Department of General Practice and Elderly Care medicine, University Medical Center Groningen, Groningen, The Netherlands
Department of General Practice and Elderly Care medicine, University Medical Center Groningen, Groningen, The Netherlands.
BMJ Open. 2022 Jul 25;12(7):e051827. doi: 10.1136/bmjopen-2021-051827.
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).
A prediction model study using data from a pragmatic, randomised controlled, non-inferiority trial.
Dutch primary care from 2015, with social media included from 2017. Enrolment ended on July 2018.
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.
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.
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).
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.
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.
NL4948t.
为基于应用程序的治疗和常规护理治疗尿失禁（UI）之间的治疗决策制定预测模型并说明个性化的实际潜力。
使用来自一项实用、随机对照、非劣效性试验的数据进行预测模型研究。
2015 年荷兰初级保健，自 2017 年起纳入社交媒体。招募于 2018 年 7 月结束。
符合条件的成年女性每周至少有 2 次 UI 发作、能够使用移动应用程序并希望接受治疗。在 350 名筛查女性中，262 名符合条件并被随机分配至基于应用程序的治疗或常规护理；195 名（74%）参加了随访。
文献回顾和专家意见确定了 13 个候选预测因子，分为两组：预后因素（与治疗类型无关），如 UI 严重程度、绝经状态、阴道分娩、一般身体健康状况、盆底肌肉功能和体重指数；和修饰因子（取决于治疗类型），如年龄、UI 类型和持续时间、对生活质量的影响、以前的物理治疗、招募方法和教育水平。
主要结局是 4 个月随访期后的症状严重程度，采用国际尿失禁咨询问卷尿失禁简短问卷进行测量。将预后因素和修饰因子组合成最终预测模型。对于每个参与者，我们预测治疗结果并计算个性化优势指数（PAI）。
基线 UI 严重程度（预后）和年龄、教育水平和对生活质量的影响（修饰）独立影响了电子健康治疗效果。PAI 的平均值为 0.99±0.79 分，21%的个体具有临床意义。应用 PAI 也显著改善了组水平的治疗结果。
通过预测模型的实际应用，个性化治疗选择可以支持基于应用程序的治疗和常规护理之间的治疗决策。就 UI 的电子健康而言，这可以促进在基于应用程序的治疗和常规护理之间做出选择。
NL4948t。
