Development and Evaluation of Clinical Innovations
Innovations can improve the quality and efficiency of healthcare. We develop clinical risk prediction models for diagnosis and prognosis, as well as clinical interventions to support decision making. We also evaluate prediction models and biomarkers, as well as preventive, lifestyle and therapeutic interventions, using a range of methodological tools. Our team has extensive experience in validation and diagnostic accuracy studies, (quasi-)experimental studies, systematic literature review and meta-analysis, and qualitative implementation research. Key applications include oncology, obstetrics, gynecology, infectious disease, cardiovascular disease, and psychiatry.
Projects
Expect
Unfortunately, pregnancies are not always without complications. Although complications are treatable in some cases, prevention is better than cure. It is therefore useful if an obstetrician or gynecologist can make a good assessment of the possible risks at the start of pregnancy.
Early risk selection in pregnant women can be improved by applying risk models. In the Expect study I, we selected all existing risk models that predict the individual risk of preeclampsia, gestational diabetes, growth disorder or spontaneous preterm birth based on maternal characteristics and blood pressure from the medical literature. We then investigated whether these models perform sufficiently in the Dutch population. A total of 2614 pregnant women in the province of Limburg participated in the study. The models for preeclampsia and gestational diabetes showed good predictive power.
In the Expect study II, models for estimation of the risk of preeclampsia or gestational diabetes have been applied in maternity care in combination with care pathways tailored to the personal risk. The results show that more healthy babies were born during the application of the new birth care, especially among women who were pregnant for the first time. In addition, the new birth care has led to a significant cost reduction of an average of €2,700 per pregnancy.
The fate of clinical prediction models, and factors influencing their chance of success
Clinical prediction models (CPMs) can inform individuals and clinicians about the risk of having (diagnosis) or developing (prognosis) a particular disorder or outcome. They can improve healthcare quality by helping clinicians and individuals with timely decision making, or improved cost-effectiveness of treatment strategies.
The annual number of articles on prediction model development has exponentially increased over the last decades. While some CPMs have been implemented and are regularly used in clinical practice, the clinical uptake of CPMs is known to be limited relative to the number that are developed. Prior to implementing a CPM in practice, research must show that its predictions are generalisable to individuals independent of model development, through external validation. Following validation, the impact of the CPM on clinical management and outcomes should ideally be evaluated. Currently, based on reviews, a minority of developed models are externally validated. Although it has rarely been quantified systematically, even fewer CPMs undergo impact assessment.
In addition, the proportion of CPMs being implemented or used in clinical practice has not been studied systematically and is limited to one study on the clinical utilization of artificial intelligence in the COVID-19 response. Studies of CPMs that are not translated into clinical practice, such as well-performing models that do not undergo external validation or validated models that are never assessed for clinical utility, can be considered research waste.
The aim of our research is to evaluate the proportion of CPMs that undergo external validation, impact assessment or implementation and make it into clinical practice after development. We also aim to identify factors that influence a prediction models chances of being used in clinical practice.
Cancer After Care Guide
Former cancer patients often experience residual symptoms such as fatigue after treatment. They require guidance during their recovery. General practitioners need effective methods to provide support to these patients. In this project, we offer general practitioners and practice nurses a simple way to provide appropriate support without taking up a lot of time. Patients get to work on lifestyle changes and common physical or psychosocial complaints after cancer, such as fatigue.
Participants will be randomly assigned to the experimental or control group. The experimental group will use the eHealth program, which guides them throughout the post-cancer period. The intervention consists of two regular billable consultations with the GP or practice nurse. During these consulataions, the counsellor discusses the patient's progress in the program and any problems they experience in implementing lifestyle) changes.
The patients work independently with the eHealth program, which includes feedback on their self-management. General practitioners or nurse practitioners in the control group will only include patients during the study whom they will then provide normal GP care. The patients in the control group will complete digital questionnaires. After one year, the patients in the control group will also be actively made aware of the digital self-help program. At the end of the intervention period, biomedical measurements (e.g., Blood pressure) will be administered to all patients.
The aim is to include 10-20 patients per practice. Expense reimbursement is available. Enrollment is open until August 2023. Interested parties can contact the researchers via 045-5762384 or herstelnakanker@ou.nl. The research 'Recovery after cancer' is a collaboration between the Open University and Maastricht University, together with the Netherlands Comprehensive Cancer Centre, the Dutch Federation of Cancer Patients, CZ and four regional cancer centers.
Contact: Ilse Mesters