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Background A mismatch between a widespread use of broad-spectrum antibiotic agents and a low prevalence of reported bacterial co-infections in patients with SARS-CoV-2 infections has been observed . Herein, we sought to characterize and compare bacterial co-infections at admission in hospitalized patients with SARS-CoV-2, influenza or respiratory syncytial virus (RSV) positive community-acquired pneumonia (CAP). Methods A retrospective cohort study of bacterial co-infections at admission in SARS-CoV-2, influenza or RSV-positive adult patients with CAP admitted to Karolinska University Hospital in Stockholm, Sweden, from year 2011 to 2020. The prevalence of bacterial co-infections was investigated and compared between the three virus groups. In each virus group, length of stay, ICU-admission and 30-day mortality was compared in patients with and without bacterial co-infection, adjusting for age, sex and co-morbidities. In the SARS-CoV-2 group, risk factors for bacterial co-infection, were assessed using logistic regression models and creation of two scoring systems based on disease severity, age, co-morbidities and inflammatory markers with assessment of concordance statistics. Results Compared to influenza and RSV, the bacterial co-infection testing frequency in SARS-CoV-2 was lower for all included test modalities. Four percent [46/1243 (95% CI 3–5)] of all SARS-CoV-2 patients had a bacterial co-infection at admission, whereas the proportion was 27% [209/775 (95% CI 24–30)] and 29% [69/242 (95% CI 23–35)] in influenza and RSV, respectively. S. pneumoniae and S. aureus constituted the most common bacterial findings for all three virus groups. Comparing SARS-CoV-2 positive patients with and without bacterial co-infection at admission, a relevant association could not be demonstrated nor excluded with regards to risk of ICU-admission (aHR 1.53, 95% CI 0.87–2.69) or 30-day mortality (aHR 1.28, 95% CI 0.66–2.46) in adjusted analyses. Bacterial co-infection was associated with increased inflammatory markers, but the diagnostic accuracy was not substantially different in a scoring system based on disease severity, age, co-morbidities and inflammatory parameters [C statistic 0.66 (95% CI 0.59–0.74)], compared to using disease severity, age and co-morbidities only [C statistic 0.63 (95% CI 0.56–0.70)]. Conclusions The prevalence of bacterial co-infections was significantly lower in patients with community-acquired SARS-CoV-2 positive pneumonia as compared to influenza and RSV positive pneumonia.

Clinical isolates of Klebsiella pneumoniae are divided into three phylogroups and differ in their virulence factor contents. The aim of this study was to determine an association between phylogroup, virulence factors and mortality following bloodstream infection (BSI) caused by Klebsiella pneumoniae. Isolates from all adult patients with BSI caused by K. pneumoniae admitted to Karolinska University Hospital, Solna between 2007 and 2009 (n = 139) were included in the study. Phylogenetic analysis was performed based on multilocus sequence typing (MLST) data. Testing for mucoid phenotype, multiplex PCR determining serotypes K1, K2, K5, K20, K54 and K57, and testing for virulence factors connected to more severe disease in previous studies, was also performed. Data was retrieved from medical records including age, sex, comorbidity, central and urinary catheters, time to adequate treatment, hospital-acquired infection, and mortality, to identify risk factors. The primary end-point was 30- day mortality. The three K. pneumoniae phylogroups were represented: KpI (n = 96), KpII (corresponding to K. quasipneumoniae, n = 9) and KpIII (corresponding to K. variicola, n = 34). Phylogroups were not significantly different in baseline characteristics. Overall, the 30-day mortality was 24/139 (17.3%). Isolates belonging to KpIII were associated with the highest 30-day mortality (10/34 cases, 29.4%), whereas KpI isolates were associated with mortality in 13/96 cases (13.5%). This difference was significant both in univariate statistical analysis (P = 0.037) and in multivariate analysis adjusting for age and comorbidity (OR 3.03 (95% CI: 1.10-8.36). Only three of the isolates causing mortality within 30 days belonged to any of the virulent serotypes (K54, n = 1), had a mucoid phenotype (n = 1) and/or contained virulence genes (wcaG n = 1 and wcaG/allS n = 1). In conclusion, the results indicate higher mortality among patients infected with isolates belonging to K. variicola. The increased mortality could not be related to any known virulence factors, including virulent capsular types or mucoid phenotype.

Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model—SepsisFinder—which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care.

Background Studies elucidating the risk factors for nosocomial SARS-CoV-2 infections or assessing effectiveness of vaccination on acquisition prevention throughout the pandemic period are scarce. Here, we aimed to identify individual- and care-related risk factors and study the association between vaccination and risk of infection for nosocomial SARS-CoV-2 infections. Methods We performed a nested case-control study of patients aged ≥ 18 years hospitalised in Region Stockholm, between 1 March 2020 -15 November 2023. Each case was matched to up to 10 controls, on admission date, time period, and length of stay. Individual factors of sex, age, region of birth, education level, comorbidities and care-related factors such as number of transfers during care episode, surgery status, type of hospital unit and hospitals in the region were assessed. Vaccine effectiveness was assessed for number of doses and time since last vaccination. Conditional logistic regression was used to calculate odds ratio for risk factors and vaccine effectiveness. Results Among 2711 cases and 27,065 matched controls, older age (90 + years: 4.11 [2.71–6.23]), male sex (1.11 [1.02–1.21]) and chronic lung disease (1.25 [1.12 to 1.40]) were associated with increased odds of nosocomial infection. Among care-related factors, admission to geriatric hospital units (1.54 [1.26–1.89]) and increased number of transfers (2 + transfers: 2.48 [1.89–2.34]) were associated with higher odds. Overall, while vaccination with 2 or more doses and any time since last dose compared to being unvaccinated was associated with lower odds of infections, we observed that vaccination with 3 doses (aOR: 0.55, 95% CI: 0.46–0.67), and those with their latest dose administered within the last 3 months had the lowest odds of infection (aOR: 0.48, 95% CI: 0.39 to 0.59). Conclusions We demonstrated that vaccination is effective in reducing the risk of nosocomial SARS-CoV-2 infection, and preventive measures during early phases of pandemics should focus on high-risk patient groups. Risk of infection can be further reduced by focusing on high-risk areas within hospital settings and by reducing patient transfers during their care episode.

In recent years, the world has seen a surge in extended-spectrum β-lactamase (ESBL)-producing bacteria. However, data on the dissemination of ESBL-producing Enterobacteriaceae in the community from systematically enrolled study subjects in Africa remains limited. To determine the prevalence, phenotypic resistance patterns and genetic characteristics of ESBL-producing E. coli and K. pneumoniae in fecal carriage and to analyze associated risk factors in children attending a pediatric emergency department in Guinea-Bissau. From June to September 2010, children <5 years of age with fever or tachycardia attending a pediatric emergency ward during the day was screened for ESBL carriage in feces. Socio-demographic and health seeking behavior data was collected. Antibiotic susceptibility was tested with VITEK2 and EUCAST disk diffusion method, molecular characterization of ESBL-encoding genes was performed with multiplex PCR and clonal relatedness was established by automated rep-PCR. Of 408 enrolled children 133 (32.6%) were ESBL carriers. In total, 83 E. coli and 91 K. pneumoniae ESBL-producing isolates were obtained. Nearly all isolates were multidrug-resistant. Co-resistance to ciprofloxacin, trimethoprim-sulfamethoxazole and aminoglycosides was common. Of the isolates, 38.5% were co-resistant to these classes plus extended-spectrum cephalosporins, which infers resistance to all easily available antibiotic agents for treatment of gram-negative sepsis in Guinea-Bissau. The predominant resistance-encoding gene subgroup was bla(CTX-M-1) and epidemiologic typing showed that the bacterial ESBL population was highly diverse both for E. coli and K. pneumoniae. Bed sharing with another child <5 years of age was a risk factor for ESBL carriage, indicating crowding as a potential risk factor for transmission of ESBL-producing bacteria. Prevalence of ESBL-producing bacteria in this population was high and clonally diverse. This is alarming considering the limited diagnostic and treatment possibilities in Guinea-Bissau and other resource-poor countries.

Background In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR). Methods Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated. Results From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5–99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm. Conclusions The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4–12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.

Background Continuous surveillance for healthcare-associated infections such as central venous catheter-related bloodstream infections (CVC-BSI) is crucial for prevention. However, traditional surveillance methods are resource-intensive and prone to bias. This study aimed to develop and validate fully-automated surveillance algorithms for CVC-BSI. Methods Two algorithms were developed using electronic health record data from 1000 admissions with a positive blood culture (BCx) at Karolinska University Hospital from 2017: (1) Combining microbiological findings in BCx and CVC cultures with BSI symptoms; (2) Only using microbiological findings. These algorithms were validated in 5170 potential CVC-BSI-episodes from all admissions in 2018–2019, and results extrapolated to all potential CVC-BSI-episodes within this period (n = 181,354). The reference standard was manual record review according to ECDC’s definition of microbiologically confirmed CVC-BSI (CRI3-CVC). Results In the potential CVC-BSI-episodes, 51 fulfilled ECDC’s definition and the algorithms identified 47 and 49 episodes as CVC-BSI, respectively. Both algorithms performed well in assessing CVC-BSI. Overall, algorithm 2 performed slightly better with in the total period a sensitivity of 0.880 (95%-CI 0.783–0.959), specificity of 1.000 (95%-CI 0.999–1.000), PPV of 0.918 (95%-CI 0.833–0.981) and NPV of 1.000 (95%-CI 0.999–1.000). Incidence according to the reference and algorithm 2 was 0.33 and 0.31 per 1000 in-patient hospital-days, respectively. Conclusions Both fully-automated surveillance algorithms for CVC-BSI performed well and could effectively replace manual surveillance. The simpler algorithm, using only microbiology data, is suitable when BCx testing adheres to recommendations, otherwise the algorithm using symptom data might be required. Further validation in other settings is necessary to assess the algorithms’ generalisability.

Background Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surveillance patients are classified as low or high probability of having developed infection, and only high probability patients subsequently undergo manual record review. The aim of this study was to externally validate two existing semi-automated surveillance algorithms for deep SSI after colorectal surgery, developed on Spanish and Dutch data, in a Swedish setting. Methods The algorithms were validated in 225 randomly selected surgeries from Karolinska University Hospital from the period January 1, 2015 until August 31, 2020. Both algorithms were based on (re)admission and discharge data, mortality, reoperations, radiology orders, and antibiotic prescriptions, while one additionally used microbiology cultures. SSI was based on ECDC definitions. Sensitivity, specificity, positive predictive value, negative predictive value, and workload reduction were assessed compared to manual surveillance. Results Both algorithms performed well, yet the algorithm not relying on microbiological culture data had highest sensitivity (97.6, 95%CI: 87.4–99.6), which was comparable to previously published results. The latter algorithm aligned best with clinical practice and would lead to 57% records less to review. Conclusions The results highlight the importance of thorough validation before implementation in other clinical settings than in which algorithms were originally developed: the algorithm excluding microbiology cultures had highest sensitivity in this new setting and has the potential to support large-scale semi-automated surveillance of SSI after colorectal surgery.

We aimed to study if certain clinical and/or microbiological factors are associated with a high nasopharyngeal (NP) density of Streptococcus pneumoniae in pneumococcal pneumonia. In addition, we aimed to study if a high NP pneumococcal density could be useful to detect severe pneumococcal pneumonia. Adult patients hospitalized for radiologically confirmed community-acquired pneumonia were included in a prospective study. NP aspirates were collected at admission and were subjected to quantitative PCR for pneumococcal DNA (Spn9802 DNA). Patients were considered to have pneumococcal etiology if S. pneumoniae was detected in blood culture and/or culture of respiratory secretions and/or urinary antigen test. Of 166 included patients, 68 patients had pneumococcal DNA detected in NP aspirate. Pneumococcal etiology was noted in 57 patients (84%) with positive and 8 patients (8.2%) with negative test for pneumococcal DNA (p<0.0001). The median NP pneumococcal density of DNA positive patients with pneumococcal etiology was 6.83 log10 DNA copies/mL (range 1.79-9.50). In a multivariate analysis of patients with pneumococcal etiology, a high pneumococcal density was independently associated with severe pneumonia (Pneumonia Severity Index risk class IV-V), symptom duration ≥2 days prior to admission, and a medium/high serum immunoglobulin titer against the patient's own pneumococcal serotype. NP pneumococcal density was not associated with sex, age, smoking, co-morbidity, viral co-infection, pneumococcal serotype, or bacteremia. Severe pneumococcal pneumonia was noted in 28 study patients. When we studied the performance of PCR with different DNA cut-off levels for detection of severe pneumococcal pneumonia, we found sensitivities of 54-82% and positive predictive values of 37-56%, indicating suboptimal performance. Pneumonia severity, symptom duration ≥2 days, and a medium/high serum immunoglobulin titer against the patient's own serotype were independently associated with a high NP pneumococcal density. NP pneumococcal density has limited value for detection of severe pneumococcal pneumonia.

Background Pseudomonas aeruginosa (PA) bloodstream infection (BSI) is a common healthcare-associated complication linked to antimicrobial resistance and high mortality. Ongoing clinical trials are exploring novel anti-virulence agents, yet studies on how bacterial virulence affects PA infection outcomes is conflicting and data from real-world clinical populations is limited. Methods We studied a multicentre cohort of 773 adult patients with PA BSI consecutively collected during 7-years from sites in Europe and Australia. Comprehensive clinical data and whole-genome sequencing of all bacterial strains were obtained. Results Based on the virulence genotype, we identify several virulence clusters, each showing varying proportions of multidrug-resistant phenotypes. Genes tied to biofilm synthesis and epidemic clones ST175 and ST235 are associated with mortality, while the type III secretion system is associated with septic shock. Adding genomic biomarkers to machine learning models based on clinical data indicates improved prediction of severe outcomes in PA BSI patients. Conclusions These findings suggest that virulence markers provide prognostic information with potential applications in guiding adjuvant sepsis treatments. Plain language summary Pseudomonas aeruginosa bacteria are often found in the hospital environment, primarily infecting vulnerable patients with underlying health conditions. Due to antibiotic resistance, which occurs when bacteria are not killed by antibiotic treatment, these infections are often difficult to treat, and death rates are high. In this study, we analyzed data from patients in Europe and Australia with bloodstream infections to understand how bacterial traits affect patient outcomes. Using genetic information from the bacteria, we identified characteristics associated with antibiotic resistance. In addition, we found certain bacterial traits, such as the ability to synthesize toxins and biofilms, were linked to disease severity and mortality risk. These findings indicate that specific characteristics of P. aeruginosa may influence the severity of infection and could be targeted in newly developed treatments. Valik et al. incorporate genomic biomarkers from Pseudomonas aeruginosa isolates and clinical data into machine-learning models to predict the severity of bloodstream infection. Bacterial virulence markers identified through whole genome sequencing offer prognostic insights that could inform treatment strategies in sepsis management.