Clinical sub-phenotypes of Staphylococcus aureus bacteraemia.

BACKGROUND: Staphylococcus aureus bacteraemia (SAB) is a clinically heterogeneous disease. The ability to identify sub-groups of patients with shared traits (sub-phenotypes) is an unmet need that could allow patient stratification for clinical management and research. We aimed to test the hypothesis that clinically-relevant sub-phenotypes can be reproducibly identified amongst patients with SAB. METHODS: We studied three cohorts of hospitalised adults with monomicrobial SAB: a UK retrospective observational study (Edinburgh cohort, n=458), the UK ARREST randomised trial (n=758), and the Spanish SAFO randomised trial (n=214). Latent class analysis was used to identify sub-phenotypes using routinely-collected clinical data, without considering outcomes. Mortality and microbiologic outcomes were then compared between sub-phenotypes. RESULTS: Included patients had predominantly methicillin-susceptible SAB (1366/1430,95.5%). We identified five distinct, reproducible clinical sub-phenotypes: (A) SAB associated with older age and comorbidity, (B) nosocomial intravenous catheter-associated SAB in younger people without comorbidity, (C) community-acquired metastatic SAB, (D) SAB associated with chronic kidney disease, and (E) SAB associated with injection drug use. Survival and microbiologic outcomes differed between the sub-phenotypes. 84-day mortality was highest in sub-phenotype A, and lowest in B and E. Microbiologic outcomes were worse in sub-phenotype C. In a secondary analysis of the ARREST trial, adjunctive rifampicin was associated with increased 84-day mortality in sub-phenotype B and improved microbiologic outcomes in sub-phenotype C. CONCLUSIONS: We have identified reproducible and clinically-relevant sub-phenotypes within SAB, and provide proof-of-principle of differential treatment effects. Through clinical trial enrichment and patient stratification, these sub-phenotypes could contribute to a personalised medicine approach to SAB.

Spatial Analysis of Drug-Susceptible and Multidrug-Resistant Cases of Tuberculosis, Ho Chi Minh City, Vietnam, 2020-2023.

We characterized the spatial distribution of drug-susceptible (DS) and multidrug-resistant (MDR) tuberculosis (TB) cases in Ho Chi Minh City, Vietnam, a major metropolis in southeastern Asia, and explored demographic and socioeconomic factors associated with local TB burden. Hot spots of DS and MDR TB incidence were observed in the central parts of Ho Chi Minh City, and substantial heterogeneity was observed across wards. Positive spatial autocorrelation was observed for both DS TB and MDR TB. Ward-level TB incidence was associated with HIV prevalence and the male proportion of the population. No ward-level demographic and socioeconomic indicators were associated with MDR TB case count relative to total TB case count. Our findings might inform spatially targeted TB control strategies and provide insights for generating hypotheses about the nature of the relationship between DS and MDR TB in Ho Chi Minh City and the wider southeastern region of Asia.

Targeted sequencing from cerebrospinal fluid for rapid identification of drug-resistant tuberculous meningitis.

Mortality from tuberculous meningitis (TBM) remains around 30%, with most deaths occurring within 2 months of starting treatment. Mortality from drug-resistant strains is higher still, making early detection of drug resistance (DR) essential. Targeted next-generation sequencing (tNGS) produces high read depths, allowing the detection of DR-associated alleles with low frequencies. We applied Deeplex Myc-TB-a tNGS assay-to cerebrospinal fluid (CSF) samples from 72 adults with microbiologically confirmed TBM and compared its genomic drug susceptibility predictions to a composite reference standard of phenotypic susceptibility testing (pDST) and whole genome sequencing, as well as to clinical outcomes. Deeplex detected Mycobacterium tuberculosis complex DNA in 24/72 (33.3%) CSF samples and generated full DR reports for 22/24 (91.7%). The read depth generated by Deeplex correlated with semi-quantitative results from MTB/RIF Xpert. Alleles with <20% frequency were seen at canonical loci associated with first-line DR. Disregarding these low-frequency alleles, Deeplex had 100% concordance with the composite reference standard for all drugs except pyrazinamide and streptomycin. Three patients had positive CSF cultures after 30 days of treatment; reference tests and Deeplex identified isoniazid resistance in two, and Deeplex alone identified low-frequency rifampin resistance alleles in one. Five patients died, of whom one had pDST-identified pyrazinamide resistance. tNGS on CSF can rapidly and accurately detect drug-resistant TBM, but its application is limited to those with higher bacterial loads. In those with lower bacterial burdens, alternative approaches need to be developed for both diagnosis and resistance detection.

A novel diagnostic model for tuberculous meningitis using Bayesian latent class analysis.

BACKGROUND: Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known. METHODS: We included 659 individuals aged [Formula: see text] years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl-Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally. RESULTS: Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden's Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively. CONCLUSION: Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research.

A Season at the Himalayan Rescue Association Aid Post in Manang.

The Himalayan Rescue Association (HRA) has operated high altitude clinics in Nepal for 50 years, with rising visitor numbers, especially from India, China, and Nepal. New roads have eased access and increased the speed of ascent in some areas. Our aim was to provide a description of the activities, clinical problems, and lecture attendees of the HRA aid post in Manang over one season. We also highlight the evolving challenges of providing healthcare and education in the high Himalayan region. We describe the clinical and educational activities of the HRA aid post in Manang from September 24 to December 1, 2023. Prospective clinical data collection included anonymized patient demographics and diagnoses. Lecture data were taken from the attendee register and by daily manual counts of lecture attendees. We saw 376 patients, 62% of whom were Nepalis. Infectious diseases (42%) and altitude illness (16%) were the most common problems. A total of 846 people from 47 countries attended the daily altitude lectures. Only 5% of attendees were Nepali. Electrical supply interruptions and limitations in medical evacuation options were among the challenges of providing care at a high altitude clinic and preventing altitude illness using educational lectures. Altitude illness remains a common and potentially life-threatening problem, with risks increased by rapid ascent enabled by new road access and by ignorance of risks of altitude among travelers, especially Nepalis. Language barriers in educational outreach call for novel approaches and interventions that will ensure the effectiveness of altitude education.

The impact of antibiotics on the gut microbiota of children recovering from watery diarrhoea.

Infectious diarrhoeal diseases remain a substantial health burden in young children in low- and middle-income countries. The disease and its variable treatment options significantly alter the gut microbiome, which may affect clinical outcomes and overall gut health. Antibiotics are often prescribed, but their impact on the gut microbiome during recovery is unclear. Here, we used 16S rRNA sequencing to investigate changes in the gut microbiota in Vietnamese children with acute watery diarrhoea, and highlight the impact of antibiotic treatment on these changes. Our analyses identified that, regardless of treatment, recovery was characterised by reductions in Streptococcus and Rothia species and expansion of Bacteroides/Phocaeicola, Lachnospiraceae and Ruminococcacae taxa. Antibiotic treatment significantly delayed the temporal increases in alpha- and beta-diversity within patients, resulting in distinctive patterns of taxonomic change. These changes included a pronounced, transient overabundance of Enterococcus species and depletion of Bifidobacterium pseudocatenulatum. Our findings demonstrate that antibiotic treatment slows gut microbiota recovery in children following watery diarrhoea.

Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data.

Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other outcomes, especially when impacted by co-morbidities such as HIV infection. Brain magnetic resonance imaging (MRI) characterises the extent and severity of disease and may enable more accurate prediction of complications and poor outcomes. We analysed clinical and brain MRI data from a prospective longitudinal study of 216 adults with TBM; 73 (34%) were HIV-positive, a factor highly correlated with mortality. We implemented an end-to-end framework to model clinical and imaging features to predict disease progression. Our model used state-of-the-art machine learning models for automatic imaging feature encoding, and time-series models for forecasting, to predict TBM progression. The proposed approach is designed to be robust to missing data via a novel tailored model optimisation framework. Our model achieved a 60% balanced accuracy in predicting the prognosis of TBM patients over the six different classes. HIV status did not alter the performance of the models. Furthermore, our approach identified brain morphological lesions caused by TBM in both HIV and non-HIV-infected, associating lesions to the disease staging with an overall accuracy of 96%. These results suggest that the lesions caused by TBM are analogous in both populations, regardless of the severity of the disease. Lastly, our models correctly identified changes in disease symptomatology and severity in 80% of the cases. Our approach is the first attempt at predicting the prognosis of TBM by combining imaging and clinical data, via a machine learning model. The approach has the potential to accurately predict disease progression and enable timely clinical intervention.

Rifampicin resistant Mycobacterium tuberculosis in Vietnam, 2020-2022.

Objective: We conducted a descriptive analysis of multi-drug resistant tuberculosis (MDR-TB) in Vietnam's two largest cities, Hanoi and Ho Chi Minh city. Methods: All patients with rifampicin resistant tuberculosis were recruited from Hanoi and surrounding provinces between 2020 and 2022. Additional patients were recruited from Ho Chi Minh city over the same time period. Demographic data were recorded from all patients, and samples collected, cultured, whole genome sequenced and analysed for drug resistance mutations. Genomic susceptibility predictions were made on the basis of the World Health Organization's catalogue of mutations in Mycobacterium tuberculosis associated with drug resistance, version 2. Comparisons were made against phenotypic drug susceptibility test results where these were available. Multivariable logistic regression was used to assess risk factors for previous episodes of tuberculosis. Results: 233/265 sequenced isolates were of sufficient quality for analysis, 146 (63 %) from Ho Chi Minh City and 87 (37 %) from Hanoi. 198 (85 %) were lineage 2, 20 (9 %) were lineage 4, and 15 (6 %) were lineage 1. 17/211 (8 %) for whom HIV status was known were infected, and 109/214 (51 %) patients had had a previous episode of tuberculosis. The main risk factor for a previous episode was HIV infection (odds ratio 5.1 (95 % confidence interval 1.3-20.0); p = 0.021). Sensitivity for predicting first-line drug resistance from whole genome sequencing data was over 90 %, with the exception of pyrazinamide (85 %). For moxifloxacin and amikacin it was 50 % or less. Among rifampicin-resistant isolates, prevalence of resistance to each non-first-line drug was < 20 %. Conclusions: Drug resistance among most MDR-TB strains in Vietnam's two largest cities is confined largely to first-line drugs. Living with HIV is the main risk factor among patients with MDR-TB for having had a previous episode of tuberculosis.

Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis.

No accurate and rapid diagnostic test exists for tuberculous meningitis (TBM), leading to delayed diagnosis. We leveraged data from multiple studies to improve the predictive performance of diagnostic models across different populations, settings, and subgroups to develop a new predictive tool for TBM diagnosis. We conducted a systematic review to analyze eligible datasets with individual-level participant data (IPD). We imputed missing data and explored three approaches: stepwise logistic regression, classification and regression tree (CART), and random forest regression. We evaluated performance using calibration plots and C-statistics via internal-external cross-validation. We included 3,761 individual participants from 14 studies and nine countries. A total of 1,240 (33%) participants had "definite" (30%) or "probable" (3%) TBM by case definition. Important predictive variables included cerebrospinal fluid (CSF) glucose, blood glucose, CSF white cell count, CSF differential, cryptococcal antigen, HIV status, and fever presence. Internal validation showed that performance varied considerably between IPD datasets with C-statistic values between 0.60 and 0.89. In external validation, CART performed the worst (C = 0.82), and logistic regression and random forest had the same accuracy (C = 0.91). We developed a mobile app for TBM clinical prediction that accounted for heterogeneity and improved diagnostic performance (https://tbmcalc.github.io/tbmcalc). Further external validation is needed.

The effect of M. tuberculosis lineage on clinical phenotype.

Six lineages of Mycobacterium tuberculosis sensu stricto (which excludes M. africanum) are described. Single-country or small observational data suggest differences in clinical phenotype between lineages. We present strain lineage and clinical phenotype data from 12,246 patients from 3 low-incidence and 5 high-incidence countries. We used multivariable logistic regression to explore the effect of lineage on site of disease and on cavities on chest radiography, given pulmonary TB; multivariable multinomial logistic regression to investigate types of extra-pulmonary TB, given lineage; and accelerated failure time and Cox proportional-hazards models to explore the effect of lineage on time to smear and culture-conversion. Mediation analyses quantified the direct effects of lineage on outcomes. Pulmonary disease was more likely among patients with lineage(L) 2, L3 or L4, than L1 (adjusted odds ratio (aOR) 1.79, (95% confidence interval 1.49-2.15), p<0.001; aOR = 1.40(1.09-1.79), p = 0.007; aOR = 2.04(1.65-2.53), p<0.001, respectively). Among patients with pulmonary TB, those with L1 had greater risk of cavities on chest radiography versus those with L2 (aOR = 0.69(0.57-0.83), p<0.001) and L4 strains (aOR = 0.73(0.59-0.90), p = 0.002). L1 strains were more likely to cause osteomyelitis among patients with extra-pulmonary TB, versus L2-4 (p = 0.033, p = 0.008 and p = 0.049 respectively). Patients with L1 strains showed shorter time-to-sputum smear conversion than for L2. Causal mediation analysis showed the effect of lineage in each case was largely direct. The pattern of clinical phenotypes seen with L1 strains differed from modern lineages (L2-4). This has implications for clinical management and could influence clinical trial selection strategies.