Suppr超能文献

预测感染人类免疫缺陷病毒患者的鸟分枝杆菌复合群菌血症:一个经过前瞻性验证的模型。

Predicting Mycobacterium avium complex bacteremia in patients infected with human immunodeficiency virus: a prospectively validated model.

作者信息

Chin D P, Reingold A L, Horsburgh C R, Yajko D M, Hadley W K, Elkin E P, Stone E N, Simon E M, Gonzalez P C, Ostroff S M

机构信息

Medical Service, San Francisco General Hospital Medical Center, California.

出版信息

Clin Infect Dis. 1994 Oct;19(4):668-74. doi: 10.1093/clinids/19.4.668.

Abstract

In cases of advanced infection with human immunodeficiency virus, mycobacterial blood cultures are frequently used to diagnose disseminated infection with the Mycobacterium avium complex (MAC). However, no prospectively validated guidelines exist for the use of such cultures. In this study, a two-part model for predicting MAC bacteremia was developed and then validated prospectively. First, a CD4+ cell count of < or = 50/microL was used to predict bacteremia. Then, among patients with < or = 50 CD4+ cells/microL, the documentation of fever on more than 30 days during the preceding 3 months, a hematocrit of < 30%, or a serum albumin concentration of < 3.0 g/dL was used to predict bacteremia. This model had a sensitivity of 89% and positive and negative predictive values of 30% and 98%, respectively, for the identification of patients with bacteremia. Had the model been applied to patients in this study, the number of blood cultures performed would have decreased by 61%, but 11% of the positive cultures would have been missed. In short, this model can predict MAC bacteremia and can potentially guide the use of mycobacterial blood cultures.

摘要

在人类免疫缺陷病毒晚期感染病例中,分枝杆菌血培养常用于诊断鸟分枝杆菌复合群(MAC)的播散性感染。然而,目前尚无关于此类培养物使用的前瞻性验证指南。在本研究中,开发了一个用于预测MAC菌血症的两部分模型,然后进行了前瞻性验证。首先,使用CD4 +细胞计数≤50/μL来预测菌血症。然后,在CD4 +细胞≤50/μL的患者中,使用前3个月内超过30天有发热记录、血细胞比容<30%或血清白蛋白浓度<3.0 g/dL来预测菌血症。该模型识别菌血症患者的灵敏度为89%,阳性预测值和阴性预测值分别为30%和98%。如果将该模型应用于本研究中的患者,进行的血培养数量将减少61%,但会漏检11%的阳性培养物。简而言之,该模型可以预测MAC菌血症,并有可能指导分枝杆菌血培养的使用。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验