Géraldine Daneau1, Jozefien Buyze2, Djibril Wade1,3,4, Papa Alassane Diaw3, Tandakha Ndeye Dieye3, Thai Sopheak5, Eric Florence2, Lutgarde Lynen2, Luc Kestens1,4. 1. Department of Biomedical Sciences, Immunology Unit, Institute of Tropical Medicine, Antwerp, Belgium. 2. Department of Clinical Sciences, HIV/AIDS and infectious diseases Unit, Institute of Tropical Medicine, Antwerp, Belgium. 3. Immunology Unit, Laboratory of Bacteriology Virology, Le Dantec University Teaching Hospital, Check Anta Diop University, Dakar, Senegal. 4. Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium. 5. Infectious Diseases Department, Sihanouk Hospital Center of HOPE, Phnom Penh, Cambodia.
Abstract
BACKGROUND: CD4 counts are currently used to assess HIV patients for treatment eligibility and to monitor antiretroviral response to treatment. The emerging point-of-care devices could fill an important gap in resource-limited settings. However, the accuracy of CD4-counting instruments is diverse and data on how CD4 measurement errors have an impact on clinical decisions are lacking. METHODS: Clinicians were queried on the use of CD4 results in their clinical setting. Subsequently, the effect of CD4 measurement errors on treatment initiation was put in a statistical model. Based on clinical CD4 databases from Belgium, Cambodia, and Senegal, the percentage of unchanged clinical decisions was calculated (treatment initiation should start within a 3-month delay [one visit]) for escalating CD4 measurement errors, taking into account the strict or preventive application of CD4 thresholds at 350 or 500 cells/µl used by clinicians. RESULTS: To ensure that the treatment was initiated appropriately for at least 95% of patients, an error of 5 - 10 cells/µl was allowed. This is significantly smaller than the bias of ±50 cells/µl most clinicians considered acceptable. For limits of agreement (LOA, 1.96 x error) of 100 cells/µl, corresponding to most CD4 instrument evaluations, the misclassification rate of patients was found to be 3 - 28% at the threshold of 350 cells/µl (strict or flexible), and 13 - 20% at 500 cells/µl. CONCLUSIONS: The maximum allowed CD4 bias on results from new CD4 technologies should not exceed 50 cells/µl (LOA 100 cells/µl) when applied for treatment initiation, to ensure at least 72% of correct clinical decisions.
BACKGROUND:CD4 counts are currently used to assess HIVpatients for treatment eligibility and to monitor antiretroviral response to treatment. The emerging point-of-care devices could fill an important gap in resource-limited settings. However, the accuracy of CD4-counting instruments is diverse and data on how CD4 measurement errors have an impact on clinical decisions are lacking. METHODS: Clinicians were queried on the use of CD4 results in their clinical setting. Subsequently, the effect of CD4 measurement errors on treatment initiation was put in a statistical model. Based on clinical CD4 databases from Belgium, Cambodia, and Senegal, the percentage of unchanged clinical decisions was calculated (treatment initiation should start within a 3-month delay [one visit]) for escalating CD4 measurement errors, taking into account the strict or preventive application of CD4 thresholds at 350 or 500 cells/µl used by clinicians. RESULTS: To ensure that the treatment was initiated appropriately for at least 95% of patients, an error of 5 - 10 cells/µl was allowed. This is significantly smaller than the bias of ±50 cells/µl most clinicians considered acceptable. For limits of agreement (LOA, 1.96 x error) of 100 cells/µl, corresponding to most CD4 instrument evaluations, the misclassification rate of patients was found to be 3 - 28% at the threshold of 350 cells/µl (strict or flexible), and 13 - 20% at 500 cells/µl. CONCLUSIONS: The maximum allowed CD4 bias on results from new CD4 technologies should not exceed 50 cells/µl (LOA 100 cells/µl) when applied for treatment initiation, to ensure at least 72% of correct clinical decisions.
Authors: John Bainbridge; Wes Rountree; Raul Louzao; John Wong; Liam Whitby; Thomas N Denny; David Barnett Journal: Cytometry B Clin Cytom Date: 2017-05-16 Impact factor: 3.058