A Clinical Analysis of 293 FUO Patients, A Diagnostic Model Discriminating infectious Diseases from Non-infectious Diseases

Autor: Yun-zhu Long, Yu-tao Xie, Meng-hou Lu, Xu-wen Xu, Qing Zhou, De-ming Tan
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Zdroj: Infection International, Vol 3, Iss 2, Pp 54-63 (2014)
ISSN: 2544-0349
Popis: Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin (FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The first group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases (αin = 0.05, αout = 0.10). The second group was used to evaluate the diagnostic model and make ROC analysis. Results The diagnostic rate of 143 patients in the first group was 87.4%, the diagnosis included infectious disease (52.4%), connective tissue diseases (16.8%), neoplastic disease (16.1%) and miscellaneous (2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the first group. Logistic regression analysis showed that decreased white blood cell count (WBC < 4.0×109/L), higher lactate dehydrogenase level (LDH > 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11 (P ≤ 0.01) , respectively. In ROC analysis, the sensitivity and specificity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively (AUC = 0.76, P = 0.00). Conclusions The combination of WBC < 4.0×109/L, LDH > 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.
Databáze: OpenAIRE