G G Alvarez1, E Sabri, D Ling, D W Cameron, G Maartens, D Wilson. 1. Ottawa Hospital Research Institute, University of Ottawa, Division of Respirology and Infectious Diseases, The Ottawa Hospital, Ottawa, Ontario, Canada. galvarez@ohri.ca
Abstract
SETTING: Improved diagnostic algorithms for sputum smear-negative tuberculosis (SNTB) are needed to address the dramatic increase in SNTB in regions with high human immunodeficiency virus (HIV) prevalence. OBJECTIVE: To determine whether the addition of C-reactive protein (CRP) to a prediction model using simple clinical criteria improves the diagnosis of SNTB among mostly antiretroviral-naïve adult HIV TB suspects in an out-patient setting. DESIGN: A multiple logistic regression model was derived from a database of 228 HIV patients to predict the risk of SNTB using data from a previous prospective study. RESULTS: The derived model demonstrated that male sex, night sweats, fever, low body mass index and anemia increased the probability of having SNTB. CRP improved the accuracy of the model (without CRP, area under the curve [AUC] 0.75, 95%CI 0.68-0.81 vs. model with CRP, AUC 0.81, 95%CI 0.76-0.87, P = 0.0014) to predict SNTB. Using reclassification tables, CRP correctly reclassified 27.9% of the patients (net reclassification improvement, P = 0.0005) into higher or lower risk categories. The strongest effect was seen in the reclassification improvement among patients with no TB, which was 20.6% (P = 0.0023). CONCLUSION: CRP improved the performance of the prediction model in the diagnosis of SNTB in HIV patients, and may play a role in ruling out SNTB in this population. Prospective validation of this model is needed.
SETTING: Improved diagnostic algorithms for sputum smear-negative tuberculosis (SNTB) are needed to address the dramatic increase in SNTB in regions with high human immunodeficiency virus (HIV) prevalence. OBJECTIVE: To determine whether the addition of C-reactive protein (CRP) to a prediction model using simple clinical criteria improves the diagnosis of SNTB among mostly antiretroviral-naïve adult HIVTB suspects in an out-patient setting. DESIGN: A multiple logistic regression model was derived from a database of 228 HIVpatients to predict the risk of SNTB using data from a previous prospective study. RESULTS: The derived model demonstrated that male sex, night sweats, fever, low body mass index and anemia increased the probability of having SNTB. CRP improved the accuracy of the model (without CRP, area under the curve [AUC] 0.75, 95%CI 0.68-0.81 vs. model with CRP, AUC 0.81, 95%CI 0.76-0.87, P = 0.0014) to predict SNTB. Using reclassification tables, CRP correctly reclassified 27.9% of the patients (net reclassification improvement, P = 0.0005) into higher or lower risk categories. The strongest effect was seen in the reclassification improvement among patients with no TB, which was 20.6% (P = 0.0023). CONCLUSION:CRP improved the performance of the prediction model in the diagnosis of SNTB in HIVpatients, and may play a role in ruling out SNTB in this population. Prospective validation of this model is needed.
Authors: F C Semitala; L H Chaisson; S den Boon; N Walter; A Cattamanchi; M Awor; J Katende; L Huang; M Joloba; H Albert; M R Kamya; J L Davis Journal: Public Health Action Date: 2015-05-08
Authors: P K Drain; L Mayeza; P Bartman; R Hurtado; P Moodley; S Varghese; G Maartens; G G Alvarez; D Wilson Journal: Int J Tuberc Lung Dis Date: 2014-01 Impact factor: 2.373
Authors: Christina Yoon; J Lucian Davis; Laurence Huang; Conrad Muzoora; Helen Byakwaga; Colin Scibetta; David R Bangsberg; Payam Nahid; Fred C Semitala; Peter W Hunt; Jeffrey N Martin; Adithya Cattamanchi Journal: J Acquir Immune Defic Syndr Date: 2014-04-15 Impact factor: 3.731
Authors: Simon C Mendelsohn; Andrew Fiore-Gartland; Denis Awany; Humphrey Mulenga; Stanley Kimbung Mbandi; Michèle Tameris; Gerhard Walzl; Kogieleum Naidoo; Gavin Churchyard; Thomas J Scriba; Mark Hatherill Journal: EClinicalMedicine Date: 2022-03-05