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

Yue Wu, Charlie McLeod, Christopher Blyth, Asha Bowen, Andrew Martin, Ann Nicholson, Steven Mascaro, Tom Snelling

Research output: Contribution to journalArticle

Abstract

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 predicting culture results of different types of specimens. BN-recommended antibiotics were rated optimal or adequate by experts in 82–98% of 81 cases sampled from the cohort. We have demonstrated the potential use of BNs in improving antibiotic selection for children with OM, which we believe to be generalisable in the development of a broader range of decision support tools. With appropriate validation, such tools might be effectively deployed for real-time clinical decision support, to promote a shift in clinical practice from generic to individually-targeted antibiotic therapy, and ultimately improve the management and outcomes for a range of serious bacterial infections.

Original languageEnglish
Article number101895
JournalArtificial Intelligence in Medicine
Volume107
DOIs
Publication statusPublished - Jul 2020

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