Subtype-specific optimal cut-off values of the Berg Balance Scale for predicting independent walking in inpatient stroke rehabilitation: a multicentre cohort study
DOI:
https://doi.org/10.2340/jrm.v58.45663Keywords:
balance, Berg Balance Scale, gait, prediction, rehabilitation, strokeAbstract
Objective: To determine whether admission Berg Balance Scale score independently predicts independent walking on discharge after adjustment for major confounders, and to derive subtype-specific optimal cut-off values for ischaemic and haemorrhagic stroke.
Design: Multicentre retrospective cohort study.
Subjects/Patients: A total of 565 stroke patients (316 ischaemic, 249 haemorrhagic) admitted to 3 inpatient rehabilitation centres in the Republic of Korea.
Methods: Multivariable logistic regression was used to evaluate the independent predictive value of the Berg Balance Scale. Optimal cut-off values were derived using receiver operating characteristic curve analysis and the Youden index. Bootstrap internal validation, calibration analysis, and decision curve analysis were performed.
Results: Admission Berg Balance Scale was a significant independent predictor of independent walking (adjusted odds ratio 1.053, 95% confidence interval 1.030–1.076). The difference in discriminative ability between the Berg Balance Scale only and multivariable models was not statistically significant (p = 0.097). The overall optimal cut-off was 24 points; subtype-specific cut-offs were 33 for ischaemic and 12 for haemorrhagic stroke.
Conclusion: The Berg Balance Scale has different optimal cut-off values by stroke subtype and, as a standalone assessment, maintains discriminative ability equivalent to a multivariable model, providing clinical evidence for subtype-specific precision rehabilitation strategies.
Downloads
References
Feigin VL, Owolabi MO, World Stroke Organization–Lancet Neurology Commission Stroke Collaboration Group. World Stroke Organization (WSO): Global Stroke Fact Sheet 2025. Int J Stroke 2025. DOI: https://doi.org/10.1177/17474930241308142
Jørgensen HS, Nakayama H, Raaschou HO, Olsen TS. Recovery of walking function in stroke patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil 1995; 76: 27–32. DOI: https://doi.org/10.1016/S0003-9993(95)80038-7
Bohannon RW, Andrews AW, Smith MB. Rehabilitation goals of patients with hemiplegia. Int J Rehabil Res 1988; 11: 181–184. DOI: https://doi.org/10.1097/00004356-198806000-00012
Kwakkel G, Wagenaar RC, Kollen BJ, Lankhorst GJ. Predicting disability in stroke: a critical review of the literature. Age Ageing 1996; 25: 479–489. DOI: https://doi.org/10.1093/ageing/25.6.479
Blum L, Korner-Bitensky N. Usefulness of the Berg Balance Scale in stroke rehabilitation: a systematic review. Phys Ther 2008; 88: 559–566. DOI: https://doi.org/10.2522/ptj.20070205
Mao HF, Hsueh IP, Tang PF, Sheu CF, Hsieh CL. Analysis and comparison of the psychometric properties of three balance measures for stroke patients. Stroke 2002; 33: 1022–1027. DOI: https://doi.org/10.1161/01.STR.0000012516.63191.C5
Louie DR, Eng JJ. Berg Balance Scale score at admission can predict walking suitable for community ambulation at discharge from inpatient stroke rehabilitation. J Rehabil Med 2018; 50: 37–44. DOI: https://doi.org/10.2340/16501977-2280
Veerbeek JM, Kwakkel G, van Wegen EE, Ket JC, Heymans MW. Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke 2011; 42: 1482–1488. DOI: https://doi.org/10.1161/STROKEAHA.110.604090
Veerbeek JM, Van Wegen EE, Harmeling-Van der Wel BC, Kwakkel G; EPOS Investigators. Is accurate prediction of gait in nonambulatory stroke patients possible within 72 hours poststroke? The EPOS study. Neurorehabil Neural Repair 2011; 25: 268–274. DOI: https://doi.org/10.1177/1545968310384271
Wouda NC, Knijff B, Punt M, Visser-Meily JMA, Pisters MF. Predicting recovery of independent walking after stroke: a systematic review. Am J Phys Med Rehabil 2024; 103: 458–464. DOI: https://doi.org/10.1097/PHM.0000000000002436
Stinear CM, Barber PA, Petoe M, Anber S, Byblow WD. Prediction of motor recovery after stroke: advances in biomarkers. Lancet Neurol 2017; 16: 826–836. DOI: https://doi.org/10.1016/S1474-4422(17)30283-1
Kitago T, Ratan RR. Rehabilitation following hemorrhagic stroke: building the case for stroke-subtype specific recovery therapies. F1000Research 2017; 6: 2044. DOI: https://doi.org/10.12688/f1000research.11913.1
Paolucci S, Antonucci G, Grasso MG, Bragoni M, Coiro P, De Angelis D, et al. Functional outcome of ischemic and hemorrhagic stroke patients after inpatient rehabilitation: a matched comparison. Stroke 2003; 34: 2861–2865. DOI: https://doi.org/10.1161/01.STR.0000102902.39759.D3
Kelly PJ, Furie KL, Shafqat S, Rallis N, Chang Y, Stein J. Functional recovery following rehabilitation after hemorrhagic and ischemic stroke. Arch Phys Med Rehabil 2003; 84: 968–972. DOI: https://doi.org/10.1016/S0003-9993(03)00040-6
Chu CL, Chen YP, Chen CCP, Chen CK, Chang HN, Chang CH, et al. Functional recovery patterns of hemorrhagic and ischemic stroke patients under post-acute care rehabilitation program. Neuropsychiatr Dis Treat 2020; 16: 1975–1985. DOI: https://doi.org/10.2147/NDT.S253700
Perna R, Temple J. Rehabilitation outcomes: ischemic versus hemorrhagic strokes. Behav Neurol 2015; 2015: 891651. DOI: https://doi.org/10.1155/2015/891651
von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Lancet 2007; 370: 1453–1457. DOI: https://doi.org/10.1016/S0140-6736(07)61602-X
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015; 162: 55–63. DOI: https://doi.org/10.7326/M14-0697
Berg KO, Wood-Dauphinee SL, Williams JI, Maki B. Measuring balance in the elderly: validation of an instrument. Can J Public Health 1992; 83 Suppl 2: S7–11.
Preston E, Ada L, Stanton R, Mahendran N, Dean CM. Prediction of independent walking in people who are nonambulatory early after stroke: a systematic review. Stroke 2021; 52: 3217–3224. DOI: https://doi.org/10.1161/STROKEAHA.120.032345
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189–198. DOI: https://doi.org/10.1016/0022-3956(75)90026-6
Shah S, Vanclay F, Cooper B. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J Clin Epidemiol 1989; 42: 703–709. DOI: https://doi.org/10.1016/0895-4356(89)90065-6
Holden MK, Gill KM, Magliozzi MR, Nathan J, Piehl-Baker L. Clinical gait assessment in the neurologically impaired: reliability and meaningfulness. Phys Ther 1984; 64: 35–40. DOI: https://doi.org/10.1093/ptj/64.1.35
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837–845. DOI: https://doi.org/10.2307/2531595
Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001; 54: 774–781. DOI: https://doi.org/10.1016/S0895-4356(01)00341-9
Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken, NJ: Wiley; 2013. DOI: https://doi.org/10.1002/9781118548387
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26: 565–574. DOI: https://doi.org/10.1177/0272989X06295361
Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res 2019; 3: 18. DOI: https://doi.org/10.1186/s41512-019-0064-7
Liao WL, Chang CW, Sung PY, Hsu WN, Lai MW, Tsai SW. The Berg Balance Scale at admission can predict community ambulation at discharge in patients with stroke. Medicina 2021; 57: 556. DOI: https://doi.org/10.3390/medicina57060556
Makizako H, Kabe N, Takano A, Isobe K. Use of the Berg Balance Scale to predict independent gait after stroke: a study of an inpatient population in Japan. PM R 2015; 7: 471–478. DOI: https://doi.org/10.1016/j.pmrj.2015.01.009
Jenkin J, Parkinson S, Jacques A, Kho L, Hill K. Berg Balance Scale score as a predictor of independent walking at discharge among adult stroke survivors. Physiother Can 2021; 73: 252–256. DOI: https://doi.org/10.3138/ptc-2019-0090
Mehrholz J, Wagner K, Rutte K, Meissner D, Pohl M. Predictive validity and responsiveness of the functional ambulation category in hemiparetic patients after stroke. Arch Phys Med Rehabil 2007; 88: 1314–1319. DOI: https://doi.org/10.1016/j.apmr.2007.06.764
Tyson SF, Connell LA. How to measure balance in clinical practice: a systematic review of the psychometrics and clinical utility of measures of balance activity for neurological conditions. Clin Rehabil 2009; 23: 824–840. DOI: https://doi.org/10.1177/0269215509335018
Downloads
Additional Files
Published
How to Cite
License
Copyright (c) 2026 Jeong-Soo Kim, In-Geon Hwang, Eu-Jeong Ko

This work is licensed under a Creative Commons Attribution 4.0 International License.
All digitalized JRM contents is available freely online. The Foundation for Rehabilitation Medicine owns the copyright for all material published until volume 40 (2008), as from volume 41 (2009) authors retain copyright to their work and as from volume 49 (2017) the journal has been published Open Access, under CC-BY-NC licences (unless otherwise specified). The CC-BY-NC licenses allow third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for non-commercial purposes, provided proper attribution to the original work.
From 2024, articles are published under the CC-BY licence. This license permits sharing, adapting, and using the material for any purpose, including commercial use, with the condition of providing full attribution to the original publication.