Department of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Curr Med Sci. 2019 Aug;39(4):589-596. doi: 10.1007/s11596-019-2078-3. Epub 2019 Jul 25.
The diagnosis and treatment of fever of unknown origin (FUO) are huge challenges to clinicians. Separating the etiologies of FUO into infectious and non-infectious disease is conducive to clinical physicians not only on making decisions rapidly concerning the prescription of suitable antibiotics but also on further analysis of the final diagnosis. In order to develop and validate a diagnostic tool to efficiently distinguish the etiologies of adult FUO patients as infectious or non-infectious disease, FUO patients from the departments of infectious disease and internal medicine in three Chinese tertiary hospitals were enrolled retrospectively and prospectively. By using polynomial logistic regression analysis, the diagnostic formula and the associated scoring system were developed. The variables included in this diagnostic formula were from clinical evaluations and common laboratory examinations. The proposed tool could discriminate infectious and non-infectious causes of FUO with an area under receiver operating characteristic curve (AUC) of 0.83, sensitivity of 0.80 and specificity of 0.75. This diagnosis tool could predict the infectious and non-infectious causes of FUO in the validation cohort with an AUC of 0.79, sensitivity of 0.79 and specificity of 0.70. The results suggested that this diagnostic tool could be a reliable tool to discriminate between infectious and non-infectious causes of FUO.
发热待查(FUO)的诊断和治疗对临床医生来说是巨大的挑战。将 FUO 的病因分为感染性和非感染性疾病有助于临床医生不仅快速决定是否使用合适的抗生素,而且有助于对最终诊断进行进一步分析。为了开发和验证一种有效的诊断工具,以区分成人 FUO 患者的病因是感染性还是非感染性疾病,我们回顾性和前瞻性地招募了来自中国 3 家三级医院感染科和内科的 FUO 患者。通过使用多项式逻辑回归分析,开发了诊断公式和相关评分系统。该诊断公式中包含的变量来自临床评估和常规实验室检查。该工具对 FUO 的感染性和非感染性病因的鉴别诊断具有较高的准确性,其受试者工作特征曲线(ROC)下面积(AUC)为 0.83,敏感性为 0.80,特异性为 0.75。在验证队列中,该工具对 FUO 的感染性和非感染性病因的预测具有较高的准确性,AUC 为 0.79,敏感性为 0.79,特异性为 0.70。结果表明,该诊断工具可作为一种可靠的工具,用于区分 FUO 的感染性和非感染性病因。