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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 26

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        STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT

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          QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

          In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
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            Patient retention in antiretroviral therapy programs up to three years on treatment in sub-Saharan Africa, 2007–2009: systematic review

            Objectives To estimate the proportion of all-cause adult patient attrition from antiretroviral therapy (ART) programs in service delivery settings in sub-Saharan Africa through 36 months on treatment. Methods We identified cohorts within Ovid Medline, ISI Web of Knowledge, Cochrane Database of Systematic Reviews and four conference abstract archives. We summarized retention rates from studies describing observational cohorts from sub-Saharan Africa reporting on adult HIV 1- infected patients initiating first-line three-drug ART. We estimated all-cause attrition rates for 6, 12, 18, 24, or 36 months after ART initiation including patients who died or were lost to follow-up (as defined by the author), but excluding transferred patients. Results We analysed 33 sources describing 39 cohorts and 226 307 patients. Patients were more likely to be female (median 65%) and had a median age at initiation of 37 (range 34–40). Median starting CD4 count was 109 cells/mm3. Loss to follow-up was the most common cause of attrition (59%), followed by death (41%). Median attrition at 12, 24 and 36 months was 22.6% (range 7%–45%), 25% (range 11%–32%) and 29.5% (range 13%–36.1%) respectively. After pooling data in a random-effects meta-analysis, retention declined from 86.1% at 6 months to 80.2% at 12 months, 76.8% at 24 months and 72.3% at 36 months. Adjusting for variable follow-up time in a sensitivity analysis, 24 month retention was 70.0% (range: 66.7%–73.3%), while 36 month retention was 64.6% (range: 57.5%–72.1%). Conclusions Our findings document the difficulties in retaining patients in care for lifelong treatment, and the progress being made in raising overall retention rates.
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              Author and article information

              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
              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
              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.

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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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