Surveillance of vancomycin-resistant enterococci reveals shift in dominating clusters from vanA to vanB Enterococcus faecium clusters, Denmark, 2015 to 2022.

BackgroundVancomycin-resistant enterococci (VRE) are increasing in Denmark and Europe. Linezolid and vancomycin-resistant enterococci (LVRE) are of concern, as treatment options are limited. Vancomycin-variable enterococci (VVE) harbour the vanA gene complex but are phenotypically vancomycin-susceptible.AimThe aim was to describe clonal shifts for VRE and VVE in Denmark between 2015 and 2022 and to investigate genotypic linezolid resistance among the VRE and VVE.MethodsFrom 2015 to 2022, 4,090 Danish clinical VRE and VVE isolates were whole genome sequenced. We extracted vancomycin resistance genes and sequence types (STs) from the sequencing data and performed core genome multilocus sequence typing (cgMLST) analysis for Enterococcus faecium. All isolates were tested for the presence of mutations or genes encoding linezolid resistance.ResultsIn total 99% of the VRE and VVE isolates were E. faecium. From 2015 through 2019, 91.1% of the VRE and VVE were vanA E. faecium. During 2020, to the number of vanB E. faecium increased to 254 of 509 VRE and VVE isolates. Between 2015 and 2022, seven E. faecium clusters dominated: ST80-CT14 vanA, ST117-CT24 vanA, ST203-CT859 vanA, ST1421-CT1134 vanA (VVE cluster), ST80-CT1064 vanA/vanB, ST117-CT36 vanB and ST80-CT2406 vanB. We detected 35 linezolid vancomycin-resistant E. faecium and eight linezolid-resistant VVEfm.ConclusionFrom 2015 to 2022, the numbers of VRE and VVE increased. The spread of the VVE cluster ST1421-CT1134 vanA E. faecium in Denmark is a concern, especially since VVE diagnostics are challenging. The finding of LVRE, although in small numbers, ia also a concern, as treatment options are limited.

A study on the risk stratification for patients within 24 hours of admission for risk of hospital-acquired urinary tract infection using Bayesian network models.

Early identification of patients at risk of hospital-acquired urinary tract infections (HA-UTI) enables the initiation of timely targeted preventive and therapeutic strategies. Machine learning (ML) models have shown great potential for this purpose. However, existing ML models in infection control have demonstrated poor ability to support explainability, which challenges the interpretation of the result in clinical practice, limiting the adaption of the ML models into a daily clinical routine. In this study, we developed Bayesian Network (BN) models to enable explainable assessment within 24 h of admission for risk of HA-UTI. Our dataset contained 138,250 unique hospital admissions. We included data on admission details, demographics, lifestyle factors, comorbidities, vital parameters, laboratory results, and urinary catheter. Models developed from a reduced set of five features were characterized by transparency compared to models developed from a full set of 50 features. The expert-based clinical BN model over the reduced feature space showed the highest performance (area under the curve = 0.746) compared to the naïve- and tree-augmented-naïve BN models. Moreover, models developed from expert-based knowledge were characterized by enhanced explainability.

Comparison of morbidity and mortality after bloodstream infection with vancomycin-resistant versus -susceptible Enterococcus faecium: a nationwide cohort study in Denmark, 2010-2019.

The emergence of bloodstream infections (BSI) caused by vancomycin-resistant Enterococci (VRE) has caused concern. Nonetheless, it remains unclear whether these types are associated with an excess risk of severe outcomes when compared with infections caused by vancomycin-susceptible Enterococci (VSE). This cohort study included hospitalized patients in Denmark with Enterococcus faecium-positive blood cultures collected between 2010 and 2019 identified in the Danish Microbiology Database. We estimated 30-day hazard ratio (HR) of death or discharge among VRE compared to VSE patients adjusted for age, sex, and comorbidity. The cohort included 6071 patients with E. faecium BSI (335 VRE, 5736 VSE) among whom VRE increased (2010-13, 2.6%; 2014-16, 6.3%; 2017-19; 9.4%). Mortality (HR 1.08, 95%CI 0.90-1.29; 126 VRE, 37.6%; 2223 VSE, 37.0%) or discharge (HR 0.89, 95%CI 0.75-1.06; 126 VRE, 37.6%; 2386 VSE, 41.6%) was not different between VRE and VSE except in 2014 (HR 1.87, 95% CI 1.18-2.96). There was no interaction between time from admission to BSI (1-2, 3-14, and >14 days) and HR of death (P = 0.14) or discharge (P = 0.45) after VRE compared to VSE, despite longer time for VRE patients (17 vs. 10 days for VSE, P < 0.0001). In conclusion, VRE BSI was not associated with excess morbidity and mortality. The excess mortality in 2014 only may be attributed to improved diagnostic- and patient-management practices after 2014, reducing time to appropriate antibiotic therapy. The high level of mortality after E. faecium BSI warrants further study.

Clinical explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection.

BACKGROUND: Machine learning (ML) models for early identification of patients at risk of hospital-acquired urinary tract infections (HA-UTI) may enable timely and targeted preventive and therapeutic strategies. However, clinicians are often challenged in the interpretation of the predictive outcomes provided by the ML models, which often reach different performances. AIM: To train ML models for predicting patients at risk of HA-UTI using available data from electronic health records at the time of hospital admission. We focused on the performance of different ML models and clinical explainability. METHODS: This retrospective study investigated patient data representing 138.560 hospital admissions in the North Denmark Region from 01.01.2017 to 31.12.2018. We extracted 51 health socio-demographic and clinical features in a full dataset and used the χ2 test in addition to expert knowledge for feature selection, resulting in two reduced datasets. Seven different ML models were trained and compared between the three datasets. We applied the SHapley Additive exPlanation (SHAP) method to support population- and patient-level explainability. FINDINGS: The best-performing ML model was a neural network based on the full dataset, reaching an area under the curve (AUC) of 0.758. The neural network was also the best-performing ML model based on the reduced datasets, reaching an AUC of 0.746. Clinical explainability was demonstrated with a SHAP summary- and forceplot. CONCLUSION: Within 24h of hospital admission, the ML models were able to identify patients at risk of developing HA-UTI, providing new opportunities to develop efficient strategies for the prevention of HA-UTI. Using SHAP, we demonstrate how risk predictions can be explained at individual patient level and for the patient population in general.

Work exposure and associated risk of hospitalisation with pneumonia and influenza: A nationwide study.

BACKGROUND: Pneumonia and influenza are major health concerns and constitute a high economic burden. However, few data are available on the associated risk of pneumonia and influenza and work exposure on a large population scale. AIM: This study aimed to examine the associated risk of pneumonia and influenza by type of work exposure. METHODS: By cross-linking administrative Danish registries, we classified people in 10 different profession types. The main outcome was hospitalisation with pneumonia or influenza. A multivariable Poisson regression analysis was used to assess the associated incidence rate ratio (IRR) of being hospitalised with pneumonia or influenza by type of profession. RESULTS: A total of 1,327,606 people added risk time to the analyses. In a multivariable model, work in day care, public transportation, sewers and nursing home care was associated with an increased risk of hospitalisation with pneumonia compared to work within public administration: IRR=1.20 (95% confidence interval (CI) 1.12-1.28), IRR=1.21 (95% CI 1.09-1.34), IRR=1.61 (95% CI 1.19-2.19) and IRR=1.10 (95% CI 1.03-1.18), respectively. In a multivariable analysis, people working within public transportation were associated with an increased risk of hospitalisation with influenza compared to people working within public administration: IRR=2.54 (95% CI 1.79-3.58). CONCLUSIONS: Working in day care, public transportation, sewers and nursing home care increased the associated risk of hospitalisation with pneumonia, and working within public transportation increased the associated risk of being hospitalised with influenza compared to working within public administration.

Socioeconomic inequalities in risk of hospitalization for community-acquired bacteremia: a Danish population-based case-control study.

In a Danish population-based case-control study, we examined the association between socioeconomic status (SES) and risk of community-acquired bacteremia, as well as the contribution of chronic diseases and substance abuse to differences in bacteremia risk. Analyses were based on 4,117 patients aged 30-65 years who were hospitalized with first-time community-acquired bacteremia during 2000-2008 and 41,170 population controls matched by sex, age, and region of residence. Individual-level information on SES (education and income), chronic diseases, and substance abuse was retrieved from public and medical registries. Conditional logistic regression was used to compute odds ratios for bacteremia. Persons of low SES had a substantially higher risk of bacteremia than those of high SES (for short duration of education vs. long duration, odds ratio = 2.30 (95% confidence interval: 2.10, 2.52); for low income vs. high income, odds ratio = 2.77 (95% confidence interval: 2.54, 3.02)). A higher prevalence of chronic diseases and substance abuse in low-SES individuals versus high-SES individuals explained 43%-48% of the socioeconomic differences in bacteremia risk. In a country with a universal welfare system, differences in the burden of chronic diseases and substance abuse seem to have major importance in explaining inequalities in bacteremia risk.

The effect of community-acquired bacteraemia on return to workforce, risk of sick leave, permanent disability pension and death: a Danish population-based cohort study.

OBJECTIVES: Little is known about the prognosis of community-acquired bacteraemia (CAB) in workforce adults. We assessed return to workforce, risk for sick leave, disability pension and mortality within 1 year after CAB in workforce adults compared with blood culture-negative controls and population controls. DESIGN: Population-based cohort study. SETTING: North Denmark, 1996-2011. PARTICIPANTS: We used population-based healthcare registries to identify all patients aged 20-58 years who had first-time blood cultures obtained within 48 h of medical hospital admission, and who were part of the workforce (450 bacteraemia exposed patients and 6936 culture-negative control patients). For each bacteraemia patient, we included up to 10 matched population controls. PRIMARY AND SECONDARY OUTCOME MEASURES: Return to workforce, risk of sick leave, permanent disability pension and mortality within 1 year after bacteraemia. Regression analyses were used to compute adjusted relative risks (RRs) with 95% CIs. RESULTS: One year after admission, 78% of patients with CAB, 85.7% of culture-negative controls and 96.8% of population controls were alive and in the workforce, and free from sick leave or disability pension. Compared with culture-negative controls, bacteraemia was associated with an increased risk for long-term sick leave (4-week duration, 40.2% vs 23.9%, adjusted RR, 1.51; CI 1.34 to 1.70) and an increased risk for mortality (30-day mortality, 4% vs 1.4%, adjusted RR, 2.34, CI 1.22 to 4.50; 1-year mortality, 8% vs 3.9%, adjusted RR, 1.73; CI 1.18 to 2.55). Bacteraemia patients had a risk for disability pension similar to culture-negative controls (2.7% vs 2.6%, adjusted RR, 0.99, CI 0.48 to 2.02) but greater than population controls (adjusted RR, 5.20; 95% CI 2.16 to 12.50). CONCLUSIONS: CAB is associated with long duration of sick leave and considerable mortality in working-age adults when compared with blood culture-negative controls, and an increased 1-year risk for disability pension when compared with population controls.

Effect of socioeconomic status on mortality after bacteremia in working-age patients. A Danish population-based cohort study.

OBJECTIVES: To examine the effect of socioeconomic status (SES) on mortality in patients with bacteremia and the underlying factors that may mediate differences in mortality. METHODS: We conducted a population-based cohort study in two Danish regions. All patients 30 to 65 years of age with first time bacteremia from 2000 through 2008 were identified in a population-based microbiological bacteremia database (n = 8,653). Individual-level data on patients' SES (educational level and personal income) and comorbid conditions were obtained from public and medical registries. We used Cox regression to examine mortality within 30 days after bacteremia with and without cumulative adjustment for potential mediators. RESULTS: Bacteremia patients of low SES were more likely to live alone and be unmarried than patients of high SES. They also had more pre-existing comorbidity, more substance abuse, more Staphylococcus aureus and nosocomial infections, and more admissions to small nonteaching hospitals. Overall, 1,374 patients (15.9%) died within 30 days of follow-up. Patients of low SES had consistently higher mortality after bacteremia than those of high SES crude hazard ratio for low vs. high education, 1.38 [95% confidence interval (CI), 1.18-1.61]; crude hazard ratio for low-income vs. high-income tertile, 1.58 [CI, 1.39-1.80]. Adjustment for differences in social support, pre-existing comorbidity, substance abuse, place of acquisition of the infection, and microbial agent substantially attenuated the effect of SES on mortality (adjusted hazard ratio for low vs. high education, 1.15 [95% CI, 0.98-1.36]; adjusted hazard ratio for low-income vs. high-income tertile, 1.29 [CI, 1.12-1.49]). Further adjustment for characteristics of the admitting hospital had minimal effect on observed mortality differences. CONCLUSIONS: Low SES was strongly associated with increased 30-day mortality after bacteremia. Less social support, more pre-existing comorbidity, more substance abuse, and differences in place of acquisition and agent of infection appeared to mediate much of the observed disparities in mortality.

International travel and the risk of hospitalization with non-typhoidal Salmonella bacteremia. A Danish population-based cohort study, 1999-2008.

BACKGROUND: Information is sparse regarding the association between international travel and hospitalization with non-typhoidal Salmonella bacteremia. The aim of this study was to determine the proportion, risk factors and outcomes of travel-related non-typhoidal Salmonella bacteremia. METHODS: We conducted a 10-year population-based cohort study of all patients hospitalized with non-typhoidal Salmonella bacteremia in three Danish counties (population 1.6 million). We used denominator data on Danish travellers to assess the risk per 100,000 travellers according to age and travel destination. We used patients contemporaneously diagnosed with travel-related Salmonella gastroenteritis as reference patients to estimate the relative risk of presenting with travel-related bacteremia as compared with gastroenteritis. To evaluate clinical outcomes, we compared patients with travel-related bacteremia and patients with domestically acquired bacteremia in terms of length of hospital stay, number of extraintestinal focal infections and mortality after 30 and 90 days. RESULTS: We identified 311 patients hospitalized with non-typhoidal Salmonella bacteremia of whom 76 (24.4%) had a history of international travel. The risk of travel-related bacteremia per traveller was highest in the age groups 15-24 years (0.8/100,000 travellers) and 65 years and above (1.2/100,000 travellers). The sex- and age-adjusted relative risk of presenting with bacteremia was associated with travel to Sub-Saharan Africa (odds ratio 18.4; 95% confidence interval [6.9-49.5]), the Middle East (10.6; [2.1-53.2]) and South East Asia (4.0; [2.2-7.5]). We found high-risk countries in the same three regions when estimating the risk per traveller according to travel destination. Patients hospitalized with travel-related bacteremia had better clinical outcomes than patients with domestically acquired bacteremia, they had a shorter length of hospital stay (8 vs. 11 days), less extraintestinal focal infections (5 vs. 31 patients) and a lower risk of death within both 30 days (relative risk 0.2; [0.1-0.7]) and 90 days (0.3; [0.1-0.7]). A healthy traveller effect was a plausible explanation for the observed differences in outcomes. CONCLUSIONS: International travel is a notable risk factor for being hospitalized with non-typhoidal Salmonella bacteremia and the risk differs between age groups and travel destinations. Healthy travellers hospitalized with bacteremia are less likely to have poor outcomes than patients with domestically acquired bacteremia.