| Literature DB >> 27780216 |
Nádia Sitoe1, Rosa Macamo1, Bindiya Meggi1, Ocean Tobaiwa2, Osvaldo Loquiha2, Timothy Bollinger2, Lara Vojnov2, Ilesh Jani1.
Abstract
BACKGROUND: In resource-limited countries, CD4 T-cell (CD4) testing continues to be used for determining antiretroviral therapy (ART) initiation eligibility and opportunistic infection monitoring. To support expanded access to CD4 testing, simple and robust technologies are necessary. We conducted this study to evaluate the performance of a new Point-of-Care (POC) CD4 technology, the MyT4, compared to conventional laboratory CD4 testing.Entities:
Mesh:
Year: 2016 PMID: 27780216 PMCID: PMC5079624 DOI: 10.1371/journal.pone.0165163
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Patient sample demographics.
| Female | Male | |||
|---|---|---|---|---|
| 32 (14–41) | 32.5 (15–43) | |||
| 6 | 8 | 9 | 11 | |
| 23 | 29 | 27 | 28 | |
| 24 | 15 | 24 | 23 | |
| 37 | 38 | 29 | 28 | |
| 18 | 18 | 3 | 2 | |
| 108 (54%) | 92 (46%) | |||
Fig 1Regression plot (a) and Bland-Altman difference plot (b) for comparison of the MyT4 with the BD FACSCalibur™.
(a) In the Bland-Altman difference plot the vertical axis is the difference between the CD4 counts from the two methods while the horizontal axis is the average CD4 count from each patient.
Misclassification and agreement statistics.
| Misclassification | Agreement | ||||||
|---|---|---|---|---|---|---|---|
| Threshold | Upward | Downward | Total (95% CI) | Total (N) | Upward | Downward | Total |
| (95% CI) | (95% CI) | (95% CI) | (95% CI) | (N) | |||
| 350 | 15.6% | 10.60% | 12.50% | 25 | 89.80% | 83.90% | 175 |
| (8.3–25.6) | (5.8–17.4) | (8.3–18.4) | (85.8–93.8) | (776–90.1) | |||
| 500 | 53% | 10.40% | 7.50% | 15 | 91.10% | 93.50% | 185 |
| (2.0–11.1) | (4.9–18.9) | (4.3–13.7) | (86.7–95.6) | (90.23–96.8) | |||
| 43 | 157 | ||||||