Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients

Aminoglycosides such as amikacin continue to be part of the backbone of treatment of multidrug-resistant tuberculosis (MDR- TB). We measured amikacin concentrations in 28 MDR-TB patients in Botswana receiving amikacin therapy together with oral levofloxacin, ethionamide, cycloserine, and pyrazinamide and calculated areas under the concentration-time curves from 0 to 24 h (AUC0 –24). The patients were followed monthly for sputum culture conversion based on liquid cultures. The median duration of amikacin therapy was 184 (range, 28 to 866) days, at a median dose of 17.30 (range 11.11 to 19.23) mg/kg. Only 11 (39%) pa- tients had sputum culture conversion during treatment; the rest failed. We utilized classification and regression tree analyses (CART) to examine all potential predictors of failure, including clinical and demographic features, comorbidities, and amikacin peak concentrations (Cmax), AUC0 –24, and trough concentrations. The primary node for failure had two competing variables, Cmax of \u3c67 mg/liter and AUC0 –24 of \u3c568.30 mg · h/L; weight of \u3e41 kg was a secondary node with a score of 35% relative to the primary node. The area under the receiver operating characteristic curve for the CART model was an R2 �� 0.90 on posttest. In patients weighing \u3e41 kg, sputum conversion was 3/3 (100%) in those with an amikacin Cmax of \u3e67 mg/liter versus 3/15 (20%) in those with a Cmax of \u3c67 mg/liter (relative risk [RR] �� 5.00; 95% confidence interval [CI], 1.82 to 13.76). In all patients who had both amikacin Cmax and AUC0 –24 below the threshold, 7/7 (100%) failed, compared to 7/15 (47%) of those who had these parameters above threshold (RR �� 2.14; 95% CI, 1.25 to 43.68). These amikacin dose-schedule patterns and exposures are virtually the same as those identified in the hollow-fiber system model

Tuberculous Pericarditis is Multibacillary and Bacterial Burden Drives High Mortality

AbstractBackgroundTuberculous pericarditis is considered to be a paucibacillary process; the large pericardial fluid accumulation is attributed to an inflammatory response to tuberculoproteins. Mortality rates are high. We investigated the role of clinical and microbial factors predictive of tuberculous pericarditis mortality using the artificial intelligence algorithm termed classification and regression tree (CART) analysis.MethodsPatients were prospectively enrolled and followed in the Investigation of the Management of Pericarditis (IMPI) registry. Clinical and laboratory data of 70 patients with confirmed tuberculous pericarditis, including time-to-positive (TTP) cultures from pericardial fluid, were extracted and analyzed for mortality outcomes using CART. TTP was translated to log10 colony forming units (CFUs) per mL, and compared to that obtained from sputum in some of our patients.FindingsSeventy patients with proven tuberculous pericarditis were enrolled. The median patient age was 35 (range: 20–71) years. The median, follow up was for 11.97 (range: 0·03–74.73) months. The median TTP for pericardial fluid cultures was 22 (range: 4–58) days or 3.91(range: 0·5–8·96) log10CFU/mL, which overlapped with the range of 3.24–7.42 log10CFU/mL encountered in sputum, a multi-bacillary disease. The overall mortality rate was 1.43 per 100 person-months. CART identified follow-up duration of 5·23months on directly observed therapy, a CD4+ count of ≤199.5/mL, and TTP≤14days (bacillary load≥5.53 log10 CFU/mL) as predictive of mortality. TTP interacted with follow-up duration in a non-linear fashion.InterpretationPatients with culture confirmed tuberculous pericarditis have a high bacillary burden, and this bacterial burden drives mortality. Thus proven tuberculosis pericarditis is not a paucibacillary disease. Moreover, the severe immunosuppression suggests limited inflammation. There is a need for the design of a highly bactericidal regimen for this condition

Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis

BACKGROUND: There is a general dearth of information on extrapulmonary tuberculosis (EPTB). Here, we investigated Mycobacterium tuberculosis (Mtb) drug resistance and transmission patterns in EPTB patients treated in the Tshwane metropolitan area, in South Africa. METHODS: Consecutive Mtb culture-positive non-pulmonary samples from unique EPTB patients underwent mycobacterial genotyping and were assigned to phylogenetic lineages and transmission clusters based on spoligotypes. MTBDRplus assay was used to search mutations for isoniazid and rifampin resistance. Machine learning algorithms were used to identify clinically meaningful patterns in data. We computed odds ratio (OR), attributable risk (AR) and corresponding 95% confidence intervals (CI). RESULTS: Of the 70 isolates examined, the largest cluster comprised 25 (36%) Mtb strains that belonged to the East Asian lineage. East Asian lineage was significantly more likely to occur within chains of transmission when compared to the Euro-American and East-African Indian lineages: OR = 10.11 (95% CI: 1.56–116). Lymphadenitis, meningitis and cutaneous TB, were significantly more likely to be associated with drug resistance: OR = 12.69 (95% CI: 1.82–141.60) and AR = 0.25 (95% CI: 0.06–0.43) when compared with other EPTB sites, which suggests that poor rifampin penetration might be a contributing factor. CONCLUSIONS: The majority of Mtb strains circulating in the Tshwane metropolis belongs to East Asian, EuroAmerican and East-African Indian lineages. Each of these are likely to be clustered, suggesting on-going EPTB transmission. Since 25% of the drug resistance was attributable to sanctuary EPTB sites notorious for poor rifampin penetration, we hypothesize that poor anti-tuberculosis drug dosing might have a role in the development of resistance.http://www.biomedcentral.com/bmcinfectdispm2020Medical Microbiolog

Phylogenomic and epidemiological insights into two clinical Mycobacterium bovis BCG strains circulating in South Africa

BACKGROUND : Mycobacterium bovis BCG is a live, attenuated tuberculosis vaccine. While the vaccine protects infants from tuberculosis, complications including disseminated infections have been reported following vaccination. Genetically diverse BCG sub-strains now exist following continuous passaging of the original Pasteur strain for vaccine manufacture. This genetic diversity reportedly influences the severity of disseminated BCG infections and the efficacy of BCG immunization. METHODS : M. bovis BCG was isolated from infants suspected of being infected with tuberculosis. The whole genome of the clinical isolates and BCG Moscow were sequenced using Illumina Miseq and the sequences were analysed using CLC Genomics Workbench 7.0, PhyResSE v1.0, and Parsnp. RESULTS AND CONCLUSIONS : Genetic variations between the clinical strains and the reference BCG Copenhagen were identified. The clinical strains shared only one mutation in a secretion protein. Mutations were identified in various antibiotic resistance genes in the BCG isolates, which suggests their potential as multidrug-resistant (MDR) phenotypes. Phylogenetic analysis showed that the two isolates were distantly related, and the M1_S48 clinical isolate was closely related to M. bovis BCG Moscow. The phylogenomics results imply that two different BCG strains may be circulating in South Africa. However, it is difficult to associate the BCG vaccine strain administered and the BCG strain supplied with specific adverse events, as BCGiosis is under-reported. This study presents background genomic information for future surveillance and tracking of the distribution of BCGiosis-associated mycobacteria. It is also the first to report on the genomes of clinical BCG strains in Africa.Supplementary material: Table S1We would like to acknowledge the Department of Microbiol- ogy, National Health Laboratory Services/Tshwane Academic Division for providing the BCG isolates and funding the study, the Department of Medical Microbiology, University of Pretoria for providing facilities to conduct the study, and the National Institute of Communicable Diseases for sequencing the BCG isolates. We would also like to offer our gratitude to Mrs O.O. Onwegbuna for her assistance with sample collection and her laboratory expertise.The Department of Microbiology, National Health Laboratory Services/Tshwane Academic Division.http://www.elsevier.com/locate/ijidam2020BiochemistryGeneticsMedical MicrobiologyMicrobiology and Plant Patholog