Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm
| dc.contributor.advisor | Maartens, Gary | |
| dc.contributor.advisor | Sinxadi Phumla | |
| dc.contributor.author | Griesel, Rulan | |
| dc.date.accessioned | 2019-05-10T11:00:46Z | |
| dc.date.available | 2019-05-10T11:00:46Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-05-09T13:21:48Z | |
| dc.description.abstract | Background The WHO algorithm for the diagnosis of tuberculosis in seriously ill HIV-infected patients lacks a firm evidence base. We aimed to develop a clinical prediction rule for the diagnosis of tuberculosis and to determine the diagnostic utility of the Xpert MTB/RIF assay in seriously ill HIV-infected patients. Methods We conducted a prospective study among HIV-infected inpatients with any cough duration and WHO-defined danger signs. Culture-positive tuberculosis from any site was the reference standard. A priori selected variables were assessed for univariate associations with tuberculosis. The most predictive variables were assessed in a multivariate logistic regression model and used to establish a clinical prediction rule for diagnosing tuberculosis. Results We enrolled 484 participants: median age 36 years, 65·5% female, median CD4 count 89 cells/μL, and 35·3% on antiretroviral therapy. Tuberculosis was diagnosed in 52·7% of participants. The c-statistic of our clinical prediction rule (variables: cough ≥14 days, unable to walk unaided, temperature >39oC, chest radiograph assessment, haemoglobin, and white cell count) was 0·811 (95%CI 0·802, 0·819). The classic tuberculosis symptoms (fever, night sweats, weight loss) added no discriminatory value in diagnosing tuberculosis. Xpert MTB/RIF assay sensitivity was 86·3% and specificity was 96·1%. Conclusion Our clinical prediction rule had good diagnostic utility for tuberculosis among seriously ill HIV-infected inpatients. Xpert MTB/RIF assay, incorporated into the updated 2016 WHO algorithm, had high sensitivity and specificity in this population. Our findings could facilitate improved diagnosis of tuberculosis among seriously ill HIV-infected inpatients in resource-constrained settings. | |
| dc.identifier.apacitation | Griesel, R. (2018). <i>Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm</i>. (). ,Faculty of Health Sciences ,Department of Medicine. Retrieved from http://hdl.handle.net/11427/30006 | en_ZA |
| dc.identifier.chicagocitation | Griesel, Rulan. <i>"Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm."</i> ., ,Faculty of Health Sciences ,Department of Medicine, 2018. http://hdl.handle.net/11427/30006 | en_ZA |
| dc.identifier.citation | Griesel, R. 2018. Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm. . ,Faculty of Health Sciences ,Department of Medicine. http://hdl.handle.net/11427/30006 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Griesel, Rulan AB - Background The WHO algorithm for the diagnosis of tuberculosis in seriously ill HIV-infected patients lacks a firm evidence base. We aimed to develop a clinical prediction rule for the diagnosis of tuberculosis and to determine the diagnostic utility of the Xpert MTB/RIF assay in seriously ill HIV-infected patients. Methods We conducted a prospective study among HIV-infected inpatients with any cough duration and WHO-defined danger signs. Culture-positive tuberculosis from any site was the reference standard. A priori selected variables were assessed for univariate associations with tuberculosis. The most predictive variables were assessed in a multivariate logistic regression model and used to establish a clinical prediction rule for diagnosing tuberculosis. Results We enrolled 484 participants: median age 36 years, 65·5% female, median CD4 count 89 cells/μL, and 35·3% on antiretroviral therapy. Tuberculosis was diagnosed in 52·7% of participants. The c-statistic of our clinical prediction rule (variables: cough ≥14 days, unable to walk unaided, temperature >39oC, chest radiograph assessment, haemoglobin, and white cell count) was 0·811 (95%CI 0·802, 0·819). The classic tuberculosis symptoms (fever, night sweats, weight loss) added no discriminatory value in diagnosing tuberculosis. Xpert MTB/RIF assay sensitivity was 86·3% and specificity was 96·1%. Conclusion Our clinical prediction rule had good diagnostic utility for tuberculosis among seriously ill HIV-infected inpatients. Xpert MTB/RIF assay, incorporated into the updated 2016 WHO algorithm, had high sensitivity and specificity in this population. Our findings could facilitate improved diagnosis of tuberculosis among seriously ill HIV-infected inpatients in resource-constrained settings. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PY - 2018 T1 - Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm TI - Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm UR - http://hdl.handle.net/11427/30006 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/30006 | |
| dc.identifier.vancouvercitation | Griesel R. Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm. []. ,Faculty of Health Sciences ,Department of Medicine, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/30006 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Medicine | |
| dc.publisher.faculty | Faculty of Health Sciences | |
| dc.title | Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MMed. (Clinical Pharmacology) |