Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid
Objective To develop a prediction model and illustrate the practical potential of personalisation of treatment decisions between app-based treatment and care as usual for urinary incontinence (UI).Design A prediction model study using data from a pragmatic, randomised controlled, non-inferiority tri...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2022-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/7/e051827.full |
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| author | Huibert Burger Henk van der Worp Marco H Blanker Marjolein Y Berger Anne Martina Maria Loohuis Nienke Wessels Janny Dekker Alec GGA Malmberg |
| author_facet | Huibert Burger Henk van der Worp Marco H Blanker Marjolein Y Berger Anne Martina Maria Loohuis Nienke Wessels Janny Dekker Alec GGA Malmberg |
| author_sort | Huibert Burger |
| collection | DOAJ |
| description | Objective To develop a prediction model and illustrate the practical potential of personalisation of treatment decisions between app-based treatment and care as usual for urinary incontinence (UI).Design A prediction model study using data from a pragmatic, randomised controlled, non-inferiority trial.Setting Dutch primary care from 2015, with social media included from 2017. Enrolment ended on July 2018.Participants Adult women were eligible if they had ≥2 episodes of UI per week, access to mobile apps and wanted treatment. Of the 350 screened women, 262 were eligible and randomised to app-based treatment or care as usual; 195 (74%) attended follow-up.Predictors Literature review and expert opinion identified 13 candidate predictors, categorised into two groups: Prognostic factors (independent of treatment type), such as UI severity, postmenopausal state, vaginal births, general physical health status, pelvic floor muscle function and body mass index; and modifiers (dependent on treatment type), such as age, UI type and duration, impact on quality of life, previous physical therapy, recruitment method and educational level.Main outcome measure Primary outcome was symptom severity after a 4-month follow-up period, measured by the International Consultation on Incontinence Questionnaire the Urinary Incontinence Short Form. Prognostic factors and modifiers were combined into a final prediction model. For each participant, we then predicted treatment outcomes and calculated a Personalised Advantage Index (PAI).Results Baseline UI severity (prognostic) and age, educational level and impact on quality of life (modifiers) independently affected treatment effect of eHealth. The mean PAI was 0.99±0.79 points, being of clinical relevance in 21% of individuals. Applying the PAI also significantly improved treatment outcomes at the group level.Conclusions Personalising treatment choice can support treatment decision making between eHealth and care as usual through the practical application of prediction modelling. Concerning eHealth for UI, this could facilitate the choice between app-based treatment and care as usual.Trial registration number NL4948t. |
| format | Article |
| id | doaj-art-18274521a3eb41778daedcf3c43f20ab |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-18274521a3eb41778daedcf3c43f20ab2025-01-31T14:30:09ZengBMJ Publishing GroupBMJ Open2044-60552022-07-0112710.1136/bmjopen-2021-051827Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aidHuibert Burger0Henk van der Worp1Marco H Blanker2Marjolein Y Berger3Anne Martina Maria Loohuis4Nienke Wessels5Janny Dekker6Alec GGA Malmberg7Department of General Practice and Elderly Care medicine, University Medical Center Groningen, Groningen, The NetherlandsDepartment of General Practice and Elderly Care medicine, University Medical Center Groningen, Groningen, The Netherlands1 Department of Primary and Long Term Care, University Medical Center Groningen, Groningen University, Groningen, The NetherlandsDepartment of Primary and Long-term Care, University Medical Center Groningen, Groningen, The NetherlandsDepartment of General Practice and Elderly Care medicine, University Medical Center Groningen, Groningen, The NetherlandsDepartment of General Practice and Elderly Care medicine, University Medical Center Groningen, Groningen, The NetherlandsDepartment of General Practice and Elderly Care medicine, University Medical Center Groningen, Groningen, The NetherlandsDepartment of Obstetrics and Gynaecology, University Medical Centre Groningen, Groningen, The NetherlandsObjective To develop a prediction model and illustrate the practical potential of personalisation of treatment decisions between app-based treatment and care as usual for urinary incontinence (UI).Design A prediction model study using data from a pragmatic, randomised controlled, non-inferiority trial.Setting Dutch primary care from 2015, with social media included from 2017. Enrolment ended on July 2018.Participants Adult women were eligible if they had ≥2 episodes of UI per week, access to mobile apps and wanted treatment. Of the 350 screened women, 262 were eligible and randomised to app-based treatment or care as usual; 195 (74%) attended follow-up.Predictors Literature review and expert opinion identified 13 candidate predictors, categorised into two groups: Prognostic factors (independent of treatment type), such as UI severity, postmenopausal state, vaginal births, general physical health status, pelvic floor muscle function and body mass index; and modifiers (dependent on treatment type), such as age, UI type and duration, impact on quality of life, previous physical therapy, recruitment method and educational level.Main outcome measure Primary outcome was symptom severity after a 4-month follow-up period, measured by the International Consultation on Incontinence Questionnaire the Urinary Incontinence Short Form. Prognostic factors and modifiers were combined into a final prediction model. For each participant, we then predicted treatment outcomes and calculated a Personalised Advantage Index (PAI).Results Baseline UI severity (prognostic) and age, educational level and impact on quality of life (modifiers) independently affected treatment effect of eHealth. The mean PAI was 0.99±0.79 points, being of clinical relevance in 21% of individuals. Applying the PAI also significantly improved treatment outcomes at the group level.Conclusions Personalising treatment choice can support treatment decision making between eHealth and care as usual through the practical application of prediction modelling. Concerning eHealth for UI, this could facilitate the choice between app-based treatment and care as usual.Trial registration number NL4948t.https://bmjopen.bmj.com/content/12/7/e051827.full |
| spellingShingle | Huibert Burger Henk van der Worp Marco H Blanker Marjolein Y Berger Anne Martina Maria Loohuis Nienke Wessels Janny Dekker Alec GGA Malmberg Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid BMJ Open |
| title | Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid |
| title_full | Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid |
| title_fullStr | Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid |
| title_full_unstemmed | Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid |
| title_short | Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid |
| title_sort | prediction model study focusing on ehealth in the management of urinary incontinence the personalised advantage index as a decision making aid |
| url | https://bmjopen.bmj.com/content/12/7/e051827.full |
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