TY - JOUR
T1 - Prediction model study focusing on eHealth in the management of urinary incontinence
T2 - The Personalised Advantage Index as a decision-making aid
AU - Loohuis, Anne Martina Maria
AU - Burger, Huibert
AU - Wessels, Nienke
AU - Dekker, Janny
AU - Malmberg, Alec G.G.A.
AU - Berger, Marjolein Y.
AU - Blanker, Marco H.
AU - Van Der Worp, Henk
N1 - Funding Information:
This work was supported by a grant from ZonMw, The Dutch Organisation for Health Research and Development (project number: 837001508) and subfunded by a grant from the P.W. Boer foundation. The study won the Professor Huygen award 2016 for best study proposal in general practice, which included additional funding.
Publisher Copyright:
© 2022 BMJ Publishing Group. All rights reserved.
PY - 2022/7/25
Y1 - 2022/7/25
N2 - 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.
AB - 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.
KW - clinical trials
KW - primary care
KW - statistics & research methods
KW - telemedicine
KW - urinary incontinences
KW - urogynaecology
U2 - 10.1136/bmjopen-2021-051827
DO - 10.1136/bmjopen-2021-051827
M3 - Article
C2 - 35879013
AN - SCOPUS:85135090395
SN - 2044-6055
VL - 12
JO - BMJ Open
JF - BMJ Open
IS - 7
M1 - e051827
ER -