Two-year analysis of Clostridium difficile ribotypes associated with increased severity.

BACKGROUND: Certain Clostridium difficile ribotypes have been associated with complex disease phenotypes including recurrence and increased severity, especially the well-described hypervirulent RT027. This study aimed to determine the pattern of ribotypes causing infection and the association, if any, with severity. METHODS: All faecal samples submitted to a large diagnostic laboratory for C. difficile testing between 2011 and 2013 were subject to routine testing and culture. All C. difficile isolates were ribotyped, and associated clinical and demographic patient data were retrieved and linked to ribotyping data. RESULTS: In total, 86 distinct ribotypes were identified from 705 isolates of C. difficile. RT002 and RT015 were the most prevalent (22.5%, N=159). Only five isolates (0.7%) were hypervirulent RT027. Ninety of 450 (20%) patients with clinical information available died within 30 days of C. difficile isolation. RT220, one of the 10 most common ribotypes, was associated with elevated median C-reactive protein and significantly increased 30-day all-cause mortality compared with RT002 and RT015, and with all other ribotypes found in the study. CONCLUSIONS: A wide range of C. difficile ribotypes were responsible for C. difficile infection presentations. Although C. difficile-associated mortality has reduced in recent years, expansion of lineages associated with increased severity could herald increases in future mortality. Enhanced surveillance for emerging lineages such as RT220 that are associated with more severe disease is required, with genomic approaches to dissect pathogenicity.

Factors that impact on the burden of Escherichia coli bacteraemia: multivariable regression analysis of 2011-2015 data from West London.

BACKGROUND: The incidence of Escherichia coli bacteraemia in England is increasing amid concern regarding the roles of antimicrobial resistance and nosocomial acquisition on burden of disease. AIM: To determine the relative contributions of hospital-onset E. coli bloodstream infection and specific E. coli antimicrobial resistance patterns to the burden and severity of E. coli bacteraemia in West London. METHODS: Patient and antimicrobial susceptibility data were collected for all cases of E. coli bacteraemia between 2011 and 2015. Multivariable logistic regression was used to determine the association between the category of infection (hospital or community-onset) and length of stay, intensive care unit admission, and 30-day all-cause mortality. FINDINGS: E. coli bacteraemia incidence increased by 76% during the study period, predominantly due to community-onset cases. Resistance to quinolones, third-generation cephalosporins, and aminoglycosides also increased over the study period, occurring in both community- and hospital-onset cases. Hospital-onset and non-susceptibility to either quinolones or third-generation cephalosporins were significant risk factors for prolonged length of stay, as was older age. Rates of mortality were 7% and 12% at 7 and 30 days, respectively. Older age, a higher comorbidity score, and bacteraemia caused by strains resistant to three antibiotic classes were all significant risk factors for mortality at 30 days. CONCLUSION: Multidrug resistance, increased age, and comorbidities were the main drivers of adverse outcome. The rise in E. coli bacteraemia was predominantly driven by community-onset infections, and initiatives to prevent community-onset cases should be a major focus to reduce the quantitative burden of E. coli infection.

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.

BACKGROUND: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital. METHODS: An SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored. RESULTS: One hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21-98) years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20-0.40). ROC AUC was 0.84 (95% CI: 0.76-0.91). CONCLUSIONS: An SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.