Use of causal DAG and regression analysis to understand and predict complicated osteoarticular infection in children

BackgroundWhile osteoarticular infections of the bones and joints in children usually resolve completely with adequate treatment, some children will develop complications. Predicting which children are at highest risk of complications might enable prevention through more aggressive treatment. MethodsWe used mutual information and Kullback-Leibler divergence methods to compare the characteristics of osteoarticular infections at the same tertiary institution in Australia in two time periods, 2002-2007 (N=295) and 2016-2018 (N=192). We used expert knowledge to develop a causal directed acyclic graph (DAG) that depicts the mechanistic pathways of osteoarticular infections and their progression to complications. Guided by the DAG, we developed three logistic regression (LR) models for predicting complications and evaluated their area under curve (AUC) to assess their performance for predicting complication. ResultsWe observed a shift in the approach to diagnostic testing over the two time periods, with an increase in the number of blood cultures performed and a decrease in the rate of wound cultures. Fourteen of 43 test PCR tests (33%) for K. kingae recorded positive results. The established causal DAG clarified how the underlying, latent and dynamic biological processes become manifest as data. Utilising only data available at the initial point of care, the best LR model identified an optimal feature set that achieved an AUC of 0.85 for predicting complications. ConclusionsSupported by domain expert knowledge and data, causal and statistical approaches were combined to offer valuable insights for predicting progression to complicated disease for children with osteoarticular infections. Key messagesO_LIThis study facilitates the understanding of change in epidemiology and clinical management of paediatric osteomyelitis and septic arthritis by examining a prospective cohort of 295 cases (2016-2018) in comparison to a similar study collected at an earlier date (2002-2007) from the same facility. C_LIO_LIWe present a clarified understanding of the mechanistic pathways of osteoarticular infections and their progression to complications through the development of a causal directed acyclic graph (DAG) based on previously published DAG, recently collected data and domain expert knowledge. C_LIO_LIWe demonstrate an approach to combine causal DAG, BN-based predictions of the causative pathogen, logistic regression modelling, and recursive feature elimination to achieve optimal performance in predicting complicated disease. C_LI