Can clinical algorithms be used to detect people with virologic failure?

CD4 criteria improves the sensitivity of a clinical algorithm developed to identify viral failure in HIV-positive patients on antiretroviral therapy.

Evans DH, Fox MP, Maskew M, McNamara L, MacPhail P, Mathews C, Sanne I. J Int AIDS Soc. 2014 Sep 15;17:19139. doi: 10.7448/IAS.17.1.19139. eCollection 2014.

Introduction: Several studies from resource-limited settings have demonstrated that clinical and immunologic criteria are poor predictors of virologic failure, confirming the need for viral load monitoring or at least an algorithm to target viral load testing. We used data from an electronic patient management system to develop an algorithm to identify patients at risk of viral failure using a combination of accessible and inexpensive markers.

Methods: We analyzed data from HIV-positive adults initiated on antiretroviral therapy (ART) in Johannesburg, South Africa, between April 2004 and February 2010. Viral failure was defined as ≥2 consecutive HIV-RNA viral loads >400 copies/ml following suppression ≤400 copies/ml. We used Cox-proportional hazards models to calculate hazard ratios (HR) and 95% confidence intervals (CI). Weights for each predictor associated with virologic failure were created as the sum of the natural logarithm of the adjusted HR and dichotomized with the optimal cut-off at the point with the highest sensitivity and specificity (i.e. ≤4 vs. >4). We assessed the diagnostic accuracy of predictor scores cut-offs, with and without CD4 criteria (CD4 <100 cells/mm3; CD4 < baseline; >30% drop in CD4), by calculating the proportion with the outcome and the observed sensitivity, specificity, positive and negative predictive value of the predictor score compared to the gold standard of virologic failure.

Results: We matched 919 patients with virologic failure (1:3) to 2756 patients without. Our predictor score included variables at ART initiation (i.e. gender, age, CD4 count <100 cells/mm3, WHO stage III/IV and albumin) and laboratory and clinical follow-up data (drop in haemoglobin, mean cell volume (MCV) <100 fl, CD4 count <200 cells/mm3, new or recurrent WHO stage III/IV condition, diagnosis of new condition or symptom and regimen change). Overall, 51.4% had a score ≥4 and 48.6% had a score <4. A predictor score including CD4 criteria performed better than a score without CD4 criteria and better than WHO clinico-immunological criteria or WHO clinical staging to predict virologic failure (sensitivity 57.1% vs. 40.9%, 25.2% and 20.9%, respectively).

Conclusions: Predictor scores or risk categories, with CD4 criteria, could be used to identify patients at risk of virologic failure in resource-limited settings so that these patients may be targeted for focused interventions to improve HIV treatment outcomes.

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Editor’s notes: Monitoring people for antiretroviral treatment failure is challenging in resource-limited settings. Few countries have access to viral load monitoring and instead rely on immunological criteria or World Health Organization (WHO) clinical staging criteria to determine who is failing therapy. Unfortunately these methods have poor sensitivity and specificity for detecting virologic failure leading to late detection of virologic failure (poor sensitivity) and unnecessary switching (poor specificity).

Using electronic capture data from a large antiretroviral therapy (ART) programme in Johannesburg with access to routine viral load monitoring, the authors propose a new algorithm for identifying people at risk of confirmed virologic failure. The strength of their predictive model is that it incorporates simple clinical and laboratory markers measured at the visit, before virologic failure was confirmed. This gave healthcare workers the opportunity to intervene early. Disappointingly, as others have found, the sensitivity and specificity of their predictive model remain sub-optimal, albeit that it performs better than WHO immunological and clinical criteria alone. While clinical algorithms may be needed as an interim measure to ration access to viral load monitoring, the ultimate goal must be to increase access to low-cost viral load monitoring in these settings.

HIV Treatment
Africa
South Africa
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