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      Head to head comparisons in performance of CD4 point-of-care assays: a Bayesian meta-analysis (2000–2013)

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          Abstract

          Timely detection, staging, and treatment initiation are pertinent to controlling HIV infection. CD4+ cell-based point-of-care (POC) devices offer the potential to rapidly stage patients, and decide on initiating treatment, but a comparative evaluation of their performance has not yet been performed. With this in mind, we conducted a systematic review and meta-analyses. For the period January 2000 to April 2014, 19 databases were systematically searched, 6619 citations retrieved, and 25 articles selected. Diagnostic performance was compared across devices (i.e., PIMA, CyFlow, miniPOC, MBioCD4 System) and across specimens (i.e., capillary blood vs. venous blood). A Bayesian approach was used to meta-analyze the data. The primary outcome, the Bland–Altman (BA) mean bias (which represents agreement between cell counts from POC device and flow cytometry), was analyzed with a Bayesian hierarchical normal model. We performed a head-to-head comparison of two POC devices including the PIMA and PointCareNOW CD4. PIMA appears to perform better vs. PointCareNOW with venous samples (BA mean bias: –9.5 cells/μL; 95% CrI: –37.71 to 18.27, vs. 139.3 cells/μL; 95% CrI: –0.85 to 267.4, mean difference = 148.8, 95% CrI: 11.8, 285.8); importantly, PIMA performed well when used with capillary samples (BA mean bias: 2.2 cells/μL; 95% CrI: –19.32 to 23.6). Sufficient data were available to allow pooling of sensitivity and specificity data only at the 350 cells/μL cutoff. For PIMA device sensitivity 91.6 (84.7–95.5) and specificity was 94.8 (90.1–97.3), respectively. There were not sufficient data to allow comparisons between any other devices. PIMA device was comparable to flow cytometry. The estimated differences between the CD4+ cell counts of the device and the reference was small and best estimated in capillary blood specimens. As the evidence stands, the PointCareNOW device will need to improve prior to widespread use and more data on MBio and MiniPOC are needed. Findings inform implementation of PIMA and improvements in other CD4 POC device prior to recommending widespread use.

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          Most cited references 20

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          Point-of-Care Testing for Infectious Diseases: Diversity, Complexity, and Barriers in Low- And Middle-Income Countries

          Madhukar Pai and colleagues discuss a framework for envisioning how point-of-care testing can be applied to infectious diseases in low- and middle-income countries.
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            Effect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study.

            Loss to follow-up of HIV-positive patients before initiation of antiretroviral therapy can exceed 50% in low-income settings and is a challenge to the scale-up of treatment. We implemented point-of-care counting of CD4 cells in Mozambique and assessed the effect on loss to follow-up before immunological staging and treatment initiation. In this observational cohort study, data for enrolment into HIV management and initiation of antiretroviral therapy were extracted retrospectively from patients' records at four primary health clinics providing HIV treatment and point-of-care CD4 services. Loss to follow-up and the duration of each preparatory step before treatment initiation were measured and compared with baseline data from before the introduction of point-of-care CD4 testing. After the introduction of point-of-care CD4 the proportion of patients lost to follow-up before completion of CD4 staging dropped from 57% (278 of 492) to 21% (92 of 437) (adjusted odds ratio [OR] 0·2, 95% CI 0·15-0·27). Total loss to follow-up before initiation of antiretroviral treatment fell from 64% (314 of 492) to 33% (142 of 437) (OR 0·27, 95% CI 0·21-0·36) and the proportion of enrolled patients initiating antiretroviral therapy increased from 12% (57 of 492) to 22% (94 of 437) (OR 2·05, 95% CI 1·42-2·96). The median time from enrolment to antiretroviral therapy initiation reduced from 48 days to 20 days (p<0·0001), primarily because of a reduction in the median time taken to complete CD4 staging, which decreased from 32 days to 3 days (p<0·0001). Loss to follow-up between staging and antiretroviral therapy initiation did not change significantly (OR 0·84, 95% CI 0·49-1·45). Point-of-care CD4 testing enabled clinics to stage patients rapidly on-site after enrolment, which reduced opportunities for pretreatment loss to follow-up. As a result, more patients were identified as eligible for and initiated antiretroviral treatment. Point-of-care testing might therefore be an effective intervention to reduce pretreatment loss to follow-up. Absolute Return for Kids and UNITAID. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Utility of routine viral load, CD4 cell count, and clinical monitoring among adults with HIV receiving antiretroviral therapy in Uganda: randomised trial

              Objective To evaluate the use of routine laboratory monitoring in terms of clinical outcomes among patients receiving antiretroviral therapy (ART) in Uganda. Design Randomised clinical trial Setting A home based ART programme in rural Uganda. Participants All participants were people with HIV who were members of the AIDS Support Organisation. Participants had CD4 cell counts 500 copies/mL occurring more than three months after the start of ART. After adjustment for age, sex, baseline CD4 count, viral load, and body mass index, the rate of new AIDS defining events or death was higher in the clinical arm than the viral load arm (adjusted hazard ratio 1.83, P=0.002) or the CD4 arm (1.49, P=0.032). There was no significant difference between the CD4 arm and the viral load arm (1.23, P=0.31). Conclusion In patients receiving ART for HIV infection in Uganda, routine laboratory monitoring is associated with improved health and survival compared with clinical monitoring alone. Trial registration Clinical Trials NCT00119093.
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                Author and article information

                Contributors
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                Journal
                SOR-MED
                ScienceOpen Research
                ScienceOpen
                2199-1006
                11 July 2014
                17 July 2015
                : 0 (ID: d4a25ca8-6f2e-4ecd-9e65-10e132c05783 )
                : 0
                : 1-13
                Affiliations
                [1 ]Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, QC, Canada
                [2 ]Division of Clinical Epidemiology, McGill University Health Centre, Montreal, QC, Canada
                [3 ]Medical Library, Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada
                [4 ]Department of Medicine, McGill University, Montreal, QC, Canada
                Author notes
                [* ]Corresponding author's e-mail address: nitika.pai@ 123456mcgill.ca
                Article
                2902:XE
                10.14293/S2199-1006.1.SOR-MED.A4QF5Y.v2
                © 2014 S. Wilkinson et al.

                This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

                Page count
                Figures: 4, Tables: 3, References: 31, Pages: 13
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                Comments

                I'm particularly interested on how you performed the Bayesian statistics. Which scripts and software did you use?
                2014-10-30 15:47 UTC
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