Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model

Objectives To develop and internally validate prediction models to assess treatment success of both stand-alone and blended online vestibular rehabilitation (VR) in patients with chronic vestibular syndrome.Design Secondary analysis of a randomised controlled trial.Setting 59 general practices in Th...

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Main Authors: Henriëtte E van der Horst, Vincent A van Vugt, Otto R Maarsingh
Format: Article
Language:English
Published: BMJ Publishing Group 2020-10-01
Series:BMJ Open
Online Access:https://bmjopen.bmj.com/content/10/10/e038649.full
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spelling doaj-18cf741e116442a88fcaaf9d1049bf2d2021-05-06T09:34:20ZengBMJ Publishing GroupBMJ Open2044-60552020-10-01101010.1136/bmjopen-2020-038649Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction modelHenriëtte E van der Horst0Vincent A van Vugt1Otto R Maarsingh2Department of General Practice, Amsterdam UMC - Location VUMC, Amsterdam, The NetherlandsDepartment of General Practice, Amsterdam UMC - Location VUMC, Amsterdam, The NetherlandsDepartment of General Practice, Amsterdam UMC - Location VUMC, Amsterdam, The NetherlandsObjectives To develop and internally validate prediction models to assess treatment success of both stand-alone and blended online vestibular rehabilitation (VR) in patients with chronic vestibular syndrome.Design Secondary analysis of a randomised controlled trial.Setting 59 general practices in The Netherlands.Participants 202 adults, aged 50 years and older with a chronic vestibular syndrome who received either stand-alone VR (98) or blended VR (104). Stand-alone VR consisted of a 6-week, internet-based intervention with weekly online sessions and daily exercises. In blended VR, the same intervention was supplemented with physiotherapy support.Main outcome measures Successful treatment was defined as: clinically relevant improvement of (1) vestibular symptoms (≥3 points improvement Vertigo Symptom Scale—Short Form); (2) vestibular-related disability (>11 points improvement Dizziness Handicap Inventory); and (3) both vestibular symptoms and vestibular-related disability. We assessed performance of the predictive models by applying calibration plots, Hosmer-Lemeshow statistics, area under the receiver operating characteristic curves (AUC) and applied internal validation.Results Improvement of vestibular symptoms, vestibular-related disability or both was seen in 121, 81 and 64 participants, respectively. We generated predictive models for each outcome, resulting in different predictors in the final models. Calibration for all models was adequate with non-significant Hosmer-Lemeshow statistics, but the discriminative ability of the final predictive models was poor (AUC 0.54 to 0.61). None of the identified models are therefore suitable for use in daily general practice to predict treatment success of online VR.Conclusion It is difficult to predict treatment success of internet-based VR and it remains unclear who should be treated with stand-alone VR or blended VR. Because we were unable to develop a useful prediction model, the decision to offer stand-alone or blended VR should for now be based on availability, cost effectiveness and patient preference.Trial registration number The Netherlands Trial Register NTR5712.https://bmjopen.bmj.com/content/10/10/e038649.full
collection DOAJ
language English
format Article
sources DOAJ
author Henriëtte E van der Horst
Vincent A van Vugt
Otto R Maarsingh
spellingShingle Henriëtte E van der Horst
Vincent A van Vugt
Otto R Maarsingh
Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model
BMJ Open
author_facet Henriëtte E van der Horst
Vincent A van Vugt
Otto R Maarsingh
author_sort Henriëtte E van der Horst
title Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model
title_short Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model
title_full Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model
title_fullStr Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model
title_full_unstemmed Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model
title_sort treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model
publisher BMJ Publishing Group
series BMJ Open
issn 2044-6055
publishDate 2020-10-01
description Objectives To develop and internally validate prediction models to assess treatment success of both stand-alone and blended online vestibular rehabilitation (VR) in patients with chronic vestibular syndrome.Design Secondary analysis of a randomised controlled trial.Setting 59 general practices in The Netherlands.Participants 202 adults, aged 50 years and older with a chronic vestibular syndrome who received either stand-alone VR (98) or blended VR (104). Stand-alone VR consisted of a 6-week, internet-based intervention with weekly online sessions and daily exercises. In blended VR, the same intervention was supplemented with physiotherapy support.Main outcome measures Successful treatment was defined as: clinically relevant improvement of (1) vestibular symptoms (≥3 points improvement Vertigo Symptom Scale—Short Form); (2) vestibular-related disability (>11 points improvement Dizziness Handicap Inventory); and (3) both vestibular symptoms and vestibular-related disability. We assessed performance of the predictive models by applying calibration plots, Hosmer-Lemeshow statistics, area under the receiver operating characteristic curves (AUC) and applied internal validation.Results Improvement of vestibular symptoms, vestibular-related disability or both was seen in 121, 81 and 64 participants, respectively. We generated predictive models for each outcome, resulting in different predictors in the final models. Calibration for all models was adequate with non-significant Hosmer-Lemeshow statistics, but the discriminative ability of the final predictive models was poor (AUC 0.54 to 0.61). None of the identified models are therefore suitable for use in daily general practice to predict treatment success of online VR.Conclusion It is difficult to predict treatment success of internet-based VR and it remains unclear who should be treated with stand-alone VR or blended VR. Because we were unable to develop a useful prediction model, the decision to offer stand-alone or blended VR should for now be based on availability, cost effectiveness and patient preference.Trial registration number The Netherlands Trial Register NTR5712.
url https://bmjopen.bmj.com/content/10/10/e038649.full
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