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.

骨感染骨髓炎 (OM) 是一种严重的细菌感染，儿童需要紧急抗生素治疗。虽然通常获取和培养生物标本以指导抗生素选择，但培养结果可能需要几天时间，通常为假阴性，并且可能因非致病细菌污染而为假阳性。这给临床医生选择最合适的抗生素带来了两难选择。选择谱系太窄的抗生素有治疗失败的风险；选择过于广泛的抗生素会带来毒性并促进抗生素耐药性。

我们开发了一个贝叶斯网络 (BN) 模型，该模型可通过预测 OM 儿童最可能的致病病原体和具有最佳预期效用的抗生素，用于在护理点指导个体靶向抗生素治疗。BN 明确模拟了未观察到的感染病原体、观察到的培养结果以及临床和人口统计学变量之间的复杂关系，并在因果推理框架下将数据与关键专家知识相结合。该工具的开发源于多学科方法，涉及传染病、建模、儿科、微生物学、计算机科学和统计学方面的专家。

模型预测的骨髓炎患儿致病病原体的流行率为 56 %金黄色葡萄球菌，17 % 的“其他”可培养细菌（如化脓性链球菌），以及无法使用常规方法培养的细菌病原体（如金氏金黄色葡萄球菌）的 27 % ）。对数损失交叉验证表明模型性能是稳健的，在参数化过程中结合数据和专家知识时获得的培养结果最适合。AUC 值为 0.68 – 0.77，可用于预测不同类型标本的培养结果。在从队列中抽样的 81 例病例中，有 82-98% 的专家认为 BN 推荐的抗生素是最佳或足够的。

我们已经证明了 BN 在改善 OM 儿童抗生素选择方面的潜在用途，我们认为这在开发更广泛的决策支持工具中是可以推广的。通过适当的验证，这些工具可以有效地用于实时临床决策支持，促进临床实践从通用抗生素治疗转向个体化抗生素治疗，并最终改善一系列严重细菌感染的管理和结果。