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      Development of a Standardized Screening Rule for Tuberculosis in People Living with HIV in Resource-Constrained Settings: Individual Participant Data Meta-analysis of Observational Studies

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          Abstract

          Haileyesus Getahun and colleagues report the development of a simple, standardized tuberculosis (TB) screening rule for resource-constrained settings, to identify people living with HIV who need further investigation for TB disease.

          Abstract

          Background

          The World Health Organization recommends the screening of all people living with HIV for tuberculosis (TB) disease, followed by TB treatment, or isoniazid preventive therapy (IPT) when TB is excluded. However, the difficulty of reliably excluding TB disease has severely limited TB screening and IPT uptake in resource-limited settings. We conducted an individual participant data meta-analysis of primary studies, aiming to identify a sensitive TB screening rule.

          Methods and Findings

          We identified 12 studies that had systematically collected sputum specimens regardless of signs or symptoms, at least one mycobacterial culture, clinical symptoms, and HIV and TB disease status. Bivariate random-effects meta-analysis and the hierarchical summary relative operating characteristic curves were used to evaluate the screening performance of all combinations of variables of interest. TB disease was diagnosed in 557 (5.8%) of 9,626 people living with HIV. The primary analysis included 8,148 people living with HIV who could be evaluated on five symptoms from nine of the 12 studies. The median age was 34 years. The best performing rule was the presence of any one of: current cough (any duration), fever, night sweats, or weight loss. The overall sensitivity of this rule was 78.9% (95% confidence interval [CI] 58.3%–90.9%) and specificity was 49.6% (95% CI 29.2%–70.1%). Its sensitivity increased to 90.1% (95% CI 76.3%–96.2%) among participants selected from clinical settings and to 88.0% (95% CI 76.1%–94.4%) among those who were not previously screened for TB. Negative predictive value was 97.7% (95% CI 97.4%–98.0%) and 90.0% (95% CI 88.6%–91.3%) at 5% and 20% prevalence of TB among people living with HIV, respectively. Abnormal chest radiographic findings increased the sensitivity of the rule by 11.7% (90.6% versus 78.9%) with a reduction of specificity by 10.7% (49.6% versus 38.9%).

          Conclusions

          Absence of all of current cough, fever, night sweats, and weight loss can identify a subset of people living with HIV who have a very low probability of having TB disease. A simplified screening rule using any one of these symptoms can be used in resource-constrained settings to identify people living with HIV in need of further diagnostic assessment for TB. Use of this algorithm should result in earlier TB diagnosis and treatment, and should allow for substantial scale-up of IPT.

          Please see later in the article for the Editors' Summary

          Editors' Summary

          Background

          In 2009, 1.7 million people died from tuberculosis (TB)—equating to 4,700 deaths a day—including 380,000 people living with HIV. TB remains the most common cause of death in people living with HIV and compared to people without HIV, people living with HIV are more than 20 times more likely to develop TB. Furthermore, TB infection may occur at any stage of HIV disease and is often the initial presentation of underlying HIV infection. Without antiretroviral treatment, up to 50% of people living with HIV who are diagnosed with TB die during the 6–8 months of TB treatment.

          Although antiretroviral treatment can reduce the incidence of TB both at the individual and population level, people living with HIV on antiretroviral treatment still have higher TB incidence rates and a higher risk of dying from TB. Therefore, the World Health Organization recommends regular screening for active TB disease in all people living with HIV, so those identified as having active TB disease can be provided with appropriate treatment, and isoniazid preventive therapy (to help mitigate TB morbidity, mortality, and transmission) can be given to vulnerable individuals who do not yet have active TB.

          Why Was This Study Done?

          There is currently no internationally accepted evidence-based tool to screen for TB in people living with HIV—a serious gap given that the presenting signs and symptoms of TB in people living with HIV are different from those in people without HIV. Therefore, the researchers aimed to develop a simple, standardized TB screening rule for resource-constrained settings, on the basis of the best available evidence that would adequately distinguish between people living with HIV who are very unlikely to have TB from those who require further investigation for TB disease.

          What Did the Researchers Do and Find?

          The researchers selected 12 studies that met their strict criteria, then asked the authors of these studies for primary data so that they could map individual-level data to identify five symptoms common to most studies. Using a statistical model, the researchers devised 23 screening rules derived from these five symptoms and used meta-analysis methods (bivariate random-effects meta-analysis) and the association of study-level and individual-level correlates (hierarchical summary relative operating characteristic curves) to evaluate the sensitivity and specificity of each tool used in each individual study.

          The authors of the selected studies were able to provide data for 29,523 participants, of whom 10,057 were people living with HIV. The dataset included 9,626 people living with HIV who had TB screening and sputum culture performed, of which 8,148 individuals could be evaluated on the five symptoms of interest from nine of 12 studies. TB disease was diagnosed in 5.8% of people living with HIV and the best performing rule was the presence of any one of the following: current cough (any duration), fever, night sweats, or weight loss. The overall sensitivity of the rule was 78.9% and the specificity was 49.6%. However, the sensitivity of the rule increased to 90.1% among participants selected from clinical settings and to 88.0% among those who were not previously screened for TB.

          What Do These Findings Mean?

          The results of this study suggest that in resource-constrained settings, the absence of current cough, fever, night sweats, and weight loss (all inclusive) can identify those people living with HIV who have a low probability of having TB disease. Furthermore, any one of these symptoms can be used in resource-constrained settings to identify people living with HIV who are in need of further diagnostic assessment for TB.

          Despite the limitations of the methodology used in this study, until there are evidence-based and internationally recommended guidelines for the diagnosis and treatment of TB in people living with HIV, use of the algorithm developed and presented in this study could result in earlier TB diagnosis and treatment for people living with HIV and could help to substantially scale-up isoniazid preventive therapy.

          Additional Information

          Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000391.

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          Most cited references24

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          A unification of models for meta-analysis of diagnostic accuracy studies.

          Studies of diagnostic accuracy require more sophisticated methods for their meta-analysis than studies of therapeutic interventions. A number of different, and apparently divergent, methods for meta-analysis of diagnostic studies have been proposed, including two alternative approaches that are statistically rigorous and allow for between-study variability: the hierarchical summary receiver operating characteristic (ROC) model (Rutter and Gatsonis, 2001) and bivariate random-effects meta-analysis (van Houwelingen and others, 1993), (van Houwelingen and others, 2002), (Reitsma and others, 2005). We show that these two models are very closely related, and define the circumstances in which they are identical. We discuss the different forms of summary model output suggested by the two approaches, including summary ROC curves, summary points, confidence regions, and prediction regions.
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            Refining clinical diagnosis with likelihood ratios.

            Likelihood ratios can refine clinical diagnosis on the basis of signs and symptoms; however, they are underused for patients' care. A likelihood ratio is the percentage of ill people with a given test result divided by the percentage of well individuals with the same result. Ideally, abnormal test results should be much more typical in ill individuals than in those who are well (high likelihood ratio) and normal test results should be most frequent in well people than in sick people (low likelihood ratio). Likelihood ratios near unity have little effect on decision-making; by contrast, high or low ratios can greatly shift the clinician's estimate of the probability of disease. Likelihood ratios can be calculated not only for dichotomous (positive or negative) tests but also for tests with multiple levels of results, such as creatine kinase or ventilation-perfusion scans. When combined with an accurate clinical diagnosis, likelihood ratios from ancillary tests improve diagnostic accuracy in a synergistic manner.
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              HIV infection and multidrug-resistant tuberculosis: the perfect storm.

              Multidrug-resistant (MDR) tuberculosis (TB) has emerged as a global epidemic, with ~425,000 new cases estimated to occur annually. The global human immunodeficiency virus (HIV) infection epidemic has caused explosive increases in TB incidence and may be contributing to increases in MDR-TB prevalence. We reviewed published studies and available surveillance data evaluating links between HIV infection and MDR-TB to quantify convergence of these 2 epidemics, evaluate the consequences, and determine essential steps to address these epidemics. Institutional outbreaks of MDR-TB have primarily affected HIV-infected persons. Delayed diagnosis, inadequate initial treatment, and prolonged infectiousness led to extraordinary attack rates and case-fatality rates among HIV-infected persons. Whether this sequence occurs in communities is less clear. MDR-TB appears not to cause infection or disease more readily than drug-susceptible TB in HIV-infected persons. HIV infection may lead to malabsorption of anti-TB drugs and acquired rifamycin resistance. HIV-infected patients with MDR-TB have unacceptably high mortality; both antiretroviral and antimycobacterial treatment are necessary. Simultaneous treatment requires 6-10 different drugs. In HIV-prevalent countries, TB programs struggle with increased caseloads, which increase the risk of acquired MDR-TB. Surveillance data suggest that HIV infection and MDR-TB may converge in several countries. Institutional outbreaks, overwhelmed public health programs, and complex clinical management issues may contribute to the convergence of the MDR-TB and HIV infection epidemics. To forestall disastrous consequences, infection control, rapid case detection, effective treatment, and expanded program capacity are needed urgently.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                1549-1277
                1549-1676
                January 2011
                January 2011
                18 January 2011
                : 8
                : 1
                Affiliations
                [1 ]World Health Organization, Geneva, Switzerland
                [2 ]Thailand Ministry of Public Health - U.S. Centers for Disease Control and Prevention Collaboration, Nonthaburi, Thailand
                [3 ]United States Centers for Disease Control and Prevention, Atlanta, United States of America
                [4 ]Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [5 ]ZAMBART Project, University of Zambia Ridgeway Campus, Lusaka, Zambia
                [6 ]Aurum Institute for Health Research, Johannesburg, South Africa
                [7 ]Bill and Melinda Gates Foundation, Seattle, United States of America
                [8 ]Albert Einstein College of Medicine, New York, United States of America
                [9 ]Desmond Tutu HIV Center, University of Cape Town, South Africa
                [10 ]Department of Medicine, University of Cape Town, South Africa
                Harvard School of Public Health, United States of America
                Author notes

                ICMJE criteria for authorship read and met: HG WK CMH ELC HA KPC ADG GJC MK SS SDL RW GM RG AAD JKV. Agree with the manuscript's results and conclusions: HG WK CMH ELC HA KPC ADG GJC MK SS SDL RW GM RG AAD JKV. Designed the experiments/the study: HG CMH KPC GJC MK JKV. Analyzed the data: HG WK CMH KPC GJC SS AAD JKV. Collected data/did experiments for the study: HG ELC HA ADG GJC MK SS SDL RW GM JKV. Enrolled patients: ELC HA ADG GJC MK SS SDL RW GM. Wrote the first draft of the paper: HG JKV. Contributed to the writing of the paper: WK CMH ELC HA KPC ADG GJC MK SS SDL RW GM RG AAD JKV. Conducted the data retrieval and cleaning: WK. Designed the principal analytic method, supervised overall statistical analysis, and aided with interpretation of its results: CMH. Responsible for literature review to identify studies to be included in the meta-analysis: AAD. Member of the team to develop and finalize the data analysis plan: AAD.

                Article
                10-PLME-RA-4084R4
                10.1371/journal.pmed.1000391
                3022524
                21267059
                d2d93ab2-590e-4e68-bc60-34bc05248c24
                This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
                Page count
                Pages: 14
                Categories
                Research Article
                Public Health and Epidemiology/Global Health
                Public Health and Epidemiology/Health Policy
                Public Health and Epidemiology/Infectious Diseases
                Public Health and Epidemiology/Screening

                Medicine
                Medicine

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