Background. Multidrug-and extensively drug-resistant tuberculosis (MDR-TB and XDR-TB) threaten local and global control of the disease. The molecular line-probe assay (LPA) provides rapid diagnosis and early management of MDR-TB. The LPA detects mutations of katG and inhA genes associated with isoniazid (INH) resistance in Mycobacterium tuberculosis isolates. The katG and inhA genes are associated with high-and low-level INH resistance, respectively, as well as cross-resistance to ethionamide in the case of inhA gene mutations. Patients with MDR-TB due to an inhA mutation could benefit from the use of high-dose INH -instead of ethionamide -in their MDR-TB regimen. Objectives. To determine the frequencies of katG and inhA mutations that conferred INH resistance among MDR-TB isolates during 2014 -2016 in Free State (FS) Province of South Africa. Methods. We retrospectively reviewed MDR-TB isolates assayed with GenoType MTBDRplus (Hain Lifescience, Germany) (LPA) at the central TB laboratory of Universitas Academic Hospital, Bloemfontein, FS, and calculated the frequencies of katG and inhA mutations. Results. Among 918 MDR-TB isolates, the prevalence of katG, inhA and katG plus inhA mutations was 63.9%, 13.4% and 22.7%, respectively. Approximately 60% (n=536; 58.4%) of the isolates were obtained from male patients. The patients' ages ranged from 1 to 89 (median 37) years. The Xhariep district had the highest incidence of INH resistance-conferring mutations in the province. Conclusions. katG-associated mutations are the predominant INH resistance-conferring mechanism among MDR-TB isolates in the FS. Patients infected with isolates that harbour the katG mutation are unlikely to benefit from high-dose INH therapy in the bedaquiline (BDQ)-containing modified short MDR-TB regimen. They may, however, benefit from the inclusion of ethionamide in the regimen. Dual katG and inhA gene mutations make these patients unlikely to respond to either high-dose INH or ethionamide and should now be considered for either the BDQ-containing long MDR-TB regimen or an individualised treatment regimen, depending on fluoroquinolone susceptibility. Clinicians should familiarise themselves with interpreting various INH resistance-conferring mutation results and their implications for management of MDR-TB treatment.S Afr Med J 2019;109(9):659-664. https://doi.

Background Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians. Methods We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. Results We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. Conclusion Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.

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Background Rifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen guided by drug susceptibility test (DST) results. In most settings, DST information is not available or is limited to isoniazid and fluoroquinolones. Whole genome sequencing could more accurately guide individualized treatment as the full drug resistance profile is obtained with a single test. Whole genome sequencing has not reached its full potential for patient care, in part due to the complexity of translating a resistance profile into the most effective individualized regimen. Methods We developed a treatment recommender clinical decision support system (CDSS) and an accompanying web application for user-friendly recommendation of the optimal individualized treatment regimen to a clinician. Results Following expert stakeholder meetings and literature review, nine drug features and 14 treatment regimen features were identified and quantified. Using machine learning, a model was developed to predict the optimal treatment regimen based on a training set of 3895 treatment regimen-expert feedback pairs. The acceptability of the treatment recommender CDSS was assessed as part of a clinical trial and in a routine care setting. Within the clinical trial setting, all patients received the CDSS recommended treatment. In 8 of 20 cases, the initial recommendation was recomputed because of stock out, clinical contra-indication or toxicity. In routine care setting, physicians rejected the treatment recommendation in 7 out of 15 cases because it deviated from the national TB treatment guidelines. A survey indicated that the treatment recommender CDSS is easy to use and useful in clinical practice but requires digital infrastructure support and training. Conclusions Our findings suggest that global implementation of the novel treatment recommender CDSS holds the potential to improve treatment outcomes of rifampicin resistant tuberculosis.

BackgroundIndividualised or precision medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians.MethodsWe developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. ResultsWe applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. ConclusionOur novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.

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