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Subclinical tuberculosis among adults with HIV: clinical features and outcomes in a South African cohort
research-article
Author(s):
Kristina L. Bajema
1
,
,
Ingrid V. Bassett
2 ,
Sharon M. Coleman
3 ,
Douglas Ross
4 ,
Kenneth A. Freedberg
2 ,
Anna Wald
5 ,
Paul K. Drain
6
,
7
Publication date (Electronic):
5 January 2019
Journal:
BMC Infectious Diseases
Publisher:
BioMed Central
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Abstract
Background
Subclinical tuberculosis is an asymptomatic disease phase with important relevance to persons living with HIV. We describe the prevalence, clinical characteristics, and risk of mortality for HIV-infected adults with subclinical tuberculosis.
Methods
Untreated adults with HIV presenting for outpatient care in Durban, South Africa were screened for tuberculosis-related symptoms and had sputum tested by acid-fast bacilli smear and tuberculosis culture. Active tuberculosis and subclinical tuberculosis were defined as having any tuberculosis symptom or no tuberculosis symptoms with culture-positive sputum. We evaluated the association between tuberculosis disease category and 12-month survival using Cox regression, adjusting for age, sex, and CD4 count.
Results
Among 654 participants, 96 were diagnosed with active tuberculosis disease and 28 with subclinical disease. The median CD4 count was 68 (interquartile range 39–161) cells/mm 3 in patients with active tuberculosis, 136 (72–312) cells/mm 3 in patients with subclinical disease, and 249 (125–394) cells/mm 3 in those without tuberculosis disease ( P < 0.001). The proportion of smear positive cases did not differ significantly between the subclinical (29%) and active tuberculosis groups (14%, P 0.08). Risk of mortality was not increased in individuals with subclinical tuberculosis relative to no tuberculosis (adjusted hazard ratio 0.84, 95% confidence interval 0.26–2.73).
Related collections
Most cited references23
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Antiretroviral Therapy for the Prevention of HIV-1 Transmission.
Myron S Cohen, Ying Q Chen, Marybeth McCauley … (2016)
An interim analysis of data from the HIV Prevention Trials Network (HPTN) 052 trial showed that antiretroviral therapy (ART) prevented more than 96% of genetically linked infections caused by human immunodeficiency virus type 1 (HIV-1) in serodiscordant couples. ART was then offered to all patients with HIV-1 infection (index participants). The study included more than 5 years of follow-up to assess the durability of such therapy for the prevention of HIV-1 transmission.
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- Abstract: found
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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
Introduction By the end of 2009, an estimated 33 million people were living with HIV, the vast majority in sub-Saharan Africa and Asia. Tuberculosis (TB) remains the most common cause of death in people living with HIV. Compared to people without HIV, people living with HIV have a more than 20-fold increased risk of developing TB [1]. TB disease may occur at any stage of HIV disease and is frequently the first recognized presentation of underlying HIV infection [2],[3]. Without antiretroviral treatment (ART), up to 50% of people living with HIV who are diagnosed with TB die during the 6–8 mo of TB treatment [4]–[6]. This risk increases to 72%–98% among those with multi-drug (MDR) or extensively drug-resistant (XDR) TB [7],[8]. Although ART can reduce the incidence of TB both at individual [9] and population [10] levels, people living with HIV on ART still have higher TB incidence rates and a higher risk of dying from TB [11]. Routine TB screening offers the opportunity to diagnose and promptly treat TB disease, and to identify those without TB disease who may be eligible for TB preventive therapy [12]. The use of TB preventive therapy can reduce TB incidence and is therefore of considerable benefit to patients [13]. For these reasons, the World Health Organization (WHO) recommends regular screening for active TB disease of all people living with HIV and providing either treatment for active disease or isoniazid preventive therapy (IPT) to mitigate TB morbidity, mortality, and transmission [14]. However, in 2009, of the estimated 33 million people living with HIV, only 1.7 million (5%) were screened for TB, and about 85,000 (0.2%) were offered IPT [15]. Currently there is no internationally accepted evidence-based tool to screen for TB in people living with HIV. Several studies have shown that the presenting signs and symptoms of TB in people living with HIV are different from those in people without HIV to diagnose TB; for example, many people living with HIV who have culture-confirmed TB do not report having a prolonged cough, which is one of the standard TB screening questions used by national TB control programs globally [16]. Moreover, the most widely available TB diagnostic tests such as smear microscopy and chest radiography perform poorly among people living with HIV; because most people living with HIV and TB have either sputum acid-fast bacilli (AFB) smear negative pulmonary or extrapulmonary TB [17]. We conducted an individual participant data meta-analysis of published and unpublished studies to develop a simple, standardized TB screening rule for resource-constrained settings that will adequately separate patients into two groups: (1) those for whom TB is reliably excluded, and IPT and ART, if indicated, can be initiated; and (2) those who require further investigation for TB disease. We describe the results of this meta-analysis and propose an algorithm for TB screening among people living with HIV in resource-constrained settings. Methods We proceeded through several steps. First, we prospectively enumerated criteria for studies to be included in our meta-analysis. Second, we searched for and selected studies that met these criteria. Third, we sought primary data from investigators and mapped individual-level data to common symptoms. Fourth, we identified five symptoms available in most studies and, within each study, computed the sensitivity and specificity of 23 screening rules derived from these five symptoms. Finally, we used meta-analysis methods to estimate the performance of all 23 rules, as well as the association of study-level and individual-level correlates with performance. Inclusion of Studies We defined studies as being eligible for inclusion in this analysis if they met the following criteria: (1) collected sputum specimens from people living with HIV regardless of signs or symptoms; (2) used mycobacterial culture of at least one specimen to diagnose TB; and (3) collected data about signs and symptoms. Search Strategy and Selection of Studies To identify studies eligible for the meta-analysis, we conducted a systematic literature review of studies related to TB screening among people living with HIV in June 2008 using PubMed and various combinations of the following keywords: “HIV,” “Tuberculosis,” “TB screening,” “Smear-negative TB,” “Sputum negative TB,” “TB case finding,” “Intensified TB case finding,” “Isoniazid prevention treatment, trial or therapy.” We also searched for abstracts presented at conferences organized by the International Union Against TB and Lung Diseases and the International AIDS Society between 2000–2008. No language restriction was placed on the search. We reviewed all retrieved titles and abstracts for relevance to the topic. The reference lists of retrieved studies were also reviewed to identify further studies that meet the eligibility criteria. In addition, recognized experts in the field were contacted to identify studies that were not available (e.g., unpublished) in the initial electronic search. Studies that involve concomitant HIV testing and mycobacterial culture on all patients are resource intensive and challenging to implement in countries with a high burden of TB and HIV. Therefore, we believe it is unlikely that eligible studies would have been completed but missed by our search strategy. We found 2,119 publications and reviewed all their abstracts. Using the criteria above, we selected 53 for review of the full text. Twenty-one articles included information on signs and symptoms for TB screening in people living with HIV. A total of 14 studies (six published and eight unpublished at the time of the search) met the inclusion criteria of our meta-analysis (Figure 1). The corresponding authors or principal investigators were contacted for all 14 studies to confirm that their studies met all the eligibility criteria. One unpublished dataset was excluded for not meeting the inclusion criteria after verification with the principal investigator, and another one was excluded because the investigators could not submit the data within the agreed timeframe. A total of 12 investigators (representing six published and six unpublished studies at the time of the search) provided de-identified individual patient data for inclusion in the primary meta-analysis within an agreed time framework (Table 1) [18]–[29]. In November 2010, immediately preceding manuscript publication, we re-ran the search strategy again to look for additional studies that were reported since the initial search and should have been included in the meta-analysis. The search found seven eligible studies, of which all except one [30] were included in our meta-analysis as unpublished datasets. 10.1371/journal.pmed.1000391.g001 Figure 1 Search strategy and studies included in the meta-analysis (PRISMA flow diagram). 10.1371/journal.pmed.1000391.t001 Table 1 Summary of studies included in the meta-analysis. Reference Study Population Characteristics (Setting, Continent, n Samples, Culture Method Used) Sample Size PLTB/PLHIVa (%) PLTB/PLHIV (%) with Data on the Five Symptoms Ayles et al. 2009 Adults of more than 15 y of age sampled from one rural and one urban communities in Zambia (community, sub-Saharan Africa, 1 LJ and MGIT). 8,044 43/2,253 (1.9) 41/2,145 (1.9) Corbett et al. 2010b Random sample of adults in 46 previously enumerated neighbourhoods in the high density suburbs of Harare, Zimbabwe (community, sub-Saharan Africa, 3 LJ). 10,079 31/1,841 (1.7) 31/1,834 (1.7) Cain et al. 2010 PLHIV from 8 outpatient clinics in Cambodia, Thailand, and Vietnam who were enrolled regardless of signs or symptoms suggestive of TB (clinical, Southeast Asia, 3 MGIT and LJ). 1,748 267/1,724 (15.5) 267/1,721 (15.5) Day et al. 2006 Employees of a gold mining company first attending the TB preventive therapy clinic in South Africa (miners, sub-Saharan Africa, 2 LJ). 1,093 32/991 (3.2) 0/0 (–) Corbett et al. 2007b Employees of 22 small and medium-sized enterprises in Zimbabwe (community, sub-Saharan Africa, 3 LJ). 4,668 3/797 (0.4) 3/797 (0.4) Lewis et al. 2009b All consenting employees of a gold mining indusry who undergo annual medical examinations in an occupational health centre in South Africa (miners, sub-Saharan Africa, 2 LJ). 1,955 18/560 (3.2) 18/560 (3.2) Shah et al. 2009 All newly diagnosed HIV-positive clients of at least 18 y old from the VCT Clinic in a large referral hospital in Addis Ababa, Ethiopia (clinical, sub-Saharan Africa, 1 LJ). 453 27/427 (6.3) 22/357 (6.2) Kimerling et al. 2002 PLHIV of at least 15 y of age and enrolled in an HIV home-based care service in Phnom Penh, Cambodia (community, Southeast Asia, 1 LJ). 441 36/393 (9.2) 36/393 (9.2) Lawn et al. 2009 PLHIV with more than 18 y of age who were referred to a community-based ART service in South Africa (clinical, sub-Saharan Africa, 2–4 MGIT). 235 58/226 (25.7) 57/218 (26.1) Wood et al. 2007 Randomly sampled and consenting adults living in shacks in a high-density residential area in South Africa (community, Sub-Saharan Africa, 4 MGIT). 174 12/163 (7.4) 0/0 (–) Mohammed et al. 2004 PLHIV with WHO clinical stage 3 or 4 disease referred for possible participation in a trial of TB-preventive therapy in 3 hospital-based adult HIV clinics in South Africa (clinical, sub-Saharan Africa, 1 BACTEC). 129 10/128 (7.8) 0/0 (–) Chheng et al. 2008 All consenting participants of more than 19 y old who were tested for HIV in a Voluntary Counseling Center and referred for TB screening in Cambodia (clinical, Southeast Asia, 3 LJ). 504 20/123 (16.3) 20/123 (16.3) Total 29,523 557/9,626 (5.8) 495/8,148 (6.1) a The following patients were excluded: (1) patients who were on TB treatment or prophylaxis at enrolment; (2) patients who were smear positive, but culture grew non- tuberculosis mycobacterium (NTM); and (3) patients who were smear positive, but culture negative. b Patients were previously screened for TB before enrolment into the study. LJ, Lowenstein-Jensen culture medium; MGIT, Mycobacterial Growth Indicator Tube culture system; PLHIV, persons living with human immunodeficiency virus; PLTB, persons with tuberculosis disease; VCT, voluntary counselling and testing for HIV. Investigators for all included studies signed a data sharing and confidentiality agreement, and agreed to a data management, analysis, and publication plan. During design and analysis phases of the meta-analysis, the investigators of the studies and data managers of the included studies held multiple discussions by email, by teleconference, and in person in Geneva, Switzerland and Atlanta, Georgia, United States. Data Abstraction and Management The list of variables from the most comprehensive dataset [20] was used to construct an initial master list of variables. All the variables from each study included in the meta-analysis were mapped to this master list. Principal investigators and data managers for the 12 studies worked with the meta-analysis investigators to ensure accurate mapping of data from the primary studies to the master variable list. In the end, the final dataset of the meta-analysis included 159 variables that appeared in at least two of the studies. We identified five symptoms common to most studies and limited the meta-analysis to the nine studies with substantially complete information for those five symptoms. Case Definitions We defined a TB patient as any person living with HIV and at least one specimen culture positive for Mycobacterium tuberculosis (MTB). We defined participants as having no TB if cultures were negative for MTB and participants were judged not to have TB on the basis of the original study definition of the investigators. We excluded from the analysis: (1) patients who were receiving treatment for TB (infection or disease) at enrolment; (2) patients who were AFB smear positive, but whose culture grew non-tuberculous mycobacteria; and (3) patients who were AFB smear positive or scanty, but sputum culture negative. Sources of Study Heterogeneity All studies were reviewed to identify study-level characteristics that could substantially impact the findings of the meta-analysis. Two studies were conducted exclusively among gold miners living in South Africa [21],[23], a population that may not be broadly generalizable, because of its demographics, its high prevalence of non-TB illnesses (e.g., silicosis) that can produce cough, and the practice of annual TB radiological screening. Five studies [18],[19],[22],[25],[27] were conducted among individuals drawn from a community setting through prevalence surveys, which may lead to enrolment of patients with a different spectrum of TB and HIV disease than would be found among patients seeking care. Three studies [19],[22],[23] involved participants who were previously screened for TB or who had had access to routine TB screening before being enrolled into the studies. Finally, three studies exclusively used liquid media to culture specimens [26]–[28], two studies used both solid and liquid media [18],[20], and seven studies exclusively used solid media (Table 1). Liquid media have substantially increased sensitivity for growing MTB, particularly in patients with low levels of TB bacilli, as would be expected in a population of people living with HIV being screened for TB [31]. Studies that used liquid media, therefore, would have improved ability to classify patients correctly into those who have TB and those who do not. We explored the impact of these factors on the performance of the screening algorithms and analyzed subsets of the final dataset grouped according to these characteristics. Data Analysis We compared characteristics of patients with TB to those of patients without TB to derive a standardized rule for TB screening among people living with HIV. The goal of TB screening is to divide the population of people living with HIV into two groups: (1) those who do not have TB; and (2) those who need further evaluation for the diagnosis of TB (i.e., TB suspects). We restricted our analysis to clinical symptoms that could be readily assessed at any level of the health system and that were asked about in all studies: current cough (C), haemoptysis (H), fever (F), night sweats (S), and weight loss (W). Using the four studies that included chest radiography data [20],[23],[24],[26], we also evaluated the impact of adding abnormal chest radiography findings to the TB screening rule. Only observations with no missing data for the symptoms of interest were included in the analysis. We considered “1-of-n” rules as candidates for screening for TB that could best classify patients into two groups (not TB and suspected TB) with high sensitivity [20]. The “1” represents the minimum number of symptoms that must be present in an individual to be classified as a suspected TB patient and the “n” represents the number of symptom(s) in a given rule. For example, a “1-of-3” rule could be a set of symptoms such as current cough, fever, and weight loss, abbreviated here as CFW. An individual with at least one symptom specified in this particular rule would be classified as a TB suspect; an individual without any of these symptoms would be classified as not having TB. We considered all combinations of the five candidate symptoms except for combinations that include both current cough and haemoptysis, yielding a total of 23 candidate rules: two 1-of-4 rules (CFSW, HFSW), seven 1-of-3 rules (CFS, CFW, CSW, HFS, HFW, HSW, FSW), nine 1-of-2 rules (CF, CS, CW, HF, HS, HW, FS, FW, SW), and five 1-of-1 rules (C, H, F, S, W) (see also Table 3.) The analysis with abnormal chest radiographic findings (X) considered these 23 rules, each augmented with this additional sign (e.g., CFSWX). Other analyses have considered m-of-n rules with m>1 [20]. These rules cannot exceed the sensitivity of 1-of-n rules. Suppose that a positive screen requires the presence of at least two symptoms out of current cough, fever, and night sweats, the number of true positives for this 2-of-3 rule will not be greater than the number of true positives from the corresponding 1-of-3 rules. Because our aim in this analysis was to identify the most sensitive rule, we did not include rules of this kind in our analysis. We applied two closely related methods for cross-study analysis of sensitivity and specificity of these 23 candidate screening rules: bivariate random-effects meta-analysis (BREMA) and the hierarchical summary relative operating characteristic (HSROC) curve [32],[33]. BREMA jointly models sensitivity and specificity while accommodating between-study heterogeneity, and HSROC models tradeoffs between sensitivity and specificity across study populations. Both methods can be unified in the same model. In addition to sensitivity and specificity, we calculated predictive value negative and likelihood ratio negative of each candidate rule [34]. Our goal was to identify a combination of symptoms that achieved the highest possible sensitivity and the lowest possible negative likelihood ratio for ruling out TB disease (without any predetermined cut-off points). To further understand between-study heterogeneity and other factors associated with the diagnostic performance of the most sensitive rule, we analyzed several study-level predictors (setting, prior screening of study participants, culture medium used, and geographic region) and participant-level predictors (age, gender, CD4 T-lymphocyte cell count [CD4], and abnormal chest radiographic findings). Our analytic methods produced odds ratios that reflect the magnitude of association between each factor and the probability of correctly identifying persons with TB (sensitivity) or without TB (specificity). For a range of TB prevalence values, we calculated the negative predictive values at levels of each covariate. We calculated the ratio of the number of patients that screen positive but who actually have no TB (false positives) and hence unnecessarily require additional TB diagnostic evaluation (e.g., culture) to the number of patients that screen positive and actually have TB (true positives), which is referred to as the number needed to screen. We calculated this ratio for different rules using a theoretical population of 1,000 people living with HIV with different levels of TB prevalence. This ratio provides proxy information similar to a marginal cost-effectiveness analysis for different screening rules and it helps quantify how much a health program would need to invest (as measured by additional diagnostic tests) for every patient with TB identified through the screening rule [35]. Each observation with a missing covariate value was omitted from analysis of that covariate. BREMA models were fitted using SAS procedure Glimmix (SAS 9.22, SAS Institute), and further calculations were performed in R (R 2.10.1, R Development Core Team). Ethical Review All data collection included in the meta-analysis was approved by institutional ethical review boards at the respective institutions during the original study; if necessary, principal investigators requested additional approval from institutional review boards for the inclusion of the primary dataset in the meta-analysis. All data for the meta-analysis were provided completely de-identified. In the meta-analysis dataset, investigators were not able to link case records to individuals. Results Investigators provided data about 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 whom 8,148 could be evaluated on the five symptoms of interest from nine of 12 studies (Figure 2). 10.1371/journal.pmed.1000391.g002 Figure 2 Flow chart of study participants included in the individual patient data meta-analysis. Most patients (77% [7,386/9,626]) were from sub-Saharan Africa; the rest were from Southeast Asian countries. The median age was 34 y (interquartile range [IQR], 27–41 y). Of the 9,626 patients with HIV in the 12 studies, CD4 cell count information was available for 3,489 (36%) and chest radiography information for 3,903 (41%). The median CD4 count was 248 cells/µl (IQR, 107–409). The overall prevalence of TB disease was 5.8% (557/9,626), ranging across studies from 0.4% to 25.7% (Table 1). More than half of TB patients (52% [288/557]) had sputum smear negative pulmonary TB, whereas 39% (218/557) had sputum smear positive pulmonary, and 5% (28/557) had exclusively extrapulmonary TB. The anatomic site of TB was not specified in 4% (23/557) of patients. Table 2 summarizes the distribution of common variables, and Table S1 summarizes how each question was actually asked in each study. Because duration of cough was included in many studies but was asked about in different ways, we were able to analyze data using three different cough variables: cough in the past 4 wk (information available for 39.3% of participants); cough lasting for 2 wk or more (information available for 47.1%); and cough present in the last 24 h, which is referred to as “current cough” (information available for 89.6%). 10.1371/journal.pmed.1000391.t002 Table 2 Characteristics of participants with and without TB for variables included in the analysis. Characteristic All PLHIV (n = 9,626) PLHIV with Data on the Five Symptoms (n = 8,148) No TB Disease (n = 9,069), n (%) TB Disease (n = 557), n (%) TB Disease (n = 7,653), n (%) TB Disease (n = 495) n (%) Origin of patient Sub-Saharan Africa 7,152 (78.9) 234 (42.0) 5,739 (75.0) 172 (34.8) Southeast Asia 1,917 (21.1) 323 (58.0) 1,914 (25.0) 323 (65.2) Setting Clinical 2,246 (24.8) 382 (68.5) 2,053 (26.8) 366 (73.9) Community 5,322 (58.7) 125 (22.4) 5,058 (66.1) 111 (22.4) Miners 1,501 (16.5) 50 (9.0) 542 (7.1) 18 (3.6) Sex Male 4,957 (54.7) 356 (63.9) 3,811 (49.8) 309 (62.4) Female 4,111 (45.3) 201 (36.1) 3,841 (50.2) 186 (37.6) Missing or not recorded 1 (0.0) 0 (0.0) 1 (0.0) 0 (0.0) Median age (IQR), n = 8,633 (7,286) 34 (27–41) 33 (28–40) 33 (27–40) 32 (28–39) Median CD4+ count (IQR), n = 3,489 (2,409) 268 (126–427) 106 (38–241) 229 (94–391) 94 (33–215) Cough in the past 4 wk Yes 1,439 (15.9) 303 (54.4) 1,270 (16.6) 278 (56.2) No 1,909 (21.0) 129 (23.2) 1,067 (13.9) 110 (22.2) Missing or not recorded 5,721 (63.1) 125 (22.4) 5,316 (69.5) 107 (21.6) Cough lasting for 2 wk or more Yes 957 (10.6) 197 (35.4) 848 (11.1) 177 (35.8) No 3,093 (34.1) 288 (51.7) 2,046 (26.7) 260 (52.5) Missing or not recorded 5,019 (55.3) 72 (12.9) 4,759 (62.2) 58 (11.7) Haemoptysis Yes 543 (6.0) 60 (10.8) 523 (6.8) 58 (11.7) No 8,509 (71.4) 495 (88.9) 7,130 (93.2) 437 (88.3) Missing or not recorded 17 (0.2) 2 (0.4) 0 (0.0) 0 (0.0) Current cough or cough in the past 24 h Yes 1,625 (17.9) 274 (49.2) 1,530 (20.0) 260 (52.5) No 6,474 (71.4) 250 (44.9) 6,123 (80.0) 235 (47.5) Missing or not recorded 970 (10.7) 33 (5.9) 0 (0.0) 0 (0.0) Current fever or fever in the past 4 wk Yes 1,801 (19.9) 294 (52.8) 1,669 (21.8) 280 (56.6) No 7,002 (77.2) 246 (44.2) 5,984 (78.2) 215 (43.4) Missing or not recorded 266 (2.9) 17 (3.0) 0 (0.0) 0 (0.0) Current night sweats or night sweats in the past 4 wk Yes 1,710 (18.9) 242 (43.4) 1,497 (19.6) 225 (45.4) No 7,329 (80.8) 313 (56.2) 6,156 (80.4) 270 (54.6) Missing or not recorded 30 (0.3) 2 (0.4) 0 (0.0) 0 (0.0) Current weight loss or weight loss in the past 4 wk Yes 2,434 (26.8) 333 (59.8) 2,258 (29.5) 308 (62.2) No 6,478 (71.4) 218 (39.1) 5,395 (70.5) 187 (37.8) Missing or not recorded 157 (1.7) 6 (1.1) 0 (0.0) 0 (0.0) Abnormal chest radiography Yes 581 (6.4) 271 (48.7) 294 (3.8) 239 (48.3) No 2,900 (32.0) 151 (27.1) 2,155 (28.1) 145 (29.3) Missing or not recorded 5,588 (61.6) 135 (24.2) 5,204 (68.0) 111 (22.4) Abnormal chest radiography consistent with TB Yes 377 (4.2) 227 (40.8) 261 (3.4) 209 (42.2) No 2,589 (28.5) 144 (25.8) 1,641 (21.4) 129 (26.1) Missing or not recorded 6,103 (67.3) 186 (33.4) 5,751 (75.2) 157 (31.7) Any one of current cough, fever, night sweats, or weight loss Yes 3,591 (39.6) 425 (76.3) 3,563 (46.6) 418 (84.4) No 4,090 (45.1) 77 (13.8) 4,090 (53.4) 77 (15.6) Not evaluable 1,388 (15.3) 55 (9.9) 0 (0.0) 0 (0.0) We analyzed the performance of individual and combinations of symptoms as screening rules using data from the 8,148 participants who could be evaluated based on the five candidate symptoms. Table 3 shows the diagnostic performance characteristics for the 23 candidate combinations of symptoms, sorted from highest sensitivity to lowest. The most sensitive rule was the presence of any one of the following symptoms: current cough, fever, night sweats, and weight loss (CFSW). The population-average sensitivity of this symptom combination was 78.9% (95% confidence interval [CI] 58.3%–90.9%) with the negative likelihood ratio of 0.426 (95% CI 0.349–0.520), which corresponds to a postscreening reduction in the probability of TB by 15%–20% [34]. 10.1371/journal.pmed.1000391.t003 Table 3 Diagnostic performance of 23 candidate 1-of-n rules. Rule Sensitivity (95% CI) Specificity (95% CI) LRN (95% CI) CFSW 78.9 (58.3–90.9) 49.6 (29.2–70.1) 0.426 (0.349–0.520) HFSW 75.7 (53.9–89.2)a 52.7 (31.8–72.7) 0.461 (0.391–0.544) CFW 74.0 (51.7–88.3)a 53.8 (32.8–73.6) 0.483 (0.416–0.561) CSW 73.4 (51.0–88.0) 53.8 (32.8–73.5) 0.494 (0.428–0.570) CFS 73.1 (50.6–87.9) 61.1 (39.7–79.0) 0.440 (0.382–0.506) HFW 70.6 (47.5–86.4) 57.5 (36.2–76.4) 0.511 (0.454–0.576) FSW 69.2 (45.9–85.6) 55.7 (34.5–75.0) 0.554 (0.497–0.617) HSW 68.1 (44.6–85.0) 58.7 (37.3–77.2) 0.544 (0.492–0.602) CW 65.3 (41.6–83.3) 60.3 (38.8–78.4) 0.576 (0.530–0.625) CF 65.0 (41.3–83.1) 68.6 (47.7–83.9) 0.510 (0.470–0.553) HFS 63.7 (39.9–82.3) 66.3 (45.2–82.4) 0.548 (0.509–0.589) FW 63.1 (39.3–81.9) 61.4 (40.0–79.1) 0.601 (0.560–0.644) SW 61.0 (37.2–80.5) 61.9 (40.5–79.5) 0.630 (0.594–0.669) CS 59.7 (35.9–79.6) 69.4 (48.7–84.4) 0.581 (0.551–0.613) HW 56.8 (33.3–77.6) 66.8 (45.7–82.8) 0.647 (0.620–0.675) FS 56.3 (32.8–77.3) 70.1 (49.6–84.9) 0.623 (0.598–0.649) HF 52.0 (29.2–74.1) 75.0 (55.6–87.7) 0.640 (0.620–0.660) W 49.3 (27.0–71.9) 71.1 (50.8–85.5) 0.712 (0.693–0.733) F 42.8 (22.2–66.3) 79.8 (62.4–90.4) 0.716 (0.695–0.738) HS 38.9 (19.5–62.6) 78.1 (59.9–89.5) 0.782 (0.753–0.813) C 38.5 (19.2–62.2) 81.8 (65.3–91.5) 0.753 (0.724–0.783) S 31.4 (14.8–54.6) 82.2 (65.9–91.7) 0.835 (0.780–0.893) H 5.9 (2.3–14.5) 94.4 (87.6–97.6) 0.996 (0.735–1.351) Sensitivity, specificity, and likelihood ratio negative (LRN) from the bivariate random-effects meta-analysis model. Rule is at least one of the indicated symptoms. C, current cough; H, haemoptysis; F, fever; S, sweats; W, weight loss. a p-Value >0.05 for the same sensitivity of the CFSW rule and the indicated rule. All specificities are significantly different from that of the CFSW rule. LRN, likelihood ratio negative. The nine included studies demonstrated significant between-study heterogeneity on both sensitivity (p<0.001) and specificity (p<0.001) of the rule CFSW (see also Figure 3). The bivariate graphic shows that six studies have study-level specificities below and three above the population average specificity. Furthermore, this rule has the highest-ranking sensitivity in eight of the nine included studies (Table S2). The hierarchical summary relative operating characteristic curves (Figure S1) show slightly better overall diagnostic performance of the rules CFS and CF, but our application requires the highest sensitivity possible, allowing for some tradeoffs with lower specificity. Figure 3 shows that three studies are outliers, and they represent studies of patients who were previously screened for TB or studies in which much of the population likely had previous TB screening (e.g., miners); this can modify the performance characteristics of the screening rule. 10.1371/journal.pmed.1000391.g003 Figure 3 Diagnostic performance of CFSW rule in the included studies. BREMA, bivariate random-effects meta-analysis; HSROC, hierarchical summary relative operating characteristic. The CFSW rule has sensitivity of 90.1% (95% CI 76.3%–96.2%) and 67.1% (95% CI 41.7%–85.3%) among participants selected from clinical and community settings, respectively. Similarly the sensitivity of the rule among those who had not been previously screened for TB was higher at 88.0% (95% CI 76.1%–94.4%) compared to those who had been screened for TB at 40.5% (95% CI 16.6%–69.9%). At the 95% confidence level, the sensitivity of this rule could not be statistically distinguished from the sensitivity of the rule that substitutes haemoptysis for current cough (HFSW, 75.7% sensitive [95% CI 53.9–89.2%]) or the rule that drops night sweats (CFW, 74.0% sensitive [51.7–88.3%]). All other rules had lower sensitivity. Regression analysis of study-level predictors revealed that studies in which TB screening was performed in clinical settings had 4.5 times the odds for a true-positive screening result compared to studies in which TB screening was performed in a community setting (95% CI 1.0–19.5). Studies of participants who had not previously been screened for TB had 10.8 times the odds for a true-positive screen (95% CI 2.4–47.8) compared with studies in which participants had previously been screened for TB. Participants with CD4 cell count <200 cells/µl had 6.4 times the odds of a true-positive screen (95% CI 2.9–14.2). Statistically significant predictors of true-negative results include prescreening, geographic region, participant age ≥33 y, CD4 cell count <200 cells/ml, and abnormal result on chest radiograph (Table 4). 10.1371/journal.pmed.1000391.t004 Table 4 Association of study-level and individual-level predictors with the diagnostic performance of CFSW rule. Predictors Sensitivity (95% CI) Specificity (95% CI) Study level Setting Community 1.0 Clinical 4.45 (1.02, 19.46)a 0.25 (0.06–1.01) Miners 0.25 (0.02–2.51) 4.07 (0.44–37.68) Screening Prescreened for TB 1.0 Not screened for TB 10.82 (2.45–47.78)a 0.08 (0.06–0.12)a Culture medium Solid 1.0 Liquid 3.41 (0.57–20.30) 0.33 (0.06–1.97) Region Sub-Saharan Africa 1.0 Southeast Asia 4.03 (0.65–24.84) 0.20 (0.04–1.00)a Individual level Ageb <33 y 1.0 ≥33 y 1.43 (0.81–2.52) 0.74 (0.66–0.84)a Gender Female 1.0 Male 1.26 (0.71–2.24) 1.04 (0.93–1.16) CD4 cell countc ≥200 cells/µl 1.0 <200 cells/µl 6.38 (2.87–14.17)a 0.46 (0.38–0.57)a Abnormal chest radiographd No 1.0 Yes 1.36 (0.68–2.73) 0.41 (0.30–0.57)a Values in each cell indicate the odds ratio for sensitivity or specificity compared with a referent group. a p-value <0.05 for null hypothesis that odds ratio = 1. b Excludes Shah et al. [24]. c Includes only studies Cain et al. [20], Shah et al. [24], Lawn et al. [26], and Chheng et al. [29]. d Includes only studies Cain et al. [20], Lewis et al. [23], Shah et al. [24], and Lawn et al. [26]. Table 5 shows the negative predictive value and the numbers needed to screen for the CFSW rule adjusted for individual- and study-level covariates. In a setting with 5% TB prevalence among people living with HIV, the rule has a negative predictive value of 98.3% (95% CI 97.5%–98.8%) for patients screened in a clinical setting and 97.3% (95% CI 96.9%–97.7%) for patients screened in a community setting. The numbers needed to screen at the same prevalence of TB are 15 and 11 for clinical and community setting, respectively. The negative predictive value was similar in those having high (≥200) and low (<200) CD4 count at 96.9% (95% CI 95.1%–98.0%) and 98.9% (95% CI 97.5%–99.5%), respectively (see also Table S3). 10.1371/journal.pmed.1000391.t005 Table 5 Negative predictive value (NPV) and number needed to screen (NNS) using rule CFSW in a hypothetical population of 1,000 people living with HIV stratified by study and individual level predictors. Participants 1% TB Prevalence 5% TB Prevalence 10% TB Prevalence 20% TB Prevalence NPV 95% CI NNS NPV 95% CI NNS NPV 95% CI NNS NPV 95% CI NNS All study participants 99.6 (99.5–99.6) 62 97.7 (97.4–98.0) 12 95.3 (94.6–95.9) 6 90.0 (88.6–91.3) 3 All study participants excluding miners 99.6 (99.5–99.7) 67 97.9 (97.5–98.2) 13 95.6 (94.8–96.3) 7 90.6 (89.0–92.1) 3 Setting Clinical 99.7 (99.5–99.8) 78 98.3 (97.5–98.8) 15 96.4 (94.8–97.5) 8 92.3 (89.0–94.6) 4 Community 99.5 (99.4–99.5) 55 97.3 (96.9–97.7) 11 94.5 (93.7–95.2) 5 88.5 (86.9–89.9) 3 Miners 99.2 (98.7–99.5) 38 96.1 (93.8–97.6) 8 92.2 (87.8–95.1) 4 84.0 (76.1–89.6) 2 Screening Nonscreened for TB 99.6 (99.5–99.7) 77 98.1 (97.5–98.5) 15 96.0 (94.9–96.9) 7 91.5 (89.2–93.3) 4 Prescreened for TB 99.3 (99.3–99.3) 36 96.5 (96.2–96.7) 7 92.8 (92.4–93.2) 4 85.1 (84.3–86.0) 2 Culture medium Liquid 99.6 (99.3–99.8) 75 98.2 (96.7–99.0) 15 96.2 (93.2–98.0) 7 91.9 (85.9–95.5) 3 Solid 99.5 (99.4–99.5) 57 97.3 (97.0–97.7) 11 94.6 (93.8–95.2) 6 88.5 (87.1–89.8) 3 Geography Southeast Asia 99.6 (99.2–99.8) 81 98,0 (95.9–99.0) 16 95,9 (91.6–98.0) 8 91.2 (83.0–95.6) 4 Sub-Saharan Africa 99.5 (99.4–99.6) 52 97.4 (97.1–97.8) 10 94.8 (94.0–95.4) 5 88.9 (87.5–90.2) 3 Age ≥33 y 99.6 (99.5–99.7) 63 97.8 (97.2–98.2) 12 95.4 (94.3–96.4) 6 90.3 (88.0–92.1) 3 <33 y 99.5 (99.4–99.6) 59 97.5 (97.0–97.9) 12 94.8 (94.0–95.6) 6 89.1 (87.4–90.6) 3 Gender Male 99.5 (99.4–99.6) 64 97.5 (97.2–97.9) 13 95.0 (94.2–95.6) 6 89.3 (87.8–90.6) 3 Female 99.6 (99.5–99.7) 60 98.0 (97.5–98.4) 12 95.8 (94.9,96.6) 6 91.0 (89.2–92.6) 3 CD4 cell count ≥200 cells/µl 99.4 (99.0–99.6) 80 96.9 (95.1–98.0) 16 93.6 (90.2–95.9) 8 86.7 (80.4–91.2) 4 <200 cells/µl 99.8 (99.5–99.9) 81 98.9 (97.5–99.5) 16 97.8 (94.8–99.1) 8 95.1 (89.1–97.9) 4 Abnormal chest radiograph Yes 99.4 (99.0–99.6) 83 97.0 (95.2–98.2) 16 93.9 (90.3–96.2) 8 87.2 (80.6–91.8) 4 No 99.5 (99.3–99.7) 61 97.7 (96.7–98.4) 12 95.2 (93.2–96.7) 6 89.9 (85.9–92.9) 3 Four studies [20],[23],[24],[26] consistently recorded information on chest radiograph, allowing screening rules with this sign to be evaluated using data from 2,805 participants The addition of abnormal chest radiographic findings into the CFSW rule increases the sensitivity to 90.6% (95% CI 66.7%–97.9%) with a specificity of 38.9% (95% CI 12.8%–73.3%), and a likelihood ratio negative of 0.242 (95% CI 0.102–0.571). Fifteen of the 23 rules included in our analysis outperform the symptom-based CFSW rule when abnormal chest radiographic findings are added (Table S4). On the basis of our meta-analysis findings and incorporating current WHO recommendations on provision of IPT, we developed a simple TB screening algorithm for public health programmes to screen people living with HIV, and, depending on the outcome of screening, to either provide IPT or evaluate patients further for TB or other diseases (Figure 4). 10.1371/journal.pmed.1000391.g004 Figure 4 Algorithm for TB screening in person living with HIV in HIV prevalent and resource-constrained settings. * Every person living with HIV needs to be evaluated for ART eligibility, and all settings providing care should reduce TB transmission through proper measures. ** Chest radiography is not required to classify patients into the TB and not-TB groups, but can be done, if available, to increase the sensitivity of screening. *** Assess for contraindications, including active hepatitis (acute or chronic), regular and heavy alcohol consumption, and symptoms of peripheral neuropathy, is required prior to initiating IPT. Past history of TB is not a contraindication for starting IPT. Tuberculin skin test may be performed as part of eligibility screening in some settings. **** Investigations for TB should be done in accordance with existing national guidelines. Discussion We found that the absence of all of current cough, fever, night sweats, and weight loss can identify a subset of people living with HIV who have low probability of having TB disease. This screening rule was superior over other candidate rules in eight of the nine studies included and had an overall favourable performance over competing rules in the hierarchical summary relative operating characteristic (HSROC) analysis. We also demonstrated that the negative predictive value of the rule was high across a range of TB disease prevalence estimates and across different population subsets, including those with low and high CD4 count, and those drawn from clinical and community settings and South African miners. We believe that these screening questions are likely to be acceptable to practitioners, because they are symptoms classically associated with TB disease. Underdiagnosis and delayed diagnosis of TB contribute to excess mortality among people living with HIV [17]. Similarly, concerns about the ability to reliably rule out active TB before initiating IPT have been a major barrier for wider use of this intervention. In the absence of a rapid and effective TB diagnostic tool available at the point-of-care, simple clinical algorithms must be used to screen people living with HIV for TB, dividing them into those in whom active TB is excluded and those who require further evaluation. This meta-analysis synthesizes the best available evidence for how to do this by relying on individual patient data of culture-confirmed TB cases from people living with HIV in the two regions of the world with the most severe burden of the TB and HIV dual epidemic. The major change to existing practice would be the replacement of chronic cough with current cough as a screening question and the addition of other symptoms to standard screening. National TB programs have traditionally defined a TB “suspect” as someone with cough lasting greater than 2 or 3 wk, and designed case-finding activities to investigate up to ten TB suspects for every TB case detected [36]. However, studies included in this analysis have shown that chronic cough is highly insensitive for TB disease in people living with HIV; using this symptom as a screening rule would miss cases and contribute to diagnostic delays [16],[20]. Using the combination of symptoms that we propose, in a population of people living with HIV with a 5% TB prevalence (excluding miners), requires the investigation of 13 extra patients for every TB case detected, a ratio of TB suspects to a TB case not much different from what national TB control programmes target in the general population. There has been ongoing debate about the importance of chest radiography in screening people living with HIV for IPT eligibility [37],[38]. Our analysis showed that the addition of abnormal chest radiography findings into the screening rule of CFSW increases 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%). However, for example at a 5% TB prevalence rate among people living with HIV, augmenting the CFSW rule with abnormal chest radiographic findings increases the negative predictive value by a margin of only 1% (98.7% versus 97.8%), albeit with the same number of cases needed to be screened. On the other hand, the addition of abnormal chest radiographic findings to the rule at TB prevalence of 20% among people living with HIV increases the negative predictive value by almost 4% (94.3% versus 90.4%) without additional cases needed to be screened. It is also worth noting that the CFSW screening rule has higher sensitivity among those who presented into a clinical setting (90%) and among those who have not been previously screened for TB (88%). Our findings show that the utility of the proposed symptom-based screening rule have excellent performance in most settings with TB and HIV burden. However, the negative predictive value will fall in those settings with higher TB prevalence when symptom screening alone is used, as it depends on prevalence of disease. In particular settings (e.g., antiretroviral clinics with a very high TB burden [39]), consideration must be given to use of an algorithm that contains chest radiography, or even adding additional sensitive investigations (e.g., culture) while screening people living with HIV for TB [30],[40]. People living with HIV and receiving IPT should also be regularly screened for TB during their visit to a health facility or contact with health care provider so as to promptly detect active TB, if it develops. Programme managers need to weigh the financial, technical, and logistic difficulties, and patient cost and inconvenience associated with performing chest radiography or other additional sensitive investigations on all people living with HIV as part of a screening program compared with an approach that relies only on symptomatic screening. When interpreting our results, one must bear in mind that only a few variables were common to all studies included in the meta-analysis. It is possible that the addition of one or more symptoms not included in our list of common symptoms could have improved the performance of our proposed screening rule. However, at least one study included in our meta-analysis explored over 80 million combinations of about 100 signs and symptoms and found a symptom combination (cough and fever of any duration and night sweats for 3 weeks or longer), which was similar to the one we propose as the best performing one [20]. Furthermore, questions were not asked in a uniform manner across all studies, and the reporting of symptoms can be highly dependent on factors such as the quality of the interview and interviewer, the circumstances under which questions are asked, and the social and cultural factors that shape individual perceptions of symptoms and disease [41]. We reviewed all questions carefully with principal investigators and data managers to ensure accurate mapping of differently phrased questions to common variables. Our study relied on patients drawn from multiple countries and multiple settings, and the variation in the performance of the proposed screening rule across these different settings suggests that variation in patient self-report of symptoms is unlikely to have major impact, at least at the population level. In some studies, only one sputum specimen was collected for culture, while multiple cultures are required to maximize sensitivity. Some patients with TB may have been incorrectly classified as not having TB. Extrapulmonary TB is an important cause of morbidity and mortality in people living with HIV, but most studies included in the meta-analysis focused on screening for pulmonary TB. Young children were not included in the studies. We did not specifically look into the role of tuberculin skin test in the proposed screening rule. Ideally, the utility of the algorithm we propose, based on the screening rule from our meta-analysis, should be studied prospectively using a standardized protocol in multiple diverse sites; this is particularly important as the studies included in our analysis came from only two geographical regions of the world. Similarly, because of the time required for the data aggregation, statistical analysis, manuscript preparation, and publication, there was one potentially eligible study that was not included in our analysis [30]. We believe that the exclusion of this single study from South Africa, a country from which we have included similar studies already, will not affect the interpretation of our data and conclusions. In the future, as more studies are reported, particularly from other regions, it will be important to repeat the meta-analysis. Greatly improving TB screening, diagnosis, and treatment in people living with HIV will require deployment of a rapid, accurate, point-of-care TB diagnostic test. In the absence of such a test, we believe that a standardized algorithm employing symptoms, as we propose here, can improve the diagnosis and treatment of TB for people living with HIV, and by doing so would save many lives. Reliable exclusion of TB disease will facilitate safer initiation of antiretroviral therapy and will allow for broader use of IPT, which can substantially reduce TB incidence. Earlier and accurate HIV and TB screening and treatment may also help identify infectious cases earlier, thereby reducing both HIV and TB transmission. Evidence-based and internationally recommended guidelines should be used to expedite the diagnosis and treatment of TB in people living with HIV [42]. Supporting Information Figure S1 Hierarchical summary relative operating characteristic (HSROC) curves for the 23 candidate 1-of-n rules. (0.08 MB DOC) Click here for additional data file. Table S1 Phrasing of questions that were used in all studies to ask about five common symptoms. (0.09 MB DOC) Click here for additional data file. Table S2 Study-specific values and rankings of the sensitivity of each candidate screening rule in the nine studies included. (0.14 MB DOC) Click here for additional data file. Table S3 Diagnostic performance of all 23 candidate rules and number needed to screen in a hypothetical population of 1,000 people living with HIV stratified by TB prevalence among people living with HIV. (0.15 MB DOC) Click here for additional data file. Table S4 Diagnostic performance of 23 candidate rules that include abnormal chest radiograph and number needed to screen in a hypothetical population of 1,000 people living with HIV stratified by TB prevalence among people living with HIV. (0.15 MB DOC) Click here for additional data file.
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Screening for HIV-Associated Tuberculosis and Rifampicin Resistance before Antiretroviral Therapy Using the Xpert MTB/RIF Assay: A Prospective Study
Stephen D Lawn, Sophie V. Brooks, Katharina Kranzer … (2011)
Introduction Tuberculosis is a major challenge for antiretroviral therapy (ART) services in resource-limited settings where patients typically enrol with advanced immunodeficiency [1]. Many patients referred for ART have a current TB diagnosis, and an additional large burden of disease is detected during pre-treatment screening [2]–[4]. Tuberculosis in this population is a major cause of morbidity and mortality [1],[5]–[7] and presents a substantial hazard of nosocomial disease transmission to other patients and health care workers [8]. These risks are heightened when patients have multidrug-resistant TB (MDR-TB) [9]–[11]. To address these challenges, there is a critical need in such settings for rapid, effective screening for TB and detection of drug resistance [1],[12]. Screening for TB in this patient population is difficult, however [12]. The World Health Organization's (WHO) intensified case finding symptom screen has low specificity and misses approximately 10%–20% of cases [13],[14]. Sputum smear microscopy, the mainstay of TB diagnosis in resource-limited settings, detects as few as one in five cases when used as a screening tool pre-ART [4],[12],[15]. Chest radiography is costly and not widely available; interpretation is difficult, and up to one-third of culture-confirmed cases of pulmonary TB diagnosed during screening have a normal radiograph [12],[16]. Availability of culture-based diagnosis is also extremely limited in resource-limited settings because of high cost and technical complexity, and this approach often provides a diagnosis only after several weeks [15],[17]. These challenges are further compounded by the extremely limited laboratory capacity to detect drug resistance [18]. The threat posed by MDR-TB to efforts to control TB worldwide [19] requires urgent improvements in diagnostic capacity. Following a large multi-country evaluation [20], the WHO, in December 2010, endorsed the roll-out of a novel rapid test for the investigation of patients suspected of having TB, especially in settings with a high prevalence of HIV-associated disease and/or MDR-TB [21]. The Xpert MTB/RIF assay (Cepheid) is a fully automated molecular assay in which real-time polymerase chain reaction technology is used to simultaneously detect Mycobacterium tuberculosis and rifampicin resistance mutations in the rpoB gene [22],[23]. The cartridge-based system dispenses with the need for prior sputum processing and requires minimal laboratory expertise, and results are available in less than 2 h, permitting a specific TB diagnosis and rapid detection of rifampicin resistance. Excellent performance characteristics were observed among symptomatic adults with suspected TB in a large multi-country evaluation [20]. These findings have been confirmed in a subsequent multi-country implementation study [24] and in several laboratory-based studies [25]–[29]. The assay has sensitivities of 98%–100% for smear-positive pulmonary TB, 57%–78% for smear-negative pulmonary TB, and 53%–81% for extrapulmonary TB when testing a variety of clinical samples [20],[24]–[29]. Further studies are needed to examine the performance of the assay in different clinical settings, including use as a routine screening test to increase TB case detection in HIV-infected patients. We evaluated the diagnostic accuracy of the Xpert MTB/RIF assay among consecutive patients with advanced immunodeficiency being screened for TB (regardless of symptoms) prior to starting ART in a South African township with a very high burden of TB. Methods Setting The ART cohort was based in Gugulethu township, Cape Town, where the prevalence of HIV and the TB notification rate are both extremely high [5]. Several studies reporting the burden, diagnosis, and complications of TB in this cohort have previously been published [3],[5],[15],[16],[30],[31]. National TB programme guidelines recommend investigating symptomatic adults with suspected pulmonary TB using smear microscopy of two sputum samples; in suspected “retreatment TB” cases only, culture of one sputum sample may be requested in addition [32]. In accordance with the national ART programme guidelines, ART was provided for all patients with WHO stage 4 disease and/or blood CD4 cell counts 200 cells/µl were 28.1% (95% CI, 19.7–36.4), 19.4% (95% CI, 14.7–24.0), and 13.8% (95% CI, 10.2–17.5), respectively. In binomial regression analysis (Table 2), risk of TB was independently associated with low CD4 cell count, low body mass index, high viral load, not previously having received TB treatment, and having a positive WHO symptom screen. However, risk of TB was not associated with chronic cough of ≥2 wk duration. 10.1371/journal.pmed.1001067.t002 Table 2 Binomial regression analysis showing crude and adjusted risk ratios for the associations between risk of sputum culture-positive tuberculosis and patient characteristics. Patient Characteristics Crude Risk Ratio 95% CI p-Value Adjusted Risk Ratio 95% CI p-Value Age ≤30 y 1 Age >30 y 0.90 0.61–1.34 0.62 Male 1 Female 1.06 0.70–1.61 0.79 Body mass index 18–25 kg/m2 1 1 Body mass index 25 kg/m2 0.68 0.42–1.09 0.109 0.70 0.39–1.27 0.243 No history of previous TB treatment 1 1 History of previous TB treatment 0.68 0.41–1.13 0.14 0.50 0.26–0.96 0.036 CD4 ≥100 cells/µl 1 1 CD4 2 wk compared to 56.5% (95% CI, 41.6–71.4) among those with either no cough or cough of shorter duration (p = 0.018). Moreover, sensitivity was substantially greater in patients for whom the time to positivity of sputum samples was less than the median of 16 d (85.7%; 95% CI, 69.4–100) than in those with longer times to positivity (48.5%; 95% CI, 30.4–66.5) (p = 0.005). There was also a weak association between sensitivity and CD4 cell counts: sensitivity was 78.9% (95% CI, 58.8–99.1) in those with CD4 cell counts <100 cells/µl compared to 54.3% (95% CI, 36.9–71.6) in those with higher CD4 cell counts (p = 0.075). However, there was no association with radiographic abnormalities or with a positive WHO symptom screen. There were three patients with apparent false-positive Xpert MTB-RIF assays, giving an assay specificity of over 99.0% in each of the different analyses (Table 3). Review of the study and clinical records of these patients revealed that two of these patients had overt pulmonary and systemic symptoms suggestive of TB, and both had chest radiographs revealing parenchymal consolidation and marked hilar and paratracheal lymphadenopathy highly suggestive of TB. One of these patients was reinvestigated during routine clinical follow-up and had two positive sputum smears (2+ and 3+). Both patients received standard treatment for TB and made excellent clinical responses. The third patient had symptoms and an abnormal chest radiograph but was lost to follow-up. Use of Xpert MTB/RIF in Screening Algorithms To further explore the utility of the Xpert MTB/RIF assay, we considered clinical populations with a TB prevalence of 20%, 15%, 10%, or 5%. With an overall sensitivity of 73.3% and specificity of 99.2% (Table 3), the PPVs at these TB prevalence rates would be 95.8%, 94.2%, 91.0%, and 82.8%, respectively, and the NPVs would be 93.7%, 95.5%, 97.1%, and 98.6%, respectively. We next considered the utility of incorporating the Xpert MTB/RIF assay into different screening algorithms, examining the use of smear microscopy, symptom screening, one Xpert assay, two Xpert assays (Xpert done on a second sample if the first was negative), and sequential smear microscopy and Xpert testing (Xpert tests done if smear microscopy was negative). This was simulated for a hypothetical cohort of 1,000 patients with a TB prevalence of 20%, 15%, 10%, or 5% and assuming that 30% of cases were smear-positive. Symptom frequencies and the sensitivity and specificity of the Xpert assay as reported above were used. The yield of TB cases, the number of missed cases, and the number of Xpert tests done for each correct TB diagnosis were compared between these different screening strategies and clinical populations (Table 4). Compared to a base case scenario of smear microscopy of two sputum samples in patients with a positive WHO symptom screen, the sensitivity of algorithms incorporating the Xpert MTB/RIF assay was much greater and the corresponding number of missed diagnoses was far fewer. However, at a TB prevalence of 5%, the number of Xpert tests done per case diagnosed was high (Table 4). A strategy of sequential smear microscopy and then Xpert testing of smear-negative patients yielded the same number of diagnoses, but did not substantially reduce the number of Xpert tests per case diagnosed. 10.1371/journal.pmed.1001067.t004 Table 4 Utility of the Xpert MTB/RIF assay for tuberculosis diagnosis when incorporated into different screening algorithms and when used in hypothetical patient cohorts with a tuberculosis prevalences of 20%, 15% 10%, or 5%. Investigation Strategy Sensitivity (Percent)a Specificity (Percent) TB Prevalence 20% TB Prevalence 15% TB Prevalence 10% TB Prevalence 5% Correct TB Diagnoses Missed TB Cases Xpert Tests per TB Diagnosis Correct TB Diagnoses Missed TB Cases Xpert Tests per TB Diagnosis Correct TB Diagnoses Missed TB Cases Xpert Tests per TB Diagnosis Correct TB Diagnoses Missed TB Cases Xpert Tests per TB Diagnosis Base case screening algorithm Symptom screen + smear ×2 27.6 100.0 55.2 144.8 0 41.4 108.6 0 27.6 72.4 0 13.8 36.2 0 Using one Xpert test in algorithm Symptom screen+Xpert ×1 50.5 99.6 101 99 6.9 75.7 74.3 9.1 50.5 49.5 13.5 25.2 24.8 26.9 Symptom screen+smear ×2+Xpert ×1 50.5 99.6 101 99 6.4 75.7 74.3 8.6 50.5 49.5 13.1 25.2 24.8 26.3 Xpert ×1 for all patients 60.1 99.4 120.2 79.8 8.3 90.2 59.8 11.1 60.1 39.9 16.6 30.1 19.9 33.2 Smear ×2+Xpert ×1 for all patients 60.1 99.4 120.2 79.8 7.8 90.2 59.8 10.6 60.1 39.9 16.1 30.1 19.9 32.7 Using two Xpert tests in algorithm Symptom screen+Xpert ×2 60.6 99.4 121.2 78.8 11.1 90.9 59.1 14.7 60.6 39.4 22.1 30.2 19.8 44.4 Xpert ×2 for all patients 73.4 99.1 146.8 53.2 13.2 110.1 39.9 17.8 73.4 26.6 26.8 36.7 13.3 54.1 a Sensitivity based on the assumption that 30% of cases are sputum smear-positive. Use of symptom pre-screening limited the sensitivity of TB detection. In populations with high TB prevalence, Xpert testing of all patients regardless of symptoms increased sensitivity without substantially increasing the number of Xpert tests done per TB case diagnosed (Table 4). Compared to the strategy of doing an Xpert assay on one sputum sample from patients with a positive symptom screen, a strategy of doing two Xpert tests on all patients was associated with 22.9% higher sensitivity for TB and the fewest missed cases. Although the latter strategy would require a large absolute number of tests, at a TB prevalence of 20%, one extra TB case would be diagnosed for every additional 6.3 tests done. Detection of Rifampicin Resistance Among 81 cases of TB diagnosed, four cases had isolates resistant to rifampicin because of MDR-TB (prevalence, 4.9%; 95% CI, 1.4–12.2). Among the 445 patients (839 samples) with results of culture, drug susceptibility testing, and Xpert MTB/RIF assays all available, there were 84 isolates from 55 patients (including all four cases of MDR-TB) in which rifampicin susceptibility could be compared. Rifampicin resistance was correctly identified in all four cases of MDR-TB by the Xpert MTB/RIF assay (100% sensitivity) (Table 5). However, the Xpert MTB/RIF assay also reported rifampicin resistance in three samples from three further patients in which the isolates were reported as rifampicin susceptible using comparator assays (Table 5). A paired sputum sample was available from two of these patients and rifampicin-susceptible M. tuberculosis was reported by Xpert MTB/RIF assay in both. To resolve these discrepancies, the rpoB regions of all five isolates from these three patients were sequenced. All were found to be wild-type, confirming absence of genotypic rifampicin resistance and indicating that the three Xpert MTB/RIF assay results were false positives. All remaining patients with susceptible isolates were correctly identified as such by the assay. Thus, in a per-patient analysis, the PPV of the Xpert MTB/RIF assay for detecting rifampicin resistance was 4/7 (57%) and the specificity was 48/51 (94.1%; 95% CI, 84.8–98.8). 10.1371/journal.pmed.1001067.t005 Table 5 Comparison of results regarding drug susceptibility testing for rifampicin among paired samples from patients (n = 6) in whom rifampicin resistance was detected using one or more assays. Patient Number Sputum Smear Xpert MTB/RIF MTBDRplus on Sputum MTBDRplus on Culture Isolate MGIT Phenotypic DST rpoB Gene Sequencing Final Rifampicin Susceptibility Overall Susceptibility Pattern Concordant susceptibility results #020 NEG/NEG −/R −/− −/R −/R − Resistant MDR-TB #099 POS/POS R/R −/R R/R −/− − Resistant MDR-TB #208 NEG/NEG R/− −/− R/R R/R − Resistant MDR-TB #292 NEG/POS R/R R/− R/R R/− − Resistant MDR-TB Discordant susceptibility results #039 NEG/NEG R/S S/− S/S S/S WT/WT Susceptible Pan-susceptible #157 POS/POS R/S S/S S/S S/S WT/WT Susceptible Pan-susceptible #322 POS R − S S WT/WT Susceptible Pan-susceptible DST, drug susceptibility testing; NEG, smear-negative; POS, smear-positive; R, resistant; S, susceptible; WT, genotypically wild-type. Time to Diagnosis The median delays between sputum collection and results being available to the clinic for smear microscopy and Xpert MTB/RIF assays and positive liquid cultures were 3 d (IQR, 2–5) and 4 d (IQR, 3–6), respectively. The median delays for culture results were 12 d (IQR, 10–14) and 20 d (IQR, 17–27) for smear-positive and smear-negative disease, respectively. Cultures were incubated for 42 d before being declared negative for M. tuberculosis, with a median time to reporting of 43 d (IQR, 43–45). For the patients with confirmed MDR-TB (n = 4), the mean time to TB diagnosis and detection of rifampicin resistance was 2 d using Xpert MTB/RIF assay, 21 d using the MTBDRplus assay on a positive culture isolate, and 40 d using phenotypic drug susceptibility testing in liquid culture. Discussion A high prevalence (17.3%) of culture-proven pulmonary TB was diagnosed in this patient population, but conventional diagnostic tools widely used in resource-limited settings performed poorly. Smear microscopy detected just 28% of cases, and chest radiology was of low discriminatory value. Even using automated liquid culture as the diagnostic gold standard, diagnosis was slow, with a median delay of almost 3 wk among those with smear-negative disease. In contrast, the Xpert MTB/RIF assay was able to diagnose with extremely high specificity all cases of smear-positive TB and almost two-thirds of smear-negative cases and three-quarters of cases overall when testing two samples. Only 0.6% of test results were indeterminate. The assay also rapidly detected rifampicin resistance in all four cases of confirmed MDR-TB. However, false-positive rifampicin resistance results were also observed. The TB prevalence and associated risk factors detected in this clinical setting were similar to those previously reported from this and another ART clinic in South Africa [3],[4],[15]. Almost 30% of patients with CD4 cell counts <100 cells/µl had culture-proven TB, and rapid diagnosis is needed since such patients have high mortality risk [5],[34]. Only one-quarter of all TB patients reported a cough lasting ≥2 wk—a symptom screen widely used for many years to define suspected TB cases. Use of the new WHO symptom screening tool [13],[14] had higher sensitivity but still would have missed 13 of the 81 TB diagnoses made in this study, suggesting the need for routine microbiological screening of all patients in this setting. We evaluated the utility of the Xpert MTB/RIF assay as a screening tool in consecutive HIV-infected adult patients enrolling for ART, excluding those who already had a TB diagnosis (approximately one-third of referrals to this cohort [35]). Since patients were screened regardless of the presence or absence of symptoms, our study is likely to have diagnosed TB cases at an earlier stage in the disease course than studies in which symptomatic patients were tested. In contrast, the previous Foundation for Innovative New Diagnostics multi-country evaluation [20] enrolled only patients with overt TB symptoms; all had a chronic cough of at least 2 wk duration and were able to produce three 1.5-ml sputum specimens. Early disease in our study would tend to be associated with lower bacillary numbers in sputum samples, as indicated by the observations that almost 70% of cases were sputum smear-negative and the prolonged median time to positivity of liquid cultures. This patient population therefore represents a major challenge for any diagnostic assay [17]. The limits of detection of the Xpert MTB/RIF assay (95% sensitivity) defined by in vitro experiments is 131 bacilli/ml of sputum, which approaches than that of liquid culture, which falls within the range 10–100 bacilli/ml [17],[23]. In contrast, smear microscopy is able to detect only samples with more than approximately 10,000 organisms per millilitre [17],[23]. Testing a single sputum sample using Xpert MTB/RIF allowed diagnosis of all smear-positive cases regardless of smear grade; these cases pose the greatest infectious hazard within the community and health care settings. As anticipated [17], the sensitivity for smear-negative disease was lower than that reported in the previous multi-country evaluation [20] (43.3% versus 72.5% using one sputum sample; 63.3% versus 85.1% using two samples). Presence of cough of ≥2 wk was associated with much higher sensitivity for smear-negative TB, as was shorter time to culture positivity. The latter observation suggests that sensitivity was likely to have been limited by very low numbers of bacilli in sputum samples. Three patients had false-positive TB diagnoses using Xpert MTB/RIF compared to the predefined laboratory gold standard of liquid culture. However, the clinical and radiological features in these cases were highly suggestive of TB; one was confirmed as having smear-positive TB on reinvestigation, two exhibited excellent responses to TB treatment, and the third patient was lost to follow-up. These follow-up data suggest that some or all of these false-positive Xpert MTB/RIF assays may actually have been correct. The proportion of cultures lost to contamination was very low (3.1%), highlighting possible over-decontamination in the laboratory and loss of sensitivity in the culture gold standard. If this was the case, the PPV of the assay would be higher, which would increase assay utility, especially in clinical populations with lower disease prevalence. Few Xpert MTB/RIF assays were indeterminate, but the observation that three out of five of these were in culture-positive cases suggests that indeterminate results should be followed up by a repeat test. Despite only moderate sensitivity for smear-negative disease, Xpert MTB/RIF nevertheless increased overall case detection by 36% when testing one sample and by 45% when testing two samples, compared to smear microscopy. Used for baseline screening evaluation of patients enrolling in this ART service, Xpert MTB/RIF testing of a single sputum sample would detect TB in approximately 10% of the cohort, and testing two samples would detect TB in 12.5%. Thus, the assay would detect approximately one TB case for every eight patients screened, compared to one in 18 patients screened using sputum microscopy. We explored the potential impact of incorporating the assay in several screening algorithms applied to clinical populations with a range of TB prevalence rates. The likely benefits (increased TB yield) and assay costs (tests done per case diagnosed) were highly dependent on TB prevalence, and at a prevalence rate of 5%, the number of tests done per case diagnosed was high (4-fold higher than for a population with a prevalence of 20%). A strategy of screening with sputum microscopy and then testing smear-negative samples with Xpert MTB/RIF assay would result in minimal savings with regard to the number of Xpert tests done but would also result in failure to diagnose MDR-TB in highly infectious smear-positive cases. Symptom pre-screening restricted sensitivity and, at higher TB prevalence rates, did not substantially reduce the number of Xpert MTB/RIF tests done to identify one case of TB when compared to a strategy of testing all patients regardless of symptoms. Screening two samples with Xpert MTB/RIF would substantially increase the absolute number of tests done, but at high TB prevalence rates the high incremental yield may justify this approach. The number of Xpert MTB/RIF assays done might logically be stratified by CD4 cell count since this is a strong predictor of TB prevalence. For example, in high-burden settings such as South Africa, two tests might be done for those with CD4 cell count <200 cells/µl and just one test for those with higher counts. These strategies need to be evaluated by detailed cost-effectiveness analyses that take into account not simply the costs of testing but also the downstream impact on clinical outcomes and associated costs. Since the Xpert MTB/RIF instrument was based in a centralised laboratory service, with results reported via the routine laboratory system, the median time to diagnosis was similar to that of smear microscopy (4 d versus 3 d, respectively). The time to diagnosis of smear-negative disease, however, was shortened by a median of 2 wk compared to culture. Time to diagnosis and treatment would potentially be further shortened by location of the instrument in the ART clinic [24]. The assay also has the potential to shorten the time to exclude a diagnosis of TB; this normally takes 6 wk or more via negative cultures and may lead to inappropriate delays in ART initiation. In view of the high NPV of the Xpert MTB/RIF assay in this cohort (94.8%), a negative result at baseline evaluation could provide a useful indication of a low probability of TB, increasing clinical confidence to start ART without undue delay. In cohorts with a lower prevalence of TB, the NPV would be higher, further increasing its utility in this regard. HIV-associated MDR-TB carries a high mortality risk, and nosocomial outbreaks in HIV care and treatment centres pose a grave threat to patients accessing these services [9],[10],[36]. Many patients with HIV-associated MDR-TB die before a diagnosis can be made [9],[36]. In this study, the Xpert MTB/RIF assay identified four patients with rifampicin-resistant isolates who had MDR-TB, greatly reducing the mean time to detection (2 d) compared to using conventional culture-based susceptibility testing (40 d) or using line probe assays on culture isolates (20 d). By accelerating diagnosis, the Xpert MTB/RIF assay has the potential to substantially reduce the risks of nosocomial transmission of MDR-TB and improve the prognosis of affected individuals. The Xpert MTB/RIF assay reported three false-positive rifampicin resistance results. The finding of discordant rifampicin susceptibility results from paired samples using the Xpert MTB/RIF assay suggests that specificity might be increased by requiring confirmation of resistance in more than one sample. While such false positives were not found in the initial multi-country evaluation [20], another ongoing field study sponsored by the Foundation for Innovative New Diagnostics has also detected cases, leading the manufacturer to modify the instrument software and cartridge specifications [24],[37]. With WHO approval of roll-out of this assay in December 2010, confirmation of successful reconfiguration of the test platform is urgently required. Strengths of the study include the use of a quality-assured laboratory that participated in the previous multi-country evaluation [20]. Whereas all previously published studies have evaluated use of the assay among individuals with suspected TB [20],[24]–[29], this study evaluated the assay as a screening tool in unselected consecutive patients regardless of symptoms in a high-burden setting. The TB status of all patients was clearly defined based on a rigorous laboratory gold standard. Weaknesses include the fact that a small number of tests were not done because of a laboratory clerical error and that there were few cases of MDR-TB. While a similar burden of disease has been reported from an ART clinic elsewhere in South Africa [4], the prevalence of TB may differ in other countries, and we therefore explored utility at a range of prevalence rates. The impact of the sputum concentration procedure and of dividing the sputum pellet between three assays rather than testing unprocessed sputum was not investigated in this study, but these methods were not found to impact assay sensitivity in a previous large-scale multi-country evaluation [20]. The usefulness of the assay as a point-of-care test was not evaluated. Further studies are needed to assess the impact of Xpert MTB/RIF screening on subsequent patient outcomes, the operational feasibility of using the assay within the clinic, and cost-effectiveness. In conclusion, when used as a routine screening test among patients with advanced immunodeficiency and high TB risk, rapid screening using the Xpert MTB/RIF assay substantially increased case detection, supporting replacement of microscopy as the initial diagnostic tool. The assay also greatly decreased the time to diagnosis of MDR-TB. Use of Xpert MTB/RIF as a screening tool might effectively reduce the risk of nosocomial MDR-TB outbreaks in HIV care and treatment settings and improve the prognosis of affected patients. However, the specificity of the assay for detecting rifampicin resistance needs to be improved to prevent overdiagnosis of rifampicin-resistant disease. Supporting Information Text S1 STARD checklist. (PDF) Click here for additional data file.
Author and article information
Contributors
Kristina L. Bajema:
ORCID: http://orcid.org/0000-0002-3229-5590
kbajema@uw.edu
Ingrid V. Bassett: ibassett@mgh.harvard.edu
Sharon M. Coleman: sharcole@bu.edu
Douglas Ross: drsross@iafrica.com
Kenneth A. Freedberg: kfreedberg@mgh.harvard.edu
Anna Wald: annawald@uw.edu
Paul K. Drain: pkdrain@uw.edu
Journal
Journal ID (nlm-ta): BMC Infect Dis
Journal ID (iso-abbrev): BMC Infect. Dis
Title:
BMC Infectious Diseases
Publisher:
BioMed Central
(London
)
ISSN
(Electronic):
1471-2334
Publication date
(Electronic):
5
January
2019
Publication date PMC-release: 5
January
2019
Publication date Collection: 2019
Volume: 19
Electronic Location Identifier: 14
Affiliations
[1
]ISNI 0000000122986657, GRID grid.34477.33, Department of Medicine, , University of Washington, ; 1959 NE Pacific St., Box 356429, Seattle, WA 98195 USA
[2
]Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston
University School of Public Health, Boston, USA
[3
]ISNI 0000 0004 1936 7558, GRID grid.189504.1, Boston University School of Public Health, ; Boston, USA
[5
]Departments of Medicine, Epidemiology, and Laboratory Medicine, Vaccine and Infectious
Disease Division, Fred Hutchinson Cancer Research Center, University of Washington,
Seattle, USA
Author information
Kristina L. Bajema
http://orcid.org/0000-0002-3229-5590
Article
Publisher ID: 3614DOI: 10.1186/s12879-018-3614-7
PMC ID: 6321698
PubMed ID: 30611192
SO-VID: 8511e222-05b4-46ac-a735-5adc5d1d83b1
Copyright © © The Author(s). 2019
License:
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
History
Date
received
: 23
January
2018
Date
accepted
: 11
December
2018
Funding
Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
Award ID: T32 AI007044
Funded by: Harvard Global Health Institute
Funded by: Fogarty International Clinical Research Scholars and Fellows Program at Vanderbilt
University
Award ID: R24 TW007988
Award Recipient
:
Paul K. Drain
Funded by: Infectious Disease Society of America Education & Research Foundation and National
Foundation for Infectious Diseases
Funded by: Massachusetts General Hospital Executive Committee on Research
Funded by: National Institutes of Health (US)
Award ID: T32 AI007433
Award Recipient
:
Paul K. Drain
Funded by: FundRef http://dx.doi.org/10.13039/100007301, Harvard University Center for AIDS Research;
Award ID: P30 AI060354
Award Recipient
:
Paul K. Drain
Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
Award ID: K23 AI108293
Award ID: R01AI058736
Award Recipient
:
Paul K. Drain
Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
Award ID: R01 MH090326
Award Recipient
:
Ingrid V. Bassett
Categories
Subject:
Research Article
Custom metadata
issue-copyright-statement © The Author(s) 2019
ScienceOpen disciplines: Infectious disease & Microbiology
Data availability:
ScienceOpen disciplines: Infectious disease & Microbiology