at risk of fall-ing at admission varied with the MFS-G cut-off scores usedand ranged from 89.4% (cut-off score: 20 points) to 20.7%(cut-off score: 70 points). According to the different cut-offscores, the sensitivity ranged from 91.5 to 38.3%, the specif-icity from 81.7 to 10.9%, the PPVs from 12.5 to 22.5% andthe NPVs from 90.2 to 95.7% (Table 1). High false positiverates (i.e. patients who were classified as at risk of falling butdid not fall) ranging from 87.5% (cut-off score: 20 points)to 75.9% (cut-off score: 60 points) were observed.The area under the ROC curve ranged from 0.512 to0.701, and the accuracy of the MFS-G ranged from 20.7 to76.4% (Table 1). The most optimal cut-off point for theMFS-G was found to be 55, which showed a fairly goodsensitivity of 74.5% (95% CI = 60.5–84.7%), an acceptablespecificity of 65.8% (95% CI = 60.1–70.6%) and a highNPV (94.9%), with an acceptable accuracy of 66.8%. TheROC curve with an arrow indicating the highest peak withthe cut-off of 55 points for the MFS-G is demonstrated inFigure 1. With a cut-off score of 55 points, 23.2% of thepatients were screened positive and presented a relativeodds ratio of 5.6 (95% CI = 2.8–11.2) for falling.DiscussionThis study constitutes a prospective test of the sensitivity,specificity and predictive value of the MFS-G in hospitalisedpatients. The 12.2% proportion of patients who fell in thepresent study lies between rates reported in previous studiesof 15.7, 29.6 [6, 9], 5 and 4% [7, 21]. The variation in fall ratesmay reflect the different types of settings, sample sizes,patient characteristics and reporting practices. The MFS-Gdemonstrated moderate ability to predict patients’ risk of fall-ing using a cut-off score of 55 points as evidenced by an AUCof 0.701 in a sample of internal medicine patients.Research letters 312Using the originally identified cut-off score of 45 points,only 26% patients in another study [21] were identified asbeing at risk of falling, whereas the same cut-off score iden-tified 51% patients as being at risk of falling in the presentstudy. This difference may be explained by the heterogene-ity of the other sample, with patients enrolled from acute,rehabilitation and long-term care units, whereas the presentstudy may reflect a more homogenous sample in relationto fall risk factors despite a variety of medical diagnoses.Additionally, in the original prospective study [21], the fallrisk status of the patients was assessed at different points oftime during their hospital stay, whereas in the present studyall patients were screened for risk of falling at admission.This and the prospective follow-up during the patient’s hos-pital stay allowed calculation of the diagnostic value of theMFS-G in relation to its predictive power.Only one other study [9] scored patients at admission andperformed ROC analysis. In that study, an MFS cut-offscore of 45 points identified 75% of the patients as at risk offalling with a false positive rate of 82%. The same cut-off inthe present study resulted in a false positive rate of 81%, butdecreased slightly to 77% with a cut-off of 55 points. O’Con-nell and Myers [9] concluded, based on an AUC of 0.621,that the MFS had low ability to discriminate patients who felland those who did not fall. However, the high positive ratemay reflect a limitation in the present study because theeffects of fall interventions subsequently implemented withsome of the patients identified as being at risk of falling werenot considered. Furthermore, the performance of falls incid-ent reporting may be inflated by virtue of the study beingconducted (Hawthorne effect). Finally, changes in thepatient’s health condition which may have altered risk fac-tors for falls were not considered. Although the high NPVs(e.g. 95% of the non-falling patients were not at risk of fall-ing) may give appropriate reassurance for patients with lowrisk of falling, the scale seems to be of limited operationalvalue because the PPV is only between 12 and 24%. Wetherefore recommend that the MFS undergo local validationto determine the best cut-off score for a given setting beforeit is used clinically. Screening patients for risk of fallingshould lead to more targeted assessment and modification ofrisk factors using multifactorial interventions [22, 23]. How-ever, because the effectiveness of hospital fall preventionprogrammes that incorporate fall risk assessment leads to a25% or less reduction in fall rates [24], the idea of looking atreversible risk factors in all patients and reassessing their riskfollowing a fall may be an appropriate approach [2].Key point• The MFS should be used to screen hospitalised patientsat risk of falling only after local validation to determinebest cut-off scores in a given setting.AcknowledgementThe authors thank Prof. Kathy Dracup and Prof. SandieEngberg for editing the manuscript.Conflicts of interestThe authors have no conflicts of interest to declare.R. SCHWENDIMANN1,2*, S. DE GEEST1,3 K. MILISEN31Institute of Nursing Science, University of Basel,Basel, SwitzerlandTable 1. Predictive validity of MFS-G cut-off scores at admission (n = 386)aPositive predictive valuebNegative predictive valuecArea under the ROC curveCut-off scores 20 25 35 45 50 55 60 65 70. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Sensitivity 91.5% 91.5% 91.5% 80.9% 80.9% 74.5% 68.1% 44.7% 38.3%Specificity 10.9% 13.9% 16.5% 53.4% 58.7% 65.8% 70.2% 79.4% 81.7%PPVa 12.5% 12.8% 13.2% 19.4% 21.3% 23.2% 24.1% 23.1% 22.5%NPVb 90.2% 92.2% 93.3% 95.3% 95.7% 94.9% 94.1% 91.2% 90.5%Accuracy 20.7% 23.3% 25.6% 56.7% 61.4% 66.8% 69.9% 75.1% 76.4%AUCc 0.512 0.527 0.540 0.671 0.698 0.701 0.691 0.620 0.600Figure 1. ROC curve with AUC of the MFS-G (n = 386). Thearrow indicates the highest peak with the cut-off of 55 points.0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.00.10.20.30.40.50.60.70.80.91.0Sensitivity1 - SpecificityResearch letters3132Stadtspital Waid, Zurich, Switzerland3Center for Health Services and Nursing Research, CatholicUniversity of Leuven, Leuven, Belgium*To whom correspondence should be addressed:rene.schwendimann@unibas.chReferences1. Evans D, Hodgkinson B, Lambert L, Wood J. Falls risk fac-tors in the hospital setting: a systematic review. Int J Nurs Prac2001; 7 (1): 38–45.2. Oliver D, Daly F, Martin FC, McMurdo ME. Risk factors andrisk assessment tools for falls in hospital in-patients: a system-atic review. Age Ageing 2004; 33 (2): 122–30.3. Perell KL, Nelson A, Goldman RL, Luther SL, Prieto-Lewis N,Rubenstein LZ. Fall risk assessment measures: an analyticreview. J Gerontol 2001; 56 (12): M761–6.4. Myers H. Hospital fall risk assessment tools: a critique of theliterature. Int J Nurs Prac 2003; 9 (4): 223–35.5. Morse JM. Preventing Patient Falls, 1st edition. ThousandOaks, California: SAGE Publications, Inc., 1997.6. Eagle DJ, Salama S, Whitman D, Evans LA, Ho E, Olde J.Comparison of three instruments in predicting accidental fallsin selected inpatients in a general teaching hospital. J GerontolNurs 1999; 25 (7): 40–5.7. McCollam ME. Evaluation and implementation of a research-based falls assessment innovation. Nurs Clin North Am 1995;30 (3): 507–14.8. McFarlane-Kolb H. Falls risk assessment, multitargeted inter-ventions and the impact on hospital falls. Int J Nurs Prac2004; 10 (5): 199–206.9. O’Connell B, Myers H. The sensitivity and specificity of theMorse Fall Scale in an acute care setting. J Clin Nurs 2002; 11(1): 134–6.10. Barnett K. Reducing patient falls in an acute general hos-pital. Foundation of Nursing Studies Dissemination Series2002; 1 (1).11. Camicioli R, Licis L. Motor impairment predicts falls in spe-cialized Alzheimer care units. Alzheimer Dis Assoc Disord2004; 18 (4): 214–18.12. Cheng GYC, S. Fall prevention. In: 3rd Australasian JoannaBriggs Institute Colloquium for Evidence Based Nursing andMidwifery: 2002; Auckland, New Zealand: ContemporaryNurse, 2002: 61.13. Lai KCKYS, Wong KST. Validation of the Cantonese versionof the Morse Fall Scale. In: 11 Annual Congress of the HongKong Association of Gerontology: 2003, Hong Kong, 2003.14. Ledsham RBJ, Beardsall A. Implementing a fall risk assess-ment strategy for older people: issues and outcomes. ClinGovern Bull 2002; 3 (3): 2–4.15. McCarter-Bayer A, Bayer F, Hall K. Preventing falls in acute care:an innovative approach. J Gerontol Nurs 2005; 31 (3): 25–33.16. Weber H. Pflegeexpertinnen helfen Stürze verhindern. In:Kantonsspital Luzern Newsletter, 2003: 3.17. Morse JM. Computerized evaluation of a scale to identify thefall-prone patient. Can J Public Health 1986; 77 (Suppl. 1): 21–5.18. Morse JM, Prowse MD, Morrow N, Federspeil G. A retro-spective analysis of patient falls. Can J Public Health 1985; 76(2): 116–18.19. Schwendimann R. [Frequency and circumstances of falls inacute care hospitals: a pilot study]. Pflege 1998; 11 (6): 335–41.20. Gibson MA, RO, Isaacs B, Radebaugh T, Worm-Petersen J.The prevention of falls in later life. A report of the KelloggInternational Work Group on the Prevention of Falls by theElderly. Dan Med Bull 1987; 34 (Suppl. 4): 1–24.21. Morse JM, Black C, Oberle K, Donahue P. A prospectivestudy to identify the fall-prone patient. Soc Sci Med 1989; 28(1): 81–6.22. Haines TP, Bennell KL, Osborne RH, Hill KD. Effective-ness of targeted falls prevention programme in subacutehospital setting: randomised controlled trial. Br Med J2004; 328 (7441): 676.23. Healey F, Monro A, Cockram A, Adams V, Heseltine D.Using targeted risk factor reduction to prevent falls in older in-patients: a randomised controlled trial. Age Ageing 2004;33 (4): 390–5.24. Oliver D, Hopper A, Seed P. Do hospital fall prevention pro-grams work? A systematic review. J Am Geriatr Soc 2000;48 (12): 1679–89.doi:10.1093/ageing/afj066Published electronically 9 March 2006NHS continuing care: reliable decisions?SIR—Decisions denying free National Health Service(NHS) continuing health care have been reversed by theHealth Service Ombudsman from 1994 [1]. From 1996,health authorities published their individual criteria govern-ing eligibility for continuing NHS health care [2]. Concernthat criteria were too restrictive led to further guidance [3].The ‘Coughlan case’ [4] and others reviewed by theOmbudsman [5] revealed people who had been wronglydenied NHS care. Restitution followed, reimbursing indi-viduals or their estates if care had been wrongly denied.Currently, Primary Care Trusts (PCTs) convene panelsof senior staff (doctor, nurse, therapist, social worker) todetermine eligibility using their Strategic Health Authority(StHA) criteria. Rejected applicants can appeal and anotherpanel may reverse the decision.The appeals system, however, does not indicate the levelof inconsistency amongst panels. To our knowledge, thispoint had not been explored previously by presenting thesame case to panels applying identical criteria. Here wedescribe a small audit undertaken during 2004 with the sup-port of the Continuing Care Steering Group of the Norfolk,Suffolk and Cambridgeshire StHA.Audit standardThe standard was that StHA panels should reach consistentdecisions when determining eligibility for continuing NHShealth care.MethodThe authors and the StHA designated officer for continuingcare examined 110 completed restitution cases and selected10 to reflect the range of conditions frequently giving rise toapplications. The conditions were chronic and usually pro-gressive. The individuals were likely to need frequent atten-tion because of unpredictable health need, or managementof challenging behaviour, characteristics often demanded bythe eligibility criteria. The aetiologies were dementia, stroke

Evaluation of the Morse Fall Scale in hospitalised patients

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