Development and validation of multiplex real-time PCR for simultaneous detection of six bacterial pathogens causing lower respiratory tract infections and antimicrobial resistance genes.

BACKGROUND: Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Escherichia coli, Streptococcus pneumoniae and Staphylococcus aureus are major bacterial causes of lower respiratory tract infections (LRTIs) globally, leading to substantial morbidity and mortality. The rapid increase of antimicrobial resistance (AMR) in these pathogens poses significant challenges for their effective antibiotic therapy. In low-resourced settings, patients with LRTIs are prescribed antibiotics empirically while awaiting several days for culture results. Rapid pathogen and AMR gene detection could prompt optimal antibiotic use and improve outcomes. METHODS: Here, we developed multiplex quantitative real-time PCR using EvaGreen dye and melting curve analysis to rapidly identify six major pathogens and fourteen AMR genes directly from respiratory samples. The reproducibility, linearity, limit of detection (LOD) of real-time PCR assays for pathogen detection were evaluated using DNA control mixes and spiked tracheal aspirate. The performance of RT-PCR assays was subsequently compared with the gold standard, conventional culture on 50 tracheal aspirate and sputum specimens of ICU patients. RESULTS: The sensitivity of RT-PCR assays was 100% for K. pneumoniae, A. baumannii, P. aeruginosa, E. coli and 63.6% for S. aureus and the specificity ranged from 87.5% to 97.6%. The kappa correlation values of all pathogens between the two methods varied from 0.63 to 0.95. The limit of detection of target bacteria was 1600 CFU/ml. The quantitative results from the PCR assays demonstrated 100% concordance with quantitative culture of tracheal aspirates. Compared to culture, PCR assays exhibited higher sensitivity in detecting mixed infections and S. pneumoniae. There was a high level of concordance between the detection of AMR gene and AMR phenotype in single infections. CONCLUSIONS: Our multiplex quantitative RT-PCR assays are fast and simple, but sensitive and specific in detecting six bacterial pathogens of LRTIs and their antimicrobial resistance genes and should be further evaluated for clinical utility.

Mitigating machine learning bias between high income and low-middle income countries for enhanced model fairness and generalizability.

Collaborative efforts in artificial intelligence (AI) are increasingly common between high-income countries (HICs) and low- to middle-income countries (LMICs). Given the resource limitations often encountered by LMICs, collaboration becomes crucial for pooling resources, expertise, and knowledge. Despite the apparent advantages, ensuring the fairness and equity of these collaborative models is essential, especially considering the distinct differences between LMIC and HIC hospitals. In this study, we show that collaborative AI approaches can lead to divergent performance outcomes across HIC and LMIC settings, particularly in the presence of data imbalances. Through a real-world COVID-19 screening case study, we demonstrate that implementing algorithmic-level bias mitigation methods significantly improves outcome fairness between HIC and LMIC sites while maintaining high diagnostic sensitivity. We compare our results against previous benchmarks, utilizing datasets from four independent United Kingdom Hospitals and one Vietnamese hospital, representing HIC and LMIC settings, respectively.

Endothelial and inflammatory pathophysiology in dengue shock: New insights from a prospective cohort study in Vietnam.

Dengue shock (DS) is the most severe complication of dengue infection; endothelial hyperpermeability leads to profound plasma leakage, hypovolaemia and extravascular fluid accumulation. At present, the only treatment is supportive with intravenous fluid, but targeted endothelial stabilising therapies and host immune modulators are needed. With the aim of prioritising potential therapeutics, we conducted a prospective observational study of adults (≥16 years) with DS in Vietnam from 2019-2022, comparing the pathophysiology underlying circulatory failure with patients with septic shock (SS), and investigating the association of biomarkers with clinical severity (SOFA score, ICU admission, mortality) and pulmonary vascular leak (daily lung ultrasound for interstitial and pleural fluid). Plasma was collected at enrolment, 48 hours later and hospital discharge. We measured biomarkers of inflammation (IL-6, ferritin), endothelial activation (Ang-1, Ang-2, sTie-2, VCAM-1) and endothelial glycocalyx breakdown (hyaluronan, heparan sulfate, endocan, syndecan-1). We enrolled 135 patients with DS (median age 26, median SOFA score 7, 34 required ICU admission, 5 deaths), together with 37 patients with SS and 25 healthy controls. Within the DS group, IL-6 and ferritin were associated with admission SOFA score (IL-6: ßeta0.70, p<0.001 & ferritin: ßeta0.45, p<0.001), ICU admission (IL-6: OR 2.6, p<0.001 & ferritin: OR 1.55, p<0.001) and mortality (IL-6: OR 4.49, p = 0.005 & ferritin: OR 13.8, p = 0.02); both biomarkers discriminated survivors and non-survivors at 48 hours and all patients who died from DS had pre-mortem ferritin ≥100,000ng/ml. IL-6 most strongly correlated with severity of pulmonary vascular leakage (R = 0.41, p<0.001). Ang-2 correlated with pulmonary vascular leak (R = 0.33, p<0.001) and associated with SOFA score (ß 0.81, p<0.001) and mortality (OR 8.06, p = 0.002). Ang-1 was associated with ICU admission (OR 1.6, p = 0.005) and mortality (OR 3.62, p = 0.006). All 4 glycocalyx biomarkers were positively associated with SOFA score, but only syndecan-1 was associated with ICU admission (OR 2.02, p<0.001) and mortality (OR 6.51, p<0.001). This study highlights the central role of hyperinflammation in determining outcomes from DS; the data suggest that anti-IL-1 and anti-IL-6 immune modulators and Tie2 agonists may be considered as candidates for therapeutic trials in severe dengue.

Understanding maternal sepsis risk factors and bacterial etiology: A case control study protocol.

INTRODUCTION: Maternal disorders are the third leading cause of sepsis globally, accounting for 5.7 million (12%) cases in 2017. There are increasing concerns about the emergence of antimicrobial resistance (AMR) in bacteria commonly causing maternal sepsis. Our aim is to describe the protocol for a clinical and microbiology laboratory study to understand risk factors for and the bacterial etiology of maternal sepsis in a tertiary Obstetrics and Gynaecology Hospital. METHODS: This case-control study aims to recruit 100 cases and 200 controls at Tu Du Hospital in Ho Chi Minh City, Vietnam, which had approximately 55,000 births in 2022. Women aged ≥ 18 years and ≥ 28 weeks gestation having a singleton birth will be eligible for inclusion as cases or controls, unless they have an uncomplicated localised or chronic infection, or an infection with SARS-CoV-2. Cases will include pregnant or recently pregnant women with sepsis recognised between the onset of labour and/or time of delivery/cessation of pregnancy for up to 42 days post-partum. Sepsis will be defined as suspected or confirmed infection with an obstetrically modified Sequential Organ Failure Assessment score of ≥ 2, treatment with intravenous antimicrobials and requested cultures of any bodily fluid. Controls will be matched by age, location, parity, mode of delivery and gestational age. Primary and secondary outcomes are risk factors associated with the development of maternal sepsis, the frequency of adverse outcomes due to maternal sepsis, bacterial etiology and AMR profiles of cases and controls. DISCUSSION: This study will improve understanding of the epidemiology and clinical implications of maternal sepsis management including the presence of AMR in women giving birth in Vietnam. It will help us to determine whether women in this setting are receiving optimal care and to identify opportunities for improvement.

Clinical evaluation of AI-assisted muscle ultrasound for monitoring muscle wasting in ICU patients.

Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively (p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998-0.999 vs. 0.982 95% CI 0.962-0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9-21.7) to 9.4 min (IQR 7.2-11.7) compared to when using the AI tool (p < 0.001). AI-assisted muscle ultrasound removes the need for manual tracing, increases reproducibility and saves time. This system may aid monitoring muscle size in ICU patients assisting rehabilitation programmes.

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.

BACKGROUND: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. METHODS: We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. FINDINGS: We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. INTERPRETATION: We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. FUNDING: EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).