Department of Psychology, Health and Technology/Center for eHealth Research and Disease Management, Faculty of Behavioural Sciences, University of Twente, Enschede, The Netherlands.
Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands.
BMC Infect Dis. 2022 Jan 20;22(1):67. doi: 10.1186/s12879-022-07043-9.
Vancomycin-resistant enterococci (VRE) is the cause of severe patient health and monetary burdens. Antibiotic use is a confounding effect to predict VRE in patients, but the antibiotic use of patients who may have frequented the same ward as the patient in question is often neglected. This study investigates how patient movements between hospital wards and their antibiotic use can explain the colonisation of patients with VRE.
Intrahospital patient movements, antibiotic use and PCR screening data were used from a hospital in the Netherlands. The PageRank algorithm was used to calculate two daily centrality measures based on the spatiotemporal graph to summarise the flow of patients and antibiotics at the ward level. A decision tree model was used to determine a simple set of rules to estimate the daily probability of patient VRE colonisation for each hospital ward. The model performance was improved using a random forest model and compared using 30% test sample.
Centrality covariates summarising the flow of patients and their antibiotic use between hospital wards can be used to predict the daily colonisation of VRE at the hospital ward level. The decision tree model produced a simple set of rules that can be used to determine the daily probability of patient VRE colonisation for each hospital ward. An acceptable area under the ROC curve (AUC) of 0.755 was achieved using the decision tree model and an excellent AUC of 0.883 by the random forest model on the test set. These results confirms that the random forest model performs better than a single decision tree for all levels of model sensitivity and specificity on data not used to estimate the models.
This study showed how the movements of patients inside hospitals and their use of antibiotics could predict the colonisation of patients with VRE at the ward level. Two daily centrality measures were proposed to summarise the flow of patients and antibiotics at the ward level. An early warning system for VRE can be developed to test and further develop infection prevention plans and outbreak strategies using these results.
耐万古霉素肠球菌(VRE)是导致患者健康状况严重恶化和经济负担加重的原因。抗生素的使用是预测患者是否会感染 VRE 的一个混杂因素,但往往忽略了与患者可能在同一病房出入的患者的抗生素使用情况。本研究旨在探讨患者在医院病房之间的移动以及他们使用抗生素的情况如何解释 VRE 患者的定植。
从荷兰的一家医院收集了院内患者移动、抗生素使用和 PCR 筛查数据。使用 PageRank 算法基于时空图计算了两个每日中心度度量,以总结病房层面上患者和抗生素的流动情况。使用决策树模型确定了一组简单的规则来估计每个医院病房患者 VRE 定植的每日概率。使用随机森林模型改进模型性能,并使用 30%的测试样本进行比较。
概括医院病房之间患者和抗生素流动情况的中心度协变量可用于预测医院病房层面 VRE 的每日定植情况。决策树模型生成了一组简单的规则,可用于确定每个医院病房患者 VRE 定植的每日概率。决策树模型在测试集上获得了可接受的 0.755 的 ROC 曲线下面积(AUC),随机森林模型获得了 0.883 的优异 AUC。这些结果证实,在未用于估计模型的数据上,随机森林模型在所有模型灵敏度和特异性水平上的表现均优于单个决策树。
本研究表明,患者在医院内部的移动及其抗生素的使用情况可以预测病房层面 VRE 患者的定植情况。提出了两个每日中心度度量来总结病房层面上患者和抗生素的流动情况。可以开发一个 VRE 早期预警系统,使用这些结果来测试和进一步制定感染预防计划和爆发策略。