Fiona Harden

Objectives: Ultrasound has an established role in the diagnostic pathway for children with suspected appendicitis. Relevant clinical information can influence the diagnostic probability and reporting of ultrasound findings. A Bayesian network (BN) is a directed acyclic graph (DAG) representing variables as nodes connected by directional arrows permitting visualisation of their relationships. This research developed a BN model with ultrasonographic and clinical variables to predict acute appendicitis in children. Methods: A DAG was designed through a hybrid method based on expert opinion and a review of literature to define the model structure; and the discretisation and weighting of identified variables were calculated using principal components analysis, which also informed the conditional probability table of nodes. Results: The acute appendicitis target node was designated as an outcome of interest influenced by four sub-models, including Ultrasound Index, Clinical History, Physical Assessment, and Diagnostic Tests. These sub-models included four sonographic, three blood-test, and six clinical variables. The BN was scenario tested and evaluated for face, predictive, and content validity. A lack of similar networks complicated concurrent and convergent validity evaluation. Conclusions: To our knowledge, this is the first BN model developed for the identification of acute appendicitis incorporating imaging variables. It has particular benefit for cases in which variables are missing because prior probabilities are built into corresponding nodes. It will be of use to clinicians involved in ultrasound examination of children with suspected appendicitis, as well as their treating clinicians. Prospective evaluation and development of an online tool will permit validation and refinement of the BN.

The purpose of this study was to determine whether the awareness and inclusion of secondary sonographic signs of appendicitis, in combination with a structured evaluation as part of engagement and training for sonographers, improved appendix visualization rates and reduced equivocal findings in children with suspected acute appendicitis. This was a prospective study of 230 children at a tertiary children's hospital in Australia referred for an ultrasound examination of suspected appendicitis. The ultrasound findings, radiology reports, histology, clinical results, and follow-up were collated. Secondary signs were used as an additional assessment of the likelihood of disease where possible, even in the absence of an identified appendix. The implementation of a structured evaluation as part of sonographer engagement and training resulted in a 28% improvement in appendix visualization (68.7%) compared with a prior retrospective study in a similar population (40.7%). The diagnostic accuracy was 91.7%, with likelihood ratios suggesting a meaningful influence of the pre-test probability of appendicitis in children studied (positive likelihood ratio, 11.22; negative likelihood ratio, 0.09.). Only 7.8% of the findings were equivocal. A binary 6-mm diameter cut-off did not account for equivocal cases, particularly lymphoid hyperplasia. Engagement of sonographers performing pediatric appendiceal ultrasound through training in the scanning technique and awareness of secondary signs significantly improved the visualization rate and provided more meaningful findings to referrers.

Introduction: We evaluated the predictive value of sonographic and clinical variables in the diagnosis of acute appendicitis in children using decision tree modelling.Methods: Data pooled from two prior studies in the same population of children referred for ultrasound examination of suspected acute appendicitis. Ultrasound and clinical variables were collated and compared with patient records. Decision tree algorithms were used to model data to identify highly discriminatory variables. Receiver operative characteristic (ROC) curve analyses were performed on different models of appendiceal diameter criteria.Results: There were 687 examinations included. Diameter modelling indicated that categorical assessment – below 6 mm as negative, diameters between 6 and 8 mm equivocal for appendicitis and above 8 mm positive – was more accurate (AUROC = 0.921) than the most accurate binary cut‐off (7 mm, AUROC = 0.886). Decision tree analysis supported categorical diameter criteria, demonstrated that the presence of echogenic mesentery was an important variable and showed that common blood test results can be complementary discriminators of ultrasound findings.Discussion: The use of a binary appendiceal diameter cut‐off was less accurate than a three‐category model. Absence of peri‐appendiceal mesentery inflammation is an important negative predictor of appendicitis in children, even without direct visualisation of the appendix.

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