Browsing by Author "Smit, Johan Du Preez"
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- ItemUsing multistage pooling mechanisms to optimise HIV viral load testing.(Stellenbosch : Stellenbosch University, 2022-04) Smit, Johan Du Preez; Nieuwoudt, MJ; Van Zyl, GU; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.ENGLISH SUMMARY: HIV viral load (VL) monitoring is necessary to determine if antiretroviral (ART) treatment is successful. When the VL is virologically suppressed, patients are less likely to transmit HIV to others. The use of ART has resulted in a decreased risk of virologic failure (VF). Therefore, a lower prevalence of VF in patients receiving treatment and the high sensitivity (assays can quantify down to 20 cp/mL) of VL tests, ensure pooled testing for VF monitoring to be effective. However, such testing methods have never been implemented on a large scale, likely due to manual pooling of samples and testing each sample in a positive pool (referred to as pool deconvolution) being mundane and repetitive processes. The various steps in the pooling process make it more susceptible to human error. In this project, four models of four pooling methods were developed to reduce costs in testing. These methods are two multi-stage (Qualitative and Quantitative) and two matrix pooling (Matrix-or and Matrix-and) methods. The effects of VL dilution within a pool and error rates coupled to assays were included within testing. Data was simulated using gamma distributions fit to over 17 000 samples recorded by anonymized extraction of routine HIV-1 VL data from a South African public service diagnostic laboratory. These simulations provided a basis for optimisation by allowing an optimisation database to record the results (error rates as well as test reductions) of testing using each possible deconvolution for every prevalence of HIV-positive samples. The tests were conducted on more than 2 000 samples per deconvolution. A cost function was developed that calculates a single score based on false-negative and false-positive errors received during testing, running costs, and testing time. This score was used to find optimal testing procedures based on HIV prevalence in the data. The HIV prevalence within data is estimated using two types of estimation, with each containing three different methods, including machine learning prediction. Each optimised testing method and its cost function, which was weighted by evaluating each method using a training dataset, was then tested on unseen data. The results indicated that the Quantitative method was optimal for testing on the dataset, with prevalence larger than 24.3%. This method decreased costs by 18.0%, increased throughput by 17.7% and achieved a meaningful sensitivity of 96.0%. This method is effective for prevalence range up to 27.0%. It was observed that the Qualitative method achieved optimal results for higher prevalence than the other methods and when the prevalence is reduced the Matrix-and method would be optimum. A combined testing method was developed which increased efficiency to 19.0% and sensitivity increased to 98.5%. Each method was implemented on a Raspberry PI, to reduce human error, and is accessible using a graphical user interface (GUI). Guidelines for the adaptation of these methods to Covid-19 and other diseases were also developed.