Department of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Department of Oncology, the Third Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453000, China.
Curr Med Sci. 2018 Dec;38(6):1025-1031. doi: 10.1007/s11596-018-1979-x. Epub 2018 Dec 7.
The present study aimed to establish a list of parameters indicative of pathogen invasion and develop a predictive model to distinguish the etiologies of fever of unknown origin (FUO) into infectious and non-infectious causes. From January 2014 to September 2017, 431 patients with FUO were prospectively enrolled in the study population. This study established a list of 26 variables from the following 4 aspects: host factors, epidemiological factors, behavioral factors, and iatrogenic factors. Predefined predicted variables were included in a multivariate logistic regression analysis to develop a predictive model. The predictive model and the corresponding scoring system were developed using data from the confirmed diagnoses and 9 variables were eventually identified. These factors were incorporated into the predictive model. This model discriminated between infectious and non-infectious causes of FUO with an AUC of 0.72, sensitivity of 0.71, and specificity of 0.63. The predictive model and corresponding scoring system based on factors concerning pathogen invasion appear to be reliable screening tools to discriminate between infectious and non-infectious causes of FUO.
本研究旨在建立一组提示病原体入侵的参数，并开发一个预测模型，以区分不明原因发热（FUO）的病因是感染性或非感染性。从 2014 年 1 月至 2017 年 9 月，前瞻性纳入 431 例 FUO 患者作为研究对象。本研究从宿主因素、流行病学因素、行为因素和医源性因素 4 个方面建立了 26 个变量列表。将预定义的预测变量纳入多变量逻辑回归分析，以建立预测模型。使用确诊诊断数据和 9 个变量开发预测模型和相应的评分系统。这些因素被纳入预测模型。该模型区分 FUO 的感染性和非感染性病因的 AUC 为 0.72，敏感性为 0.71，特异性为 0.63。基于病原体入侵相关因素的预测模型和相应的评分系统似乎是区分 FUO 的感染性和非感染性病因的可靠筛选工具。
