Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia.
Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia.
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DOI：
10.12688/wellcomeopenres.14847.1
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发表时间：
2018-01-01
影响因子：
--
通讯作者：
Cooper, Ben S
中科院分区：
文献类型：
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作者：
Oonsivilai, Mathupanee;Mo, Yin;Cooper, Ben S
背景：对疑似脓毒症患者进行早期和适当的经验性抗生素治疗可降低死亡率。抗菌素耐药性日益普遍，降低了源自人群数据的经验性治疗指南的功效。这个问题对于发展中国家的儿童来说尤其严重。我们假设，通过应用机器学习方法来轻松收集患者数据，就有可能获得针对有针对性的经验性抗生素选择的个性化预测。方法和结果：我们分析了 2013 年 2 月至 2016 年 1 月从柬埔寨西北部一家拥有 100 个床位的儿童医院收集的血培养数据。通过 35 个自变量获取临床、人口和生活状况信息。利用这些变量，我们使用了一套机器学习算法来预测革兰氏染色以及细菌病原体是否可以用常见的经验性抗生素治疗方案进行治疗：i）氨苄青霉素和庆大霉素； ii) 头孢曲松； iii) 以上都不是。 243 名患有血流感染的患者可供分析。我们发现，根据接受者操作特征曲线 (AUC) 下的面积评估，随机森林方法总体上具有最佳预测性能。随机森林法给出的预测对头孢曲松敏感性的 AUC 为 0.80 (95% CI 0.66-0.94)，预测对氨苄西林和庆大霉素敏感性的 AUC 为 0.74 (0.59-0.89)，预测对两者都不敏感的 AUC 为 0.85 (0.70-1.00)，预测对两者都不敏感的 AUC 为 0.71 (0.57-0.86)。以获得革兰氏染色结果。预测易感性的最重要变量是从入院到血培养的时间、患者年龄、医院获得性感染与社区获得性感染以及年龄调整体重评分。结论：将机器学习算法应用于即使在资源有限的医院环境中也容易获得的患者数据，可以提供有关抗生素敏感性的高度信息预测，以指导适当的经验性抗生素治疗。当用作决策支持工具时，此类方法有可能改善经验性治疗的针对性、患者的治疗结果并减轻抗菌药物耐药性的负担。
Background: Early and appropriate empiric antibiotic treatment of patients suspected of having sepsis is associated with reduced mortality. The increasing prevalence of antimicrobial resistance reduces the efficacy of empiric therapy guidelines derived from population data. This problem is particularly severe for children in developing country settings. We hypothesized that by applying machine learning approaches to readily collect patient data, it would be possible to obtain individualized predictions for targeted empiric antibiotic choices. Methods and Findings: We analysed blood culture data collected from a 100-bed children's hospital in North-West Cambodia between February 2013 and January 2016. Clinical, demographic and living condition information was captured with 35 independent variables. Using these variables, we used a suite of machine learning algorithms to predict Gram stains and whether bacterial pathogens could be treated with common empiric antibiotic regimens: i) ampicillin and gentamicin; ii) ceftriaxone; iii) none of the above. 243 patients with bloodstream infections were available for analysis. We found that the random forest method had the best predictive performance overall as assessed by the area under the receiver operating characteristic curve (AUC). The random forest method gave an AUC of 0.80 (95%CI 0.66-0.94) for predicting susceptibility to ceftriaxone, 0.74 (0.59-0.89) for susceptibility to ampicillin and gentamicin, 0.85 (0.70-1.00) for susceptibility to neither, and 0.71 (0.57-0.86) for Gram stain result. Most important variables for predicting susceptibility were time from admission to blood culture, patient age, hospital versus community-acquired infection, and age-adjusted weight score. Conclusions: Applying machine learning algorithms to patient data that are readily available even in resource-limited hospital settings can provide highly informative predictions on antibiotic susceptibilities to guide appropriate empiric antibiotic therapy. When used as a decision support tool, such approaches have the potential to improve targeting of empiric therapy, patient outcomes and reduce the burden of antimicrobial resistance.