Preventive Medice Service, Complexo Hospitalario Universitario de Ourense, Rúa Ramón Puga 52-56, 32004 Ourense, Spain.
Department of Computer Science, University of Vigo, ESEI-Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain.
Biomed Res Int. 2019 Sep 23;2019:1049575. doi: 10.1155/2019/1049575. eCollection 2019.
Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%).
医院获得性感染（HAI）监测是指系统地收集与特定卫生事件相关的数据，被认为是预防 HAI 计划有效的重要方面。近年来，随着电子病历（EHR）的广泛采用，新的自动化 HAI 监测方法已经出现。在这里，我们展示了在西班牙奥伦塞大学医院综合体中部署的 InNoCBR 系统针对 HAI 诊断金标准的验证结果。作为一个完全自主的系统，InNoCBR 实现了 70.83%的 HAI 灵敏度和 97.76%的特异性，阳性预测值为 77.24%。感染类型分类的 Kappa 指数为 0.67。灵敏度取决于感染类型，血流感染达到最佳值（93.33%），而呼吸道感染的改善空间最大（53.33%）。作为半自动系统，InNoCBR 达到了高灵敏度（81.73%）、特异性（99.47%）和出色的阳性预测值（94.33%）。
