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Pulmonary tuberculosis screening in anti-retroviral treated adults living with HIV in Kenya

BMC Infectious Diseases volume 21, Article number: 218 (2021) Cite this article

Abstract

Background

People living with HIV (PLHIV) who reside in high tuberculosis burden settings remain at risk for tuberculosis disease despite treatment with anti-retroviral therapy and isoniazid preventive therapy (IPT). The performance of the World Health Organization (WHO) symptom screen for tuberculosis in PLHIV receiving anti-retroviral therapy is sub-optimal and alternative screening strategies are needed.

Methods

We enrolled HIV-positive adults into a prospective study in western Kenya. Individuals who were IPT-naïve or had completed IPT > 6 months prior to enrollment were eligible. We evaluated tuberculosis prevalence overall and by IPT status. We assessed the accuracy of the WHO symptom screen, GeneXpert MTB/RIF (Xpert), and candidate biomarkers including C-reactive protein (CRP), hemoglobin, erythrocyte sedimentation rate (ESR), and monocyte-to-lymphocyte ratio for identifying pulmonary tuberculosis. Some participants were evaluated at 6 months post-enrollment for tuberculosis.

Results

The study included 383 PLHIV, of whom > 99% were on antiretrovirals and 88% had received IPT, completed a median of 1.1 years (IQR 0.8–1.55) prior to enrollment. The prevalence of pulmonary tuberculosis at enrollment was 1.3% (n = 5, 95% CI 0.4–3.0%): 4.3% (0.5–14.5%) among IPT-naïve and 0.9% (0.2–2.6%) among IPT-treated participants. The sensitivity of the WHO symptom screen was 0% (0–52%) and specificity 87% (83–90%). Xpert and candidate biomarkers had poor to moderate sensitivity; the most accurate biomarker was CRP ≥ 3.3 mg/L (sensitivity 80% (28–100) and specificity 72% (67–77)). Six months after enrollment, the incidence rate of pulmonary tuberculosis following IPT completion was 0.84 per 100 person-years (95% CI, 0.31–2.23).

Conclusions

In Kenyan PLHIV treated with IPT, tuberculosis prevalence was low at a median of 1.4 years after IPT completion. WHO symptoms screening, Xpert, and candidate biomarkers were insensitive for identifying pulmonary tuberculosis in antiretroviral-treated PLHIV.

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Introduction

Tuberculosis (TB) remains the leading cause of death in people living with HIV (PLHIV). TB preventive therapy is a critical intervention with mortality benefits that are independent of antiretroviral therapy (ART) [1, 2]. Isoniazid preventive therapy (IPT) administered for 6 months is a World Health Organization (WHO) recommended regimen that remains the most widely used TB preventive therapy [3]. In moderate and high TB burden areas, re-infection with M. tuberculosis due to frequent re-exposure may limit the durability of TB preventive therapy’s protective benefit. This was observed in the initial trials of IPT in PLHIV [4,5,6], in which TB rates significantly increased approximately 6 months after completing treatment. Recent trials suggest that IPT’s protective effects may be more durable when administered to antiretroviral-treated PLHIV [1, 7]. Little is known about the effectiveness of IPT in high TB burden settings outside of research trials, where pre-IPT screening for TB may include sputum evaluations and/or chest radiographs [5,6,7,8], and adherence may be optimized.

WHO recommends a symptom-based intensified case finding (ICF) screen to determine PLHIV who are eligible for immediate TB preventive treatment and as a general triage test at clinic visits to determine who needs further evaluation for TB disease [9]. Notably, this ICF screen was developed and validated in PLHIV who were new to HIV care and therefore ART-naive [10]. Subsequent studies have found that the WHO ICF screen has poorer sensitivity in identifying TB in ART-treated individuals [11]. As this screen is being used to evaluate for TB in ART-treated individuals (regardless of IPT status), it is important to understand its performance and limitations under programmatic conditions. Furthermore, there is an urgent need for novel strategies to risk stratify individuals for TB evaluations. A WHO consensus-gathering meeting identified a rapid, non-sputum-based diagnostic test that accurately diagnoses pulmonary TB as the most urgently needed TB test, and established a target product profile of sensitivity greater than 90% and specificity greater than 70% [12].

In 2017, 67 countries reported providing TB preventive treatment to nearly 1 million PLHIV [13]. However, provision of TB preventive treatment remains poor, with estimated coverage of only 36% of eligible PLHIV [13]. Kenya is one of four HIV high-burden countries in Africa [14], and one of the twenty highest TB burden nations with an estimated overall TB incidence of 348/100,000 person-years in 2016 [15]. In 2015, Kenya Ministry of Health initiated provision of IPT (6-month course) to PLHIV and provided IPT to almost 400,000 PLHIV in 2016, among the highest in the world [16]. We developed the current pilot study to investigate knowledge gaps around TB prevention in PLHIV including the prevalence of TB in PLHIV who received IPT under programmatic conditions in the setting of widespread ART availability and the performance of established and novel TB screening tools. We evaluated the accuracy of the WHO ICF screen for TB and investigated the performance of candidate biomarkers as TB triage tests in PLHIV who were overwhelmingly receiving ART.

Methods

Study setting

Between March 2017 and September 2018, we performed a prospective study among PLHIV at two HIV-care clinics in western Kenya.

Participants

PLHIV ages 18 to 70 years were eligible for study enrollment if they were either IPT-naïve or had completed IPT at least 6 months prior to enrollment. Individuals who were unable to provide consent in a study language (English or Dholuo), pregnant, incarcerated, or were unwilling to provide a home location were ineligible for participation.

Procedures

Enrollment

We recruited PLHIV following an outpatient HIV clinic visit in western Kenya. Due to enrollment constraints, one participant per day (usually the first eligible patient seen that day) was enrolled at each site. After providing informed consent (written or, if illiterate, the consent was read and understanding confirmed with a thumb print), participants were interviewed by study staff using a structured interview tool that included questions on sociodemographic information, HIV history, and TB and IPT histories. Data extracted from clinic charts included results of TB screening on the day of enrollment, medication history, and laboratory values including CD4 cell count and HIV viral load. Phlebotomy was performed for complete blood count (CBC) with differential, hemoglobin, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and HIV viral load (if not available from the clinic chart within the prior 6 months). Study data were collected and managed using REDCap electronic data capture tools [17].

Tuberculin skin test (TSTs)

TSTs were performed by study personnel using 5 tuberculin units (0.1 ml) of purified protein derivative (RT23 solution, Sanofi Pasteur) and read using the ball-point technique and a ruler within 48–96 h [18, 19]. A positive TST was defined as > 5 mm of induration [20].

Sputum collection and TB laboratory testing

Participants were instructed on sputum collection and a “spot” sample was collected at the time of enrollment. If this was unsuccessful, then the participant was provided with a collection container and instructed to collect an early morning specimen upon awakening on the day of TST read. At the enrollment sites, sputum samples were refrigerated and transported same day on ice at 4–8 °C to an ISO 15189-accredited laboratory. Specimens were decontaminated using N-acetyl-L-cysteine and sodium hydroxide and examined using fluorescence microscopy. If one or more acid-fast bacilli (AFB) per equivalent of 100 immersion fields was observed, the slide was considered positive and graded. After re-suspension with phosphate buffer, equal sample volumes were used to perform mycobacterial culture and GeneXpert MTB/RIF (Xpert, Cepheid, Sunnyvale, CA). Mycobacterial culture was performed using a commercial broth method, MGIT Manual Mycobacterial Growth System (Becton-Dickinson, Franklin Lakes, NJ). The Xpert assay assigns a semiquantitative category to positive tests for M. tuberculosis based on cycle threshold (Ct) values: “high,” Ct ≤ 16; “medium,” 16 < Ct ≤ 22; “low,” 22 < Ct ≤ 28; and “very low,” 28 < Ct ≤ 38 [21]. Isolates were identified as M. tuberculosis using the Capilia TB Test Kit (TAUNS, Numazu, Japan). All smear, culture and initial Xpert tests were performed on fresh samples. In the event of a positive Xpert with negative culture, Xpert was re-performed on the frozen sample.

Candidate biomarkers

Candidate biomarkers included CRP, hemoglobin, ESR, monocyte-to-lymphocyte ratio (MLR), and the neutrophil-to-lymphocyte ratio (NLR). Investigators and clinicians were unaware of laboratory test results when assessing participants for TB disease. CRP levels were measured using a high sensitivity assay (Cobas Integra 400 Plus (Roche Diagnostics, Rotkreuz, Switzerland). HIV viral load testing was performed using the COBAS AmpliPrep/COBAS TaqMan HIV-1 Qual test (Roche Diagnostics). CBC with differential was performed using a Coulter ACT 5Diff CP analyzer (Beckman Coulter, France). The MLR and NLR were calculated as the absolute monocyte count or absolute neutrophil count divided by the absolute lymphocyte, respectively.

Six-month follow-up

Study participants were invited back 6 months after enrollment for additional data collection and repeat evaluations for TB. Due to study closure in September 2018, only participants who were enrolled prior to March 2018 were eligible for 6-month follow-up evaluations. Evaluations included a questionnaire, review of medical records, sputum collection for AFB-smear and -culture, and phlebotomy.

Study endpoints and statistical analysis

At enrollment, pulmonary TB was defined as at least one sputum culture positive for M. tuberculosis. At least one valid negative AFB-culture result was required to define participants as not having pulmonary TB (e.g. participants with missing data or only one culture result that was contaminated were excluded from analyses). Analyses were performed using Stata 14 (StataCorp, College Station, TX). TB incidence rates were calculated for participants with a history of IPT using the Stata command “stptime” with failure defined as TB diagnosis (culture-based or clinical diagnosis). Observation time was defined as time from IPT completion until the 6-month follow-up visit. If participants were not seen at 6-month follow-up, observation time was the time from completing IPT to study enrollment. Bivariate logistic regression and Fisher’s exact test were used to assess the association between potential correlates and the outcome of pulmonary TB. 95% confidence intervals (CI) were derived using logistic regression. Distributions of candidate biomarkers were compared by TB status using the Kruskal-Wallis test. The performance of the WHO ICF algorithm, AFB-smear, Xpert, and candidate biomarkers were compared to AFB-culture using sensitivity, specificity, and positive and negative predictive values. Receiver operating characteristic (ROC) curves of candidate biomarkers were determined using the “roctab” command in Stata and asymptotic normal CIs calculated using a published algorithm [22]. Optimal diagnostic cutoffs were determined based on the maximum value of Youden’s index (i.e. sensitivity + specificity – 1) [23]. All statistical tests were two-sided with α = 0.05.

We set a target enrollment of 400 participants based on an estimate that one-half of participants would be ART-naïve with a TB prevalence of 11–12% [24], and that TB prevalence among ART-treated participants would be ~ 3% [25]. An enrollment goal of 400 participants would support the evaluations of screening tests that met the WHO target product profile (90% sensitivity, 70% specificity) at α = 0.05 and establish TB prevalence estimates with error margins of + 2.5% [26].

Ethics approval

This study was approved by the Kenyatta National Hospital-University of Nairobi Ethics and Research Committee and the University of Washington Institutional Review Board.

Results

Between March 2017 and June 2018, 390 PLHIV were screened for study eligibility, among whom two declined study entry, two did not have sputum collected, and three had contaminated sputum cultures. Among 383 participants included in the analysis, the median age was 37 years (interquartile range (IQR) 31–45) (Table 1). Among participants with known ART status (n = 380), all but one participant (99.7%) were taking ART at the time of study enrollment, with a median time on ART of 5.8 years (IQR 2.4–9.2). HIV viral load within the 6 months prior to study enrollment was available for 372 participants with a median value of 20 copies/mL (IQR 0–41) and 93.0% were viral load suppressed by Kenyan Ministry of Health guidelines (< 1000 copies/mL) [27]. A history of TB was present in 15.3% of participants. TST results were available on 330 participants, 21.5% of whom had induration ≥ 5 mm.

Table 1 Participant characteristics overall and by TB status. N (%) unless otherwise indicated
Full size table

Sputum results

Sputum culture results were available for 383 participants, 376 collected as spot samples and seven collected by participants on awakening at home. All sputum AFB-smears were negative. Sputum samples from 5 participants, all collected as spot specimens, were culture-positive for M. tuberculosis. The prevalence of pulmonary TB was 1.3% (95% CI 0.4–3.0%). Xpert was initially reported as positive in 4 samples, with semiquantitative grading of low in one sample and very low in three samples. Only the sample graded as low was culture-positive for M. tuberculosis. Xpert testing was repeated in the 3 culture-negative (very low) samples and all were Xpert negative on repeat testing of the frozen sample. There were no indeterminate results or rifampin resistance detected by Xpert.

IPT and TB

IPT history was available for 381 participants, of whom 339 (87.7%) had received IPT. There were significant differences in baseline characteristic (Table 1) between participants by IPT history for current use of ART (2% ART-naïve among IPT-naïve participants vs. 0% in IPT-treated, p-value = 0.01), median time on ART (0.13 years vs. 6.7 years, p-value < 0.001), median CD4 cell count (342 cells/mm3 vs. 413 cells/mm3, p-value = 0.01), and viral suppression (78.3% vs. 95.1%, p-value < 0.001). No participants had been diagnosed with TB since completing IPT. In 328 participants for whom the date of IPT completion was available, the median time between IPT completion and study enrollment was 1.1 years (IQR 0.8–1.5) and 96.7% reported completing a full 6-month course. Pulmonary TB was diagnosed in two of 47 IPT-naïve participants (4.3, 95% CI 0.5–14.5%) and three of 334 IPT-treated participants (0.9, 95% CI 0.2–2.6%). Two of the five participants with TB disease had a prior history of treated TB, both of whom had received IPT. Isoniazid-monoresistant TB was diagnosed in one participant, who had previously completed treatment with IPT 19 months prior to study enrollment and had a history of TB disease 12 years prior to enrollment. Prior IPT history was not significantly associated with TB disease (OR 0.22, 95% CI 0.04–1.38, p-value 0.11).

Association of TB with predictors

We assessed associations between candidate predictors and TB disease (Table 1). TST ≥ 10 mm was the only predictor that was significantly associated with TB disease (OR 8.1, 95% CI 1.1–59.0, p-value 0.04).

Accuracy of WHO ICF algorithm and candidate biomarkers for diagnosis of TB

The results of a symptom screen performed during a programmatic clinic visit on the same day as study enrollment were available for 267 participants. Clinic-based screening identified 5 participants (1.9%) with a positive WHO ICF screen, 3 of whom were also found to have symptoms during the study enrollment process (kappa = 0.13). When administered by study personnel, the WHO ICF algorithm identified 48 (12.6%) participants with TB symptoms, none of whom had a positive culture for M. tuberculosis. Overall, the WHO ICF algorithm performed by study staff had sensitivity of 0% (95% CI 0–52%), specificity 87% (95% CI 83–90%), positive predictive value 0% (95% CI 0–7%), and negative predictive value 99% (95% CI 97–100%) for identifying pulmonary TB. The initial Xpert result was 20% sensitive (95% CI 1–72%), 99% specific (95% CI 98–100%), positive predictive value of 25% (95% CI 1–81%), and negative predictive value of 99% (95% CI 97–100%) for identifying pulmonary TB.

We evaluated additional candidate biomarkers for TB disease. Various cut-points for the biomarkers were obtained from published studies, including CRP [28,29,30], hemoglobin [31, 32], and monocyte-to-lymphocyte ratio [33], and/or derived from the current study based on Youden’s index. (Table 2) ESR and CRP had the largest areas under the ROC curve. In our study, the use of published cutoffs for CRP at 5 mg/L [30], 8 mg/L [29], and 10 mg/L [28]; hemoglobin at 8 g/dL [31]; and MLR at 0.285 [33]; were associated with poor sensitivity and PPV. In terms of optimizing sensitivity and specificity, the biomarker and cut-point that was closest to the WHO target product profile [12] was CRP > 3.3 mg/L: sensitivity 80% (95% CI, 28–100), specificity 72% (95% CI, 67–77).

Table 2 Test and biomarker performance in identifying tuberculosis. % (95% CI)
Full size table

Follow-up

205 participants were seen at six-month follow-up, four of whom had contaminated sputum cultures. There were significant differences in baseline characteristic (Table 1) between participants seen at six-month follow-up compared to those not seen in follow-up for any symptoms (present in 7.1% with follow-up and 19.2% without follow-up, p-value = 0.001), current cough (2.8% vs. 14.5%, p-value < 0.001), and employment status (9.0% vs 23.8%, p-value < 0.001). The median follow-up time among the included 201 participants was 195 days (IQR 175–230). 11.6% of participants at follow-up reported at least one symptom consistent with TB; only 2.5% of participants seen at 6 months reported at least one symptom consistent with TB at both enrollment and follow-up. One participant was diagnosed with TB 3 days prior to the follow-up visit. This was a clinical diagnosis after the participant presented with symptoms of cough, fever, night sweats and 3 kg weight loss. Among the 201 participants, no sputum samples were positive for M. tuberculosis. Among all study participants who had received IPT (n = 339), the median follow-up time was 1.4 years (IQR 1.22–1.6). Over 478 person-years of observation, the incidence rate of TB following IPT completion (n = 4) was estimated as 0.84 per 100 person-years (95% CI, 0.31–2.23).

Discussion

We investigated the prevalence of pulmonary TB in PLHIV receiving antiretroviral therapy, the majority of whom (88%) had completed IPT at least 6 months prior to study enrollment. We found that IPT-treated participants had high self-reported levels of completion and low annual risk of TB disease. The efficacy of IPT in preventing TB has been well-demonstrated [1, 2, 5,6,7, 34]. In our study, the prevalence of pulmonary TB at enrollment in IPT-naïve participants was 4.3% (95% CI 0.5–14.5%) and in IPT-treated participants was 0.9% (95% CI 0.2–2.6%). Our study did not identify an association between receipt of IPT and decreased frequency of TB (OR 0.22, 95% CI 0.04–1.38), although this was likely due to lack of statistical power. TB prevention trials that were performed in PLHIV not receiving antiretroviral therapy, were notable for the limited duration of protection against TB disease afforded by 6 months of isoniazid, with mixed results on the efficacy of long-term (i.e., 36 months) isoniazid treatment [4,5,6, 34]. A long-term follow-up of the TEMPRANO study of IPT and early ART, demonstrated that the benefits of 6 months of isoniazid, including mortality benefits, were sustained for 6 years in the context of concurrent antiretroviral therapy treatment [1]. The median time since IPT completion in our study was 1.4 years, precluding an evaluation of long-term IPT durability.

One cited reason for low global uptake of IPT has been concern over the potential for selection for drug resistance when isoniazid is administered to patients with unrecognized TB disease [35]. One of the five participants diagnosed with culture-positive TB in our study was found to have isoniazid-mono-resistant disease. This participant had a history of both TB disease and IPT-treatment. Our single participant with isoniazid-resistant TB may reflect the background level of isoniazid monoresistance in western Kenya, approximately 5% [36, 37], although we cannot rule-out that this resistance may have been related to IPT. Although no published trials have demonstrated increased isoniazid resistance due to IPT [5, 38,39,40], these trials stringently evaluated for TB disease prior to IPT and it is not known whether IPT, when used under programmatic conditions, will increase the risk for isoniazid-resistant TB due to inadvertent treatment of individuals with subclinical TB disease.

We found that in participants diagnosed with culture-confirmed TB disease, various TB screens, including WHO ICF screen, Xpert, CRP, hemoglobin, and monocyte-to-lymphocyte ratio, performed poorly. WHO recommends a symptom-based ICF screen to stratify patients to TB preventive therapy or further evaluation for TB disease [9]. In our study, there were no symptomatic participants diagnosed with pulmonary TB. Prior studies have described the poor performance of symptom screen in antiretroviral treated individuals [25, 41, 42], and a recent meta-analysis estimated the pooled sensitivity and specificity of the symptom screen in ART-treated individuals as 51% (95% CI 28–73) and 71% (95% CI 48–86), respectively [11]. The poorer sensitivity of this screen in ART-treated individuals may due to greater immunocompetency predisposing to asymptomatic TB, or frequent TB screening reducing the frequency of individuals with advanced and symptomatic TB [11]. We believe that our study participants were subject to frequent screening for TB as this is recommended in the Kenyan national guidelines [43], and 70% of study participants were screened for TB during a clinic visit on the same day that they were enrolled in the study. However, differences in screening results between the clinic visit (2% with a positive symptom screen) and study screening (13% positive screen) highlight an additional limitation of the ICF screen, its high inter-rater variability.

Although four participants were initially found to be Xpert positive, we believe that three of these results were false positives based on AFB-culture negativity, very low semiquantitative grading, and negative repeat testing on the frozen sample [21]. Quantitative readouts have been previously used to guide investigation of potentially false-positive results. It is unlikely that use of frozen samples for re-testing would have led to a decrease in Xpert sensitivity [44, 45].

WHO issued guidance on target product profiles for community-based triage or referral tests to identify individuals suspected of having TB, suggested a sensitivity greater than 90% and specificity greater than 70% [12]. In our study, neither Xpert nor candidate biomarkers, achieved the product profiles suggested by WHO. A recent systematic review of Xpert accuracy when applied to TB suspects found pooled sensitivities of 67% (95% CI 62–72%) and 81% (95% CI 75–86%) for smear-negative disease and TB in PLHIV, respectively [46]. The poor sensitivity of Xpert was likely due, in part, to paucibacillary disease. CRP has performed well in a number of studies [28], including a study of PLHIV initiating antiretroviral therapy in Uganda that identified sensitivity of 90% and specificity 70% for CRP > 8 mg/L. [29] We did not replicate these findings in our study which applied these tests irrespective of signs or symptoms in antiretroviral-treated PLHIV subject to frequent TB symptoms screens.

Among study participants, 12.2% had not received IPT. Self-reported IPT 6-month completion (97%) and adherence (98%) were high, comparable to recent trial results [1]. As shorter course regimens for TB prevention in PLHIV are introduced [47, 48], it is anticipated that rates of adherence and completion will be high. The TB prevention cascade [49], modeled on the HIV care cascade, is a framework to understand where patient losses are occurring along the continuum of TB prevention. Addressing losses in the TB prevention care continuum may be most cost-effective if targeted at decreasing the number of PLHIV who do not start TB prevention regimens.

Our study has several limitations. We diagnosed few participants with TB disease which limited our ability to perform multivariable analyses due to a limited number of events (TB) per variable [50]. Although we performed sample size calculations prior to study initiation, we enrolled fewer ART-naïve participants than anticipated and TB prevalence was lower in ART-treated participants than estimated. This precluded the evaluation of screening algorithms that used a combination of tests. We collected one sputum sample from each participant, potentially leading to underestimation of the true TB burden in our study population. There were baseline differences between participants who had received IPT compared to those who had not for ART status, CD4 cell count and viral suppression, all of which could increase the risk for TB among IPT-naïve individuals. We did not prospectively assess IPT adherence and remote recall may be subject to bias [51]. We were unable to determine the durability of IPT’s protective effect against TB beyond approximately one-year post-IPT. There were also baseline differences between participants seen at six-month follow-up compared to those not seen at 6 months in the presence of any TB symptom and current cough, both of which were more frequent in participants not seen in follow-up. These differences could potentially have led to a bias of underdiagnosis of TB at the follow-up visit.

In conclusion, in Kenyan PLHIV we found that IPT-uptake was high and TB prevalence was low at a median of 1.4 years after IPT-completion. We also determined that symptom-based screening, Xpert, and candidate biomarkers were insensitive for identifying ART-treated PLHIV who should undergo evaluations for TB disease. Our study results suggest several future directions for research. Although IPT-uptake and reported adherence was high, understanding patients’ reasons for not initiating TB preventive therapy and interventions to increase acceptance and completion of preventive regimens will be important to improve outcomes across the TB prevention continuum. Importantly for PLHIV in high burden settings, the TB prevention cascade includes TB screening following preventive therapy, and potentially repeat administration of TB preventive therapy, as individuals are at risk for re-infection with M. tuberculosis and progression to TB disease [52]. Prospective studies of TB preventive therapy in PLHIV will l need to enroll large numbers of participants given the effectiveness of IPT in antiretroviral-treated individuals. Finally, triage tests for TB risk stratification will need to be carefully studied and validated by ART status.

Availability of data and materials

The datasets analyzed during the current study are available in the figshare repository (https://doi.org/10.6084/m9.figshare.13514728).

Abbreviations

AFB:

Acid-fast bacilli

ART:

Antiretroviral therapy

CBC:

Complete blood count

CI:

Confidence interval

CRP:

C-reactive protein

Ct:

Cycle threshold

ESR:

Erythrocyte sedimentation rate

HIV:

Human immunodeficiency virus

ICF:

Intensified case finding

IPT:

Isoniazid preventive therapy

IQR:

Interquartile range

MLR:

Monocyte-to-lymphocyte ratio

NLR:

Neutrophil-to-lymphocyte ratio

PLHIV:

People living with HIV

TB:

Tuberculosis

TST:

Tuberculin skin test

WHO:

World Health Organization

References

  1. Badje A, Moh R, Gabillard D, Guehi C, Kabran M, Ntakpe JB, et al. Effect of isoniazid preventive therapy on risk of death in west African, HIV-infected adults with high CD4 cell counts: long-term follow-up of the Temprano ANRS 12136 trial. Lancet Glob Health. 2017;5(11):e1080–e9.

    Article  PubMed  Google Scholar 

  2. Danel C, Moh R, Gabillard D, Badje A, Le Carrou J, Ouassa T, et al. A trial of early Antiretrovirals and isoniazid preventive therapy in Africa. N Engl J Med. 2015;373(9):808–22.

    Article  CAS  PubMed  Google Scholar 

  3. World Health Organization. Latent tuberculosis infection. Geneva: Updated and consolidated guidelines for programmatic management; 2018. http://apps.who.int/iris/bitstream/handle/10665/260233/9789241550239-eng.pdf;jsessionid=3DDAACD23540E4E5C725B18D1DAA20AD?sequence=1

    Google Scholar 

  4. Samandari T, Agizew TB, Nyirenda S, Tedla Z, Sibanda T, Mosimaneotsile B, et al. Tuberculosis incidence after 36 months' isoniazid prophylaxis in HIV-infected adults in Botswana: a posttrial observational analysis. AIDS. 2015;29(3):351–9.

    Article  PubMed  Google Scholar 

  5. Martinson NA, Barnes GL, Moulton LH, Msandiwa R, Hausler H, Ram M, et al. New regimens to prevent tuberculosis in adults with HIV infection. N Engl J Med. 2011;365(1):11–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Swaminathan S, Menon PA, Gopalan N, Perumal V, Santhanakrishnan RK, Ramachandran R, et al. Efficacy of a six-month versus a 36-month regimen for prevention of tuberculosis in HIV-infected persons in India: a randomized clinical trial. PLoS One. 2012;7(12):e47400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Rangaka MX, Wilkinson RJ, Boulle A, Glynn JR, Fielding K, van Cutsem G, et al. Isoniazid plus antiretroviral therapy to prevent tuberculosis: a randomised double-blind, placebo-controlled trial. Lancet. 2014;384(9944):682–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mosimaneotsile B, Mathoma A, Chengeta B, Nyirenda S, Agizew TB, Tedla Z, et al. Isoniazid tuberculosis preventive therapy in HIV-infected adults accessing antiretroviral therapy: a Botswana experience, 2004-2006. J Acquir Immune Defic Syndr. 2010;54(1):71–7.

    Article  CAS  PubMed  Google Scholar 

  9. World Health Organization. Guidelines for intensified tuberculosis case-finding and isoniazid preventive therapy for people living with HIV in resource-constrained settings. Geneva: WHO Press; 2011. http://whqlibdoc.who.int/publications/2011/9789241500708_eng.pdf

    Google Scholar 

  10. Getahun H, Kittikraisak W, Heilig CM, Corbett EL, Ayles H, Cain KP, et al. 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. PLoS Med. 2011;8(1):e1000391.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hamada Y, Lujan J, Schenkel K, Ford N, Getahun H. Sensitivity and specificity of WHO's recommended four-symptom screening rule for tuberculosis in people living with HIV: a systematic review and meta-analysis. Lancet HIV. 2018;5(9):e515–e23.

    Article  PubMed  Google Scholar 

  12. World Health Organization. High-priority target product profiles for new tuberculosis diagnostics: report of a consensus meeting. Geneva: WHO; 2014. https://apps.who.int/iris/bitstream/handle/10665/135617/WHO_HTM_TB_2014.18_eng.pdf?sequence=1

    Google Scholar 

  13. World Health Organization. Global Tuberculosis Report 2018. Geneva; 2018. http://apps.who.int/iris/bitstream/handle/10665/274453/9789241565646-eng.pdf?ua=1

  14. GBD 2015 HIV Collaborators. Estimates of global, regional, and national incidence, prevalence, and mortality of HIV, 1980–2015: the Global Burden of Disease Study 2015. Lancet HIV. 2016;3(8):e361–e87.

    Article  Google Scholar 

  15. Enos M, Sitienei J, Ong'ang'o J, Mungai B, Kamene M, Wambugu J, et al. Kenya tuberculosis prevalence survey 2016: challenges and opportunities of ending TB in Kenya. PLoS One. 2018;13(12):e0209098.

    Article  PubMed  PubMed Central  Google Scholar 

  16. World Health Organization. Global Tuberculosis Report 2017, vol. 2017. Geneva. http://apps.who.int/iris/bitstream/10665/259366/1/9789241565516-eng.pdf?ua=1

  17. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.

    Article  PubMed  Google Scholar 

  18. Cobelens F, Van Deutekom H, Draayer-Jansen I, Schepp-Beelen A, Van Gerven P, Mensen M. Tuberculin skin test reactions by time of reading among Dutch travellers. Int J Tuberc Lung Dis. 2003;7(8):758–63.

    CAS  PubMed  Google Scholar 

  19. WHO Tuberculosis Research Office. Tuberculin reaction size on five consecutive days. Bull World Health Organ. 1955;12(1–2):189–96.

    PubMed Central  Google Scholar 

  20. Targeted tuberculin testing and treatment of latent tuberculosis infection. Joint statement of the American Thoracic Society (ATS) and the Centers for Disease Control and Prevention (CDC). Am J Respir Crit Care Med. 2000;161(4 Pt 2):S221–47.

    Google Scholar 

  21. Blakemore R, Nabeta P, Davidow AL, Vadwai V, Tahirli R, Munsamy V, et al. A multisite assessment of the quantitative capabilities of the Xpert MTB/RIF assay. Am J Respir Crit Care Med. 2011;184(9):1076–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.

    Article  CAS  PubMed  Google Scholar 

  23. Perkins NJ, Schisterman EF. The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006;163(7):670–5.

    Article  PubMed  Google Scholar 

  24. Modi S, Cavanaugh JS, Shiraishi RW, Alexander HL, McCarthy KD, Burmen B, et al. Performance of clinical screening algorithms for Tuberculosis intensified case finding among people living with HIV in Western Kenya. PLoS One. 2016;11(12):e0167685.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. LaCourse SM, Cranmer LM, Matemo D, Kinuthia J, Richardson BA, John-Stewart G, et al. Tuberculosis case finding in HIV-infected pregnant women in Kenya reveals poor performance of symptom screening and rapid diagnostic tests. J Acquir Immune Defic Syndr. 2016;71(2):219–27.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Inform. 2014;48:193–204.

    Article  PubMed  Google Scholar 

  27. Mwau M, Syeunda CA, Adhiambo M, Bwana P, Kithinji L, Mwende J, et al. Scale-up of Kenya's national HIV viral load program: findings and lessons learned. PLoS One. 2018;13(1):e0190659.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Yoon C, Chaisson LH, Patel SM, Allen IE, Drain PK, Wilson D, et al. Diagnostic accuracy of C-reactive protein for active pulmonary tuberculosis: a meta-analysis. Int J Tuberc Lung Dis. 2017;21(9):1013–9.

    Article  CAS  PubMed  Google Scholar 

  29. Yoon C, Semitala FC, Atuhumuza E, Katende J, Mwebe S, Asege L, et al. Point-of-care C-reactive protein-based tuberculosis screening for people living with HIV: a diagnostic accuracy study. Lancet Infect Dis. 2017;17(12):1285–92.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Shapiro AE, Hong T, Govere S, Thulare H, Moosa MY, Dorasamy A, et al. C-reactive protein as a screening test for HIV-associated pulmonary tuberculosis prior to antiretroviral therapy in South Africa. AIDS. 2018;32(13):1811–20.

    Article  CAS  PubMed  Google Scholar 

  31. Mupfumi L, Moyo S, Molebatsi K, Thami PK, Anderson M, Mogashoa T, et al. Immunological non-response and low hemoglobin levels are predictors of incident tuberculosis among HIV-infected individuals on Truvada-based therapy in Botswana. PLoS One. 2018;13(1):e0192030.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Kerkhoff AD, Wood R, Vogt M, Lawn SD. Predictive value of anemia for tuberculosis in HIV-infected patients in sub-Saharan Africa: an indication for routine microbiological investigation using new rapid assays. J Acquir Immune Defic Syndr. 2014;66(1):33–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. La Manna MP, Orlando V, Dieli F, Di Carlo P, Cascio A, Cuzzi G, et al. Quantitative and qualitative profiles of circulating monocytes may help identifying tuberculosis infection and disease stages. PLoS One. 2017;12(2):e0171358.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Samandari T, Agizew TB, Nyirenda S, Tedla Z, Sibanda T, Shang N, et al. 6-month versus 36-month isoniazid preventive treatment for tuberculosis in adults with HIV infection in Botswana: a randomised, double-blind, placebo-controlled trial. Lancet. 2011;377(9777):1588–98.

    Article  CAS  PubMed  Google Scholar 

  35. Pathmanathan I, Ahmedov S, Pevzner E, Anyalechi G, Modi S, Kirking H, et al. TB preventive therapy for people living with HIV: key considerations for scale-up in resource-limited settings. Int J Tuberc Lung Dis. 2018;22(6):596–605.

    Article  CAS  PubMed  Google Scholar 

  36. Jezmir J, Cohen T, Zignol M, Nyakan E, Hedt-Gauthier BL, Gardner A, et al. Use of lot quality assurance sampling to ascertain levels of drug resistant Tuberculosis in Western Kenya. PLoS One. 2016;11(5):e0154142.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Sanchez-Padilla E, Ardizzoni E, Sauvageot D, Ahoua L, Martin A, Varaine F, et al. Multidrug- and isoniazid-resistant tuberculosis in three high HIV burden African regions. Int J Tuberc Lung Dis. 2013;17(8):1036–42.

    CAS  PubMed  Google Scholar 

  38. Balcells ME, Thomas SL, Godfrey-Faussett P, Grant AD. Isoniazid preventive therapy and risk for resistant tuberculosis. Emerg Infect Dis. 2006;12(5):744–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. van Halsema CL, Fielding KL, Chihota VN, Russell EC, Lewis JJ, Churchyard GJ, et al. Tuberculosis outcomes and drug susceptibility in individuals exposed to isoniazid preventive therapy in a high HIV prevalence setting. AIDS. 2010;24(7):1051–5.

    Article  PubMed  CAS  Google Scholar 

  40. Sibanda T, Tedla Z, Nyirenda S, Agizew T, Marape M, Miranda AG, et al. Anti-tuberculosis treatment outcomes in HIV-infected adults exposed to isoniazid preventive therapy in Botswana. Int J Tuberc Lung Dis. 2013;17(2):178–85.

    Article  CAS  PubMed  Google Scholar 

  41. Ahmad Khan F, Verkuijl S, Parrish A, Chikwava F, Ntumy R, El-Sadr W, et al. Performance of symptom-based tuberculosis screening among people living with HIV: not as great as hoped. AIDS. 2014;28(10):1463–72.

    Article  PubMed  Google Scholar 

  42. Rangaka MX, Wilkinson RJ, Glynn JR, Boulle A, van Cutsem G, Goliath R, et al. Effect of antiretroviral therapy on the diagnostic accuracy of symptom screening for intensified tuberculosis case finding in a south African HIV clinic. Clin Infect Dis. 2012;55(12):1698–706.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. National Tuberculosis, Leprosy & Lung Disease Program. Guideline for integrated tuberculosis, leprosy and lung disease in Kenya. 2017. https://www.nltp.co.ke/download/guideline-for-intergrated-tubeculosis-leprosy-lung-disease-in-kenya/.

    Google Scholar 

  44. Shao W, Khin S, Kopp WC. Characterization of effect of repeated freeze and thaw cycles on stability of genomic DNA using pulsed field gel electrophoresis. Biopreserv Biobank. 2012;10(1):4–11.

    Article  CAS  PubMed  Google Scholar 

  45. Steingart KR, Schiller I, Horne DJ, Pai M, Boehme CC, Dendukuri N. Xpert(R) MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev. 2014;1:CD009593.

    Google Scholar 

  46. Horne DJ, Kohli M, Zifodya JS, Schiller I, Dendukuri N, Tollefson D, et al. Xpert MTB/RIF and Xpert MTB/RIF ultra for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev. 2019;6:CD009593.

    PubMed  Google Scholar 

  47. Sterling TR, Scott NA, Miro JM, Calvet G, La Rosa A, Infante R, et al. Three months of weekly rifapentine and isoniazid for treatment of mycobacterium tuberculosis infection in HIV-coinfected persons. AIDS. 2016;30(10):1607–15.

    Article  CAS  PubMed  Google Scholar 

  48. Swindells S, Ramchandani R, Gupta A, Benson C, Leon-Cruz J, Omoz-Oarhe A, Juste M, Lama J, Badal-Faesen S, Moran L, Fletcher C, Nuermberger E, Chaisson R. One month of rifapentine/isoniazid to prevent TB in people with HIV: Brief-TB/A5279[abstract]. Boston, MA: 25th Annual Conference on Retroviruses and Opportunistic Infections; Mar 4–7; 2018. Abstract nr. 37LB

    Google Scholar 

  49. Alsdurf H, Hill PC, Matteelli A, Getahun H, Menzies D. The cascade of care in diagnosis and treatment of latent tuberculosis infection: a systematic review and meta-analysis. Lancet Infet Dis. 2016;16(11):1269–78.

  50. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9.

    Article  CAS  PubMed  Google Scholar 

  51. Kagee A, Nel A. Assessing the association between self-report items for HIV pill adherence and biological measures. AIDS Care. 2012;24(11):1448–52.

    Article  PubMed  Google Scholar 

  52. Sumner T, Houben RM, Rangaka MX, Maartens G, Boulle A, Wilkinson RJ, et al. Post-treatment effect of isoniazid preventive therapy on tuberculosis incidence in HIV-infected individuals on antiretroviral therapy. AIDS. 2016;30(8):1279–86.

    Article  CAS  PubMed  Google Scholar 

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Funding

This research was funded in part by a 2015 developmental grant from the University of Washington/Fred Hutch Center for AIDS Research (NIH grant AI027757); the National Center For Advancing Translational Sciences of the NIH (UL1 TR002319); and the Firland Foundation. The funders had no role in the study design, analysis, interpretation of the data, or writing of the manuscript.

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Authors and Affiliations

  1. Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, USA

    Jill K. Gersh, Ruanne V. Barnabas & Sylvia M. Lacourse

  2. Department of Global Health, University of Washington, 325 9th Ave, Box 359762, Seattle, WA, 98102, USA

    Ruanne V. Barnabas, John Kinuthia, Sylvia M. Lacourse & David J. Horne

  3. Department of Epidemiology, University of Washington, Seattle, WA, USA

    Ruanne V. Barnabas

  4. Department of Obstetrics and Gynaecology, Kenyatta National Hospital, Nairobi, Kenya

    Daniel Matemo, John Kinuthia & Jerphason Mecha

  5. School of Public Health and Community Development Maseno University, Kisumu, Kenya

    Daniel Matemo

  6. Albers School of Business and Economics, Seattle University, Seattle, WA, USA

    Zachary Feldman

  7. Department of Pediatrics, Department of Medicine, Baylor College of Medicine, Houston, TX, USA

    Alex J. Warr

  8. National Tuberculosis, Leprosy, and Lung Disease Program, Nairobi, Kenya

    Maureen Kamene

  9. Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Harborview Medical Center, Seattle, WA, USA

    David J. Horne

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  1. Jill K. Gersh
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Contributions

JKG, RVB, and DJH designed the study. DM, JK, JM, and AJW oversaw the local collection of data. JKG, ZF, and DJH analyzed the data. JKG, RB, SML, MK, and DJH interpreted the results. JKG and DJH wrote the first draft of the manuscript. JGK, RVB, DM, JK, ZF, SML, JM, AJW, MK and DJH critically revised the manuscript and approved the final manuscript.

Corresponding author

Correspondence to David J. Horne.

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Ethics approval and consent to participate

This study was approved by the Kenyatta National Hospital-University of Nairobi Ethics and Research Committee (KNH-ERC/A/327) and the University of Washington Institutional Review Board (CR00001377). All methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all study participants.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Gersh, J.K., Barnabas, R.V., Matemo, D. et al. Pulmonary tuberculosis screening in anti-retroviral treated adults living with HIV in Kenya. BMC Infect Dis 21, 218 (2021). https://doi.org/10.1186/s12879-021-05916-z

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Keywords

BMC Infectious Diseases

ISSN: 1471-2334

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