Because AA can resemble common self-limiting conditions in children, its detection can be challenging for general practitioners. As a result, 19% of AA cases are missed during the first consultation, and 70% of referrals are non-AA cases. This places a significant burden on patients, their families, and the healthcare system. An evidence-based diagnostic strategy for AA could help general practitioners in reducing the rate of non-AA referrals while ensuring timely identification of children with AA. Therefore, this study aims to evaluate the impact of an externally validated clinical prediction rule including C-reactive protein for AA, on referral efficiency in children presenting with acute abdominal pain in primary care.
This study is performed in collaboration with Leiden University Medical Center and University Medical Center Utrecht. Our research team includes general practitioners as well as secondary care specialists, such as paediatricians, (paediatric) surgeons, and emergency physicians. Patients are represented within the study’s patient panel and will also be involved in the qualitative part of the research.
The goal of the ISAAK study is to improve the referral process for AA in children with acute abdominal pain. A machine-learning-based prediction rule, which classifies children into risk groups for AA, could support general practitioners in making more informed decisions. This may improve the quality and efficiency of care, thereby easing the burden on children and their parents, lowering healthcare costs and reducing workloads in both primary and secondary care.
