Evaluation of a web-based intervention for patients with Gestational Trophoblastic Disease: a randomized controlled trial

Objective Gestational Trophoblastic Diseases (GTD) comprise a group of rare diseases originating from the trophoblast, affecting women of child-bearing age. Providing optimal information to patients with a rare disease is challenging, because of the small number of patients and limited clinical expertise of many healthcare professionals. Both knowledge and lack of knowledge in patients may influence illness perception. We investigated whether a web-based interactive intervention influences illness perception and knowledge in women with GTD. Design Multicenter randomized control trial. Setting General and academic hospitals in the Netherlands. Population Newly diagnosed GTD patients between 2017-2019. Methods Sixty-nine patients were randomized between direct access or postponed access to an online tool on GTD and received online questionnaires about illness perception, knowledge and anxiety. The main outcome measures were illness perception (primary outcome measure) and knowledge (secondary outcome measure) Results Patients using the online tool were satisfied with the information from the tool (92%). Although they had a higher level of knowledge compared to the control group (p = 0.006), illness perception did not change. Also, no differences in levels of anxiety, depression and distress were observed between the groups. Limitations: Participants had access to other information sources and many searched other websites. It is unknown what kind of websites were visited and when. It is unknown if the increased knowledge levels and low levels of distress will sustain over time as no long term follow-up took place. Healthcare professionals were not interviewed on how they experienced the consultation before and after using the tool by the patients. Conclusions The online tool did not change illness perception, but was shown to be valuable for newly-diagnosed GTD patients to gain knowledge. The improvement in knowledge after digital education, indicates that this tool can be used as an effective method of supporting GTD patients' informational needs without causing extra distress.

A tutorial on dynamic risk prediction of a binary outcome based on a longitudinal biomarker

Dynamic risk predictions based on all available information are useful in timely identification of high‐risk patients. However, in contrast with time to event outcomes, there is still a lack of studies that clearly demonstrate how to obtain and update predictions for a future binary outcome using a repeatedly measured biomarker. The aim of this study is to give an illustrative overview of four approaches to obtain such predictions: likelihood based two‐stage method (2SMLE), likelihood based joint model (JMMLE), Bayesian two‐stage method (2SB), and Bayesian joint model (JMB). We applied the approaches to provide weekly updated predictions of post–molar gestational trophoblastic neoplasia (GTN) based on age and repeated measurements of human chorionic gonadotropin (hCG). Discrimination and calibration measures were used to compare the accuracy of the weekly predictions. Internal validation of the models was conducted using bootstrapping. The four approaches resulted in the same predictive and discriminative performance in predicting GTN. A simulation study showed that the joint models outperform the two‐stage methods when we increase the within‐ and the between‐patients variability of the biomarker. The applicability of these models to produce dynamic predictions has been illustrated through a comprehensive explanation and accompanying syntax (R and SAS^®).