Predicting the causative pathogen among children with osteomyelitis using Bayesian networks – improving antibiotic selection in clinical practice

• Published in: Artificial intelligence in medicine 2020-07, Vol.107, p.101895-101895, Article 101895
• Main Authors: Wu, Yue, McLeod, Charlie, Blyth, Christopher, Bowen, Asha, Martin, Andrew, Nicholson, Ann, Mascaro, Steven, Snelling, Tom
• Format: Article
• Language: English
• Subjects: Bayesian belief network; Bone infection; Causal diagram; Clinical decision support; Infectious disease; Probabilistic graph model
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2020.101895

•Establish a generalisable methodological framework to help improve our understanding of the epidemiology of bone infections in children.•Model the relationship between unobserved infecting pathogens, observed culture results, and clinical and demographic variables.•Expert knowledge plays a critical role in building the model of paediatric osteomyelitis, building on what the data provides.•Illustrate the use of utility function in translating probabilistic model outputs to implementable recommendations for antibiotic selection.•This approach can be applied broadly to antibiotic decision-making under imperfect information - a critical challenge in clinical medicine. Infection of bone, osteomyelitis (OM), is a serious bacterial infection in children requiring urgent antibiotic therapy. While biological specimens are often obtained and cultured to guide antibiotic selection, culture results may take several days, are often falsely negative, and may be falsely positive because of contamination by non-causative bacteria. This poses a dilemma for clinicians when choosing the most suitable antibiotic. Selecting an antibiotic which is too narrow in spectrum risks treatment failure; selecting an antibiotic which is too broad risks toxicity and promotes antibiotic resistance. We have developed a Bayesian Network (BN) model that can be used to guide individually targeted antibiotic therapy at point-of-care, by predicting the most likely causative pathogen in children with OM and the antibiotic with optimal expected utility. The BN explicitly models the complex relationship between the unobserved infecting pathogen, observed culture results, and clinical and demographic variables, and integrates data with critical expert knowledge under a causal inference framework. Development of this tool resulted from a multidisciplinary approach, involving experts in infectious diseases, modelling, paediatrics, microbiology, computer science and statistics. The model-predicted prevalence of causative pathogens among children with osteomyelitis were 56 % for Staphylococcus aureus, 17 % for ‘other’ culturable bacteria (like Streptococcus pyogenes), and 27 % for bacterial pathogens that are not culturable using routine methods (like Kingella kingae). Log loss cross-validation suggests that the model performance is robust, with the best fit to culture results achieved when data and expert knowledge were combined during parameterisation. AUC values of 0.68 – 0.77 were achieved for pred