Viral load monitoring is routinely carried out in PLHIVs in most countries. In low income countries, viral load tests have been previously restricted to drug resistance studies and diagnosis of HIV-exposed infants, possibly due to inadequate resources. In Kenya, viral load testing is now recommended for monitoring of patients on ART, replacing CD4 testing, which has since been reserved for baseline screening to identify patients eligible for cryptococcal meningitis prophylaxis (CD4 < 100).
Our study presents data on discrepancies in classification of treatment failure based on clinical, immunological and virological criteria. Consequently, we demonstrate that the use of clinical and immunological criteria alone may result in misclassification of patients with viral suppression as treatment failure leading to unnecessary, untimely, and incorrect switching to second line regimens.
Our study adds to the literature from studies done elsewhere which also evaluated misclassification of treatment failure in patients on ART. In a prospective cohort study in Uganda, immunological criteria failed to identify all the genuine treatment failures and had a low sensitivity . Studies in Kenya and Uganda by Moor et al. (2008), Kantor et al. (2009) and Mermin et al. (2011) were able to illustrate that virological monitoring is the most accurate way of identifying treatment failure as compared to clinical and/or immunological criteria [10-12]. These studies are important as the use of clinical and immunological criteria alone in treatment failure identification has been associated with increased resistance to HIV [13, 14].
In the course of treatment of PLHIVs, virological failure occurs early, followed by immunological and clinical failure. Therefore, there is need for timely and accurate treatment failure diagnosis based on viral load testing in order to avoid early or delayed switching of patients to second-line ART regimen. Switching early to second-line regimen increases the cost burden of HIV treatment and minimizes options for subsequent regimens, should they be required . On the other hand, delayed switching to a second-line regimen has been associated with increased drug resistance, morbidity and mortality .
In this study, the age of the patient was associated with virological failure. Majority of patients who had virological failure fell within the age range of 20–40 years. This data is similar to that of a study conducted in Kenya by Hassan (2014) where young age was a significant predictor for virological failure and drug resistance . Young HIV-infected patients constitute a special cohort experiencing challenges such as stigma, peer pressure, adherence and discrimination . This, in turn, might affect adherence on ART, increasing their chances of virological (hence treatment) failure.
Patient viral load must necessarily be interpreted with caution - viral load is affected by inter-patient variation, laboratory errors, opportunistic infections, pre-ART viral load and the ART regimen [18, 19]. These factors can cause transient viral ‘blips’ which could be misinterpreted as virological failure. After such blips, viral load spontaneously drops to undetectable levels without change in ART regimen. It is possible that any of these factors may have influenced the findings reported in this paper.
Study limitations and strengths
An important study limitation is that the dataset analysed was based on patients with suspected treatment failure only and hence the findings cannot be generalized to the general population of PLHIVs in care. A further limitation is that this study investigated over-diagnosis of treatment failure in the absence of viral load testing. In the absence of viral load monitoring, this study cannot investigate the full scale of true treatment failure. Finally, incomplete documentation of viral load data was a notable challenge. This could possibly be due to clinicians failing to update all the patient details at the time of requesting for the viral load test. This limitation can be overcome through mentorship sessions and on-job training.
A key strength of this study is the availability of a large sample size, making it possible to analyze variables at bivariate and multivariate levels. Data quality was high and obtained entirely from the online viral load monitoring system in Kenya.
The findings from this study add to the body of knowledge on clinical and laboratory challenges of managing HIV-infected patients in low-resource settings, and build the case for the need for viral load monitoring of patients on ART.