Incidences of community onset severe sepsis, Sepsis-3 sepsis, and bacteremia in Sweden - A prospective population-based study

Re-visiting humoral constitutive antibacterial heterogeneity in bloodstream infections

Although cellular immunity has garnered much attention in the era of single-cell technologies, humoral innate immunity has receded in priority due to its presumed limited roles. Hence, despite the long-recognised bactericidal activity of serum-a functional characteristic of constitutive humoral immunity-much remains unclear regarding mechanisms underlying its inter-individual heterogeneity and clinical implications in bloodstream infections. Recent work suggests that the immediate antimicrobial effect of humoral innate immunity contributes to suppression of the excessive inflammatory responses to infection by reducing the amount of pathogen-associated molecular patterns. In this Personal View, we propose the need to re-explore factors underlying the inter-individual heterogeneity in serum antibacterial competence as a new approach to better understand humoral innate immunity and revisit the clinical use of measuring serum antibacterial activity in the management of bacterial bloodstream infections. Given the current emphasis on subtyping sepsis, a serum bactericidal assay might prove useful in defining a distinct sepsis endotype, to enable more personalised management.

Design of a national patient-centred clinical pathway for sepsis in Sweden

BACKGROUND

The World Health Organization has adopted a resolution on sepsis and urged member states to develop national processes to improve sepsis care. In Sweden, sepsis was selected as one of the ten first diagnoses to be addressed, when the Swedish government in 2019 allocated funds for patient-centred clinical pathways in healthcare. A national multidisciplinary working group, including a patient representative, was appointed to develop the patient-centred clinical pathway for sepsis.

METHODS

The working group mapped challenges and needs surrounding sepsis care and included a survey sent to all emergency departments (ED) in Sweden, and then designed a patient-centred clinical pathway for sepsis.

RESULTS

The working group decided to focus on the following four areas: (1) sepsis alert for early detection and management optimisation for the most severely ill sepsis patients in the ED; (2) accurate sepsis diagnosis coding; (3) structured information to patients at discharge after sepsis care and (4) structured telephone follow-up after sepsis care. A health-economic analysis indicated that the implementation of the clinical pathway for sepsis will most likely not drive costs. An important aspect of the clinical pathway is implementing continuous monitoring of performance and process indicators. A national working group is currently building up such a system for monitoring, focusing on extraction of this information from the electronic health records systems.

CONCLUSION

A national patient-centred clinical pathway for sepsis has been developed and is currently being implemented in Swedish healthcare. We believe that the clinical pathway and the accompanying monitoring will provide a more efficient and equal sepsis care and improved possibilities to monitor and further develop sepsis care in Sweden.

Predicting sepsis using a combination of clinical information and molecular immune markers sampled in the ambulance

Sepsis is a time dependent condition. Screening tools based on clinical parameters have been shown to increase the identification of sepsis. The aim of current study was to evaluate the additional predictive value of immunological molecular markers to our previously developed prehospital screening tools. This is a prospective cohort study of 551 adult patients with suspected infection in the ambulance setting of Stockholm, Sweden between 2017 and 2018. Initially, 74 molecules and 15 genes related to inflammation were evaluated in a screening cohort of 46 patients with outcome sepsis and 50 patients with outcome infection no sepsis. Next, 12 selected molecules, as potentially synergistic predictors, were evaluated in combination with our previously developed screening tools based on clinical parameters in a prediction cohort (n = 455). Seven different algorithms with nested cross-validation were used in the machine learning of the prediction models. Model performances were compared using posterior distributions of average area under the receiver operating characteristic (ROC) curve (AUC) and difference in AUCs. Model variable importance was assessed by permutation of variable values, scoring loss of classification as metric and with model-specific weights when applicable. When comparing the screening tools with and without added molecular variables, and their interactions, the molecules per se did not increase the predictive values. Prediction models based on the molecular variables alone showed a performance in terms of AUCs between 0.65 and 0.70. Among the molecular variables, IL-1Ra, IL-17A, CCL19, CX3CL1 and TNF were significantly higher in septic patients compared to the infection non-sepsis group. Combing immunological molecular markers with clinical parameters did not increase the predictive values of the screening tools, most likely due to the high multicollinearity of temperature and some of the markers. A group of sepsis patients was consistently miss-classified in our prediction models, due to milder symptoms as well as lower expression levels of the investigated immune mediators. This indicates a need of stratifying septic patients with a priori knowledge of certain clinical and molecular parameters in order to improve prediction for early sepsis diagnosis.Trial registration: NCT03249597. Registered 15 August 2017.

Epidemiology, Resistance Profiles, and Outcomes of Bloodstream Infections in Community-Onset Sepsis in the United States

OBJECTIVES

To describe frequency of positive blood cultures, patterns of pathogens' characteristics and their resistance profile in patients with blood cultures drawn due to a presumed diagnosis of community-onset sepsis, and to examine the association between blood culture-positive pathogens and hospital mortality.

DESIGN

Retrospective cohort study.

SETTING

Two hundred one U.S. hospitals from 2016 to 2020 using the Premier Healthcare Database.

SUBJECTS

Adult patients presenting with community-onset sepsis who had blood cultures collected within 2 days of hospital admission. We defined sepsis using the U.S. Centers for Disease Control Adult Sepsis Event Surveillance criteria.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We identified 147,061 patients with community-onset sepsis. The number of blood culture-positive sepsis episodes was 21,167 (14%) and the number of nonblood culture-positive sepsis episodes was 20,326 (14%). Among patients with blood culture-positive sepsis, Gram-negative rods were isolated in 55% of patients, Gram-positive cocci were isolated in 47%. Of those, methicillin-resistant Staphylococcus aureus (MRSA) was 11%, ceftriaxone-resistant Enterobacterales /extended-spectrum β-lactamase was 7%, and carbapenem-resistant Enterobacterales was 1.3%. The crude in-hospital mortality was 17% for culture-negative sepsis, 13% for nonblood culture-positive sepsis, and 17% for blood culture-positive sepsis. In multilevel logistic regression models, compared with culture-negative sepsis, blood culture-positive sepsis (adjusted odds ratio [aOR], 0.89; 95% CI, 0.85-0.94) and nonblood culture-positive sepsis (aOR, 0.82; 95% CI, 0.78-0.87) were associated with lower odds of in-hospital mortality. Acinetobacter species, Pseudomonas aeruginosa , methicillin-sensitive Staphylococcus aureus , and MRSA were associated with higher in-hospital mortality, whereas Escherichia coli , Klebsiella species, Proteus species, and Streptococcus species were associated with lower in-hospital mortality.

CONCLUSIONS

In patients hospitalized with community-onset sepsis, the prevalence of blood culture-positive sepsis was 14%. Among positive blood culture sepsis resistant organisms were infrequent. Compared with culture-negative sepsis, blood culture-positive sepsis and nonblood culture-positive sepsis were associated with lower in-hospital mortality.

Delayed Treatment of Bloodstream Infection at Admission is Associated With Initial Low Early Warning Score and Increased Mortality

OBJECTIVES

To identify factors associated with antibiotic treatment delay in patients admitted with bloodstream infections (BSIs).

DESIGN

Retrospective cohort study.

SETTING

North Zealand Hospital, Denmark.

PATIENTS

Adult patients with positive blood cultures obtained within the first 48 hours of admission between January 1, 2015, and December 31, 2015 (n = 926).

MEASUREMENTS AND MAIN RESULTS

First recorded Early Warning Score (EWS), patient characteristics, time to antibiotic treatment, and survival at day 60 after admission were obtained from electronic health records and medicine module. Presence of contaminants and the match between the antibiotic treatment and susceptibility of the cultured microorganism were included in the analysis. Data were stratified according to EWS quartiles. Overall, time from admission to prescription of antibiotic treatment was 3.7 (3.4-4.0) hours, whereas time from admission to antibiotic treatment was 5.7 (5.4-6.1) hours. A gap between prescription and administration of antibiotic treatment was present across all EWS quartiles. Importantly, 23.4% of patients admitted with BSI presented with an initial EWS 0-1. Within this group of patients, time to antibiotic treatment was markedly higher among nonsurvivors at day 60 compared with survivors. Furthermore, time to antibiotic treatment later than 6 hours was associated with increased mortality at day 60. Among patients with an initial EWS of 0-1, 51.3% of survivors received antibiotic treatment within 6 hours, whereas only 19.0% of nonsurvivors received antibiotic treatment within 6 hours.

CONCLUSIONS

Among patients with initial low EWS, delay in antibiotic treatment of BSIs was associated with increased mortality at day 60. Lag from prescription to administration may contribute to delayed antibiotic treatment. A more frequent reevaluation of patients with infections with a low initial EWS and reduction of time from prescription to administration may reduce the time to antibiotic treatment, thus potentially improving survival.

Update on Sepsis Epidemiology in the Era of COVID-19

Timely and accurate data on the epidemiology of sepsis is essential to inform public policy, clinical practice, and research priorities. Recent studies have illuminated several ongoing questions about sepsis epidemiology, including the incidence and outcomes of sepsis in non-Western countries and in specialized populations such as surgical patients, patients with cancer, and the elderly. There have also been new insights into the limitations of current surveillance methods using administrative data and increasing experience tracking sepsis incidence and outcomes using "big data" approaches that take advantage of detailed electronic health record data. The COVID-19 pandemic, however, has fundamentally changed the landscape of sepsis epidemiology. It has increased sepsis rates, helped highlight ongoing controversies about how to define sepsis, and intensified debate about the possible unintended consequences of overly rigid sepsis care bundles. Despite these controversies, there is a growing consensus that severe COVID-19 causing organ dysfunction is appropriate to label as sepsis, even though it is treated very differently from bacterial sepsis, and that surveillance strategies need to be modified to reliably identify these cases to fully capture and delineate the current burden of sepsis. This review will summarize recent insights into the epidemiology of sepsis and highlight several urgent questions and priorities catalyzed by COVID-19.

Benchmarking of two bioinformatic workflows for the analysis of whole-genome sequenced Staphylococcus aureus collected from patients with suspected sepsis

BACKGROUND

The rapidly growing area of sequencing technologies, and more specifically bacterial whole-genome sequencing, could offer applications in clinical microbiology, including species identification of bacteria, prediction of genetic antibiotic susceptibility and virulence genes simultaneously. To accomplish the aforementioned points, the commercial cloud-based platform, 1928 platform (1928 Diagnostics, Gothenburg, Sweden) was benchmarked against an in-house developed bioinformatic pipeline as well as to reference methods in the clinical laboratory.

METHODS

Whole-genome sequencing data retrieved from 264 Staphylococcus aureus isolates using the Illumina HiSeq X next-generation sequencing technology was used. The S. aureus isolates were collected during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital, in the western region of Sweden. The collected isolates were characterized according to accredited laboratory methods i.e., species identification by MALDI-TOF MS analysis and phenotypic antibiotic susceptibility testing (AST) by following the EUCAST guidelines. Concordance between laboratory methods and bioinformatic tools, as well as concordance between the bioinformatic tools was assessed by calculating the percent of agreement.

RESULTS

There was an overall high agreement between predicted genotypic AST and phenotypic AST results, 98.0% (989/1006, 95% CI 97.3-99.0). Nevertheless, the 1928 platform delivered predicted genotypic AST results with lower very major error rates but somewhat higher major error rates compared to the in-house pipeline. There were differences in processing times i.e., minutes versus hours, where the 1928 platform delivered the results faster. Furthermore, the bioinformatic workflows showed overall 99.4% (1267/1275, 95% CI 98.7-99.7) agreement in genetic prediction of the virulence gene characteristics and overall 97.9% (231/236, 95% CI 95.0-99.2%) agreement in predicting the sequence types (ST) of the S. aureus isolates.

CONCLUSIONS

Altogether, the benchmarking disclosed that both bioinformatic workflows are able to deliver results with high accuracy aiding diagnostics of severe infections caused by S. aureus. It also illustrates the need of international agreement on quality control and metrics to facilitate standardization of analytical approaches for whole-genome sequencing based predictions.

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis

Background

Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed.

Methods and results

A total of 1,936 patients (sepsis samples, n=1,692; normal samples, n=244) in 7 discovery datasets were included to conduct weighted gene co-expression network analysis (WGCNA) to filter out candidate genes related to sepsis. Then, two subtypes of sepsis were classified in the training sepsis set (n=1,692), the Adaptive and Inflammatory, using K-means clustering analysis on 90 sepsis-related features. We validated these subtypes using 617 samples in 5 independent datasets and the merged 5 sets. Cibersort method revealed the Adaptive subtype was related to high infiltration levels of T cells and natural killer (NK) cells and a better clinical outcome. Immune features were validated by single-cell RNA sequencing (scRNA-seq) analysis. The Inflammatory subtype was associated with high infiltration of macrophages and a disadvantageous prognosis. Based on functional analysis, upregulation of the Toll-like receptor signaling pathway was obtained in Inflammatory subtype and NK cell-mediated cytotoxicity and T cell receptor signaling pathway were upregulated in Adaptive group. To quantify the cluster findings, a scoring system, called, risk score, was established using four datasets (n=980) in the discovery cohorts based on least absolute shrinkage and selection operator (LASSO) and logistic regression and validated in external sets (n=760). Multivariate logistic regression analysis revealed the risk score was an independent predictor of outcomes of sepsis patients (OR [odds ratio], 2.752, 95% confidence interval [CI], 2.234-3.389, P<0.001), when adjusted by age and gender. In addition, the validation sets confirmed the performance (OR, 1.638, 95% CI, 1.309-2.048, P<0.001). Finally, nomograms demonstrated great discriminatory potential than that of risk score, age and gender (training set: AUC=0.682, 95% CI, 0.643-0.719; validation set: AUC=0.624, 95% CI, 0.576-0.664). Decision curve analysis (DCA) demonstrated that the nomograms were clinically useful and had better discriminative performance to recognize patients at high risk than the age, gender and risk score, respectively.

Conclusions

In-depth analysis of a comprehensive landscape of the transcriptome characteristics of sepsis might contribute to personalized treatments and prediction of clinical outcomes.

Correlation of clinical sepsis definitions with microbiological characteristics in patients admitted through a sepsis alert system; a prospective cohort study

Background

Sepsis was recently redefined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. With this redefinition (Sepsis-3), clinical and microbiological characteristics of patients with sepsis may differ from the patients fulfilling the previous definition (Sepsis-2).

Purpose

To describe differences in clinical and microbiological characteristics of sepsis episodes between Sepsis-3 and Sepsis-2. The secondary aim was to compare blood culture outcomes between episodes fulfilling Sepsis-3 and Sepsis-2 criteria, respectively.

Methods

A prospective study design was used to include patients presenting with clinically suspected sepsis in the emergency department. Six blood culture bottles were collected from each patient. Blood cultures were described as having clinically relevant growth, contaminant growth, or no growth. Clinical and laboratory data were collected from medical records and the laboratory information system.

Results

The analysis included 514 episodes. There were 357/514 (79.5%) Sepsis-3 and 411/514 (80.0%) Sepsis-2 episodes. In total, 341/514 (66.3%) episodes fulfilled both Sepsis-3 and Sepsis-2 criteria. Blood cultures were positive for clinically relevant growth in 130/357 (36.1%) and 145/411 (35.3%) episodes in Sepsis-3 and Sepsis-2, respectively. Other clinical and microbiological characteristics did not differ between Sepsis-3 and Sepsis-2.

Conclusions

A high proportion of patients included through a sepsis alert system fulfilled both Sepsis-3 and Sepsis-2 criteria. The performance of blood cultures in detection of microorganisms was poor and were similar in Sepsis-3 and Sepsis-2 patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12941-022-00498-3.

Cerebral dysfunctions caused by sepsis during ageing

Systemic inflammation elicited by sepsis can induce an acute cerebral dysfunction known as sepsis-associated encephalopathy (SAE). Recent evidence suggests that SAE is common but shows a dynamic trajectory over time. Half of all patients with sepsis develop SAE in the intensive care unit, and some survivors present with sustained cognitive impairments for several years after initial sepsis onset. It is not clear why some, but not all, patients develop SAE and also the factors that determine the persistence of SAE. Here, we first summarize the chronic pathology and the dynamic changes in cognitive functions seen after the onset of sepsis. We then outline the cerebral effects of sepsis, such as neuroinflammation, alterations in neuronal synapses and neurovascular changes. We discuss the key factors that might contribute to the development and persistence of SAE in older patients, including premorbid neurodegenerative pathology, side effects of sedatives, renal dysfunction and latent virus reactivation. Finally, we postulate that some of the mechanisms that underpin neuropathology in SAE may also be relevant to delirium and persisting cognitive impairments that are seen in patients with severe COVID-19.

In this Review, Manabe and Heneka examine how the systemic inflammation associated with sepsis can lead to acute cerebral dysfunction known as sepsis-associated encephalopathy (SAE). Moreover, they suggest that some of the mechanisms involved in SAE may be relevant for understanding the cognitive impairments that develop in some patients with COVID-19.

Increasing incidence and antimicrobial resistance in Escherichia coli bloodstream infections: a multinational population-based cohort study

BACKGROUND

Escherichia coli is an important pathogen in humans and is the most common cause of bacterial bloodstream infections (BSIs). The objectives of our study were to determine factors associated with E. coli BSI incidence rate and third-generation cephalosporin resistance in a multinational population-based cohort.

METHODS

We included all incident E. coli BSIs (2014-2018) from national (Finland) and regional (Australia [Canberra], Sweden [Skaraborg], and Canada [Calgary, Sherbrooke, and western interior]) surveillance. Incidence rates were directly age and sex standardized to the European Union 28-country 2018 population. Multivariable negative binomial and logistic regression models estimated factors significantly associated with E. coli BSI incidence rate and third-generation cephalosporin resistance, respectively. The explanatory variables considered for inclusion in both models were year (2014-2018), region (six areas), age (< 70-years-old and ≥ 70-years-old), and sex (female and male).

RESULTS

We identified 31,889 E. coli BSIs from 40.7 million person-years of surveillance. Overall and third-generation cephalosporin-resistant standardized rates were 87.1 and 6.6 cases/100,000 person-years, respectively, and increased 14.0% and 40.1% over the five-year study. Overall, 7.8% (2483/31889) of E. coli BSIs were third-generation cephalosporin-resistant. Calgary, Canberra, Sherbrooke, and western interior had significantly lower E. coli BSI rates compared to Finland. The significant association between age and E. coli BSI rate varied with sex. Calgary, Canberra, and western interior had significantly greater odds of third-generation cephalosporin-resistant E. coli BSIs compared to Finland. Compared to 2014, the odds of third-generation cephalosporin-resistant E. coli BSIs were significantly increased in 2016, 2017, and 2018. The significant association between age and the odds of having a third-generation cephalosporin-resistant E. coli BSI varied with sex.

CONCLUSIONS

Increases in overall and third-generation cephalosporin-resistant standardized E. coli BSI rates were clinically important. Overall, E. coli BSI incidence rates were 40-104% greater than previous investigations from the same study areas. Region, sex, and age are important variables when analyzing E. coli BSI rates and third-generation cephalosporin resistance in E. coli BSIs. Considering E. coli is the most common cause of BSIs, this increasing burden and evolving third-generation cephalosporin resistance will have an important impact on human health, especially in aging populations.

Predicting mortality among septic patients presenting to the emergency department-a cross sectional analysis using machine learning

Background

Sepsis is a life-threatening condition, causing almost one fifth of all deaths worldwide. The aim of the current study was to identify variables predictive of 7- and 30-day mortality among variables reflective of the presentation of septic patients arriving to the emergency department (ED) using machine learning.

Methods

Retrospective cross-sectional design, including all patients arriving to the ED at Södersjukhuset in Sweden during 2013 and discharged with an International Classification of Diseases (ICD)-10 code corresponding to sepsis. All predictions were made using a Balanced Random Forest Classifier and 91 variables reflecting ED presentation. An exhaustive search was used to remove unnecessary variables in the final model. A 10-fold cross validation was performed and the accuracy was described using the mean value of the following: AUC, sensitivity, specificity, PPV, NPV, positive LR and negative LR.

Results

The study population included 445 septic patients, randomised to a training (n = 356, 80%) and a validation set (n = 89, 20%). The six most important variables for predicting 7-day mortality were: “fever”, “abnormal verbal response”, “low saturation”, “arrival by emergency medical services (EMS)”, “abnormal behaviour or level of consciousness” and “chills”. The model including these variables had an AUC of 0.83 (95% CI: 0.80–0.86). The final model predicting 30-day mortality used similar six variables, however, including “breathing difficulties” instead of “abnormal behaviour or level of consciousness”. This model achieved an AUC = 0.80 (CI 95%, 0.78–0.82).

Conclusions

The results suggest that six specific variables were predictive of 7- and 30-day mortality with good accuracy which suggests that these symptoms, observations and mode of arrival may be important components to include along with vital signs in a future prediction tool of mortality among septic patients presenting to the ED. In addition, the Random Forests appears to be a suitable machine learning method on which to build future studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-021-00475-7.

Impact of obesity on outcome of severe bacterial infections

Introduction

Obesity is a rapidly growing global health concern with considerable negative impact on life-time expectancy. It has yet not been clarified if and how obesity impacts outcomes of severe bacterial infections. The aim of this study was to determine how body mass index impacts outcome of severe bacterial infections in a well-defined population-based cohort.

Methods

This study is based on a cohort of 2196 patients included in a Swedish prospective, population-based, consecutive observational study of the incidence of community-onset severe sepsis and septic shock in adults. All patients with weight and height documented in the medical records on admission were included.

Results

The case fatality rate (CFR) was negatively correlating with increasing BMI. Outcomes included 28-day CFR (p-value = 0.002), hospital CFR (p-value = 0.039) and 1-year CFR (p-value<0.001). When BMI was applied as continuous variable in a multiple logistic regression together with other possible covariates, we still could discern that BMI was associated with decreasing 28-day CFR (OR = 0.93, 95% CI 0.88–0.98, p-value = 0.009) and 1-year CFR (OR = 0.94, 95% CI 0.91–0.97, p-value<0.001).

Conclusion

The hypothesis and paradox of obesity being associated with higher survival rates in severe bacterial infections was confirmed in this prospective, population-based observational study.

Genotypic Characterization of Clinical Klebsiella spp. Isolates Collected From Patients With Suspected Community-Onset Sepsis, Sweden

Klebsiella is a genus of Gram-negative bacteria known to be opportunistic pathogens that may cause a variety of infections in humans. Highly drug-resistant Klebsiella species, especially K. pneumoniae, have emerged rapidly and are becoming a major concern in clinical management. Although K. pneumoniae is considered the most important pathogen within the genus, the true clinical significance of the other species is likely underrecognized due to the inability of conventional microbiological methods to distinguish between the species leading to high rates of misidentification. Bacterial whole-genome sequencing (WGS) enables precise species identification and characterization that other technologies do not allow. Herein, we have characterized the diversity and traits of Klebsiella spp. in community-onset infections by WGS of clinical isolates (n = 105) collected during a prospective sepsis study in Sweden. The sequencing revealed that 32 of the 82 isolates (39.0%) initially identified as K. pneumoniae with routine microbiological methods based on cultures followed by matrix-assisted laser desorption-time of flight mass spectrometry (MALDI-TOF MS) had been misidentified. Of these, 23 were identified as Klebsiella variicola and nine as other members of the K. pneumoniae complex. Comparisons of the number of resistance genes showed that significantly fewer resistance genes were detected in Klebsiella oxytoca compared to K. pneumoniae and K. variicola (both values of p < 0.001). Moreover, a high proportion of the isolates within the K. pneumoniae complex were predicted to be genotypically multidrug-resistant (MDR; 79/84, 94.0%) in contrast to K. oxytoca (3/16, 18.8%) and Klebsiella michiganensis (0/4, 0.0%). All isolates predicted as genotypically MDR were found to harbor the combination of β-lactam, fosfomycin, and quinolone resistance markers. Multi-locus sequence typing (MLST) revealed a high diversity of sequence types among the Klebsiella spp. with ST14 (10.0%) and ST5429 (10.0%) as the most prevalent ones for K. pneumoniae, ST146 for K. variicola (12.0%), and ST176 for K. oxytoca (25.0%). In conclusion, the results from this study highlight the importance of using high-resolution genotypic methods for identification and characterization of clinical Klebsiella spp. isolates. Our findings indicate that infections caused by other members of the K. pneumoniae complex than K. pneumoniae are a more common clinical problem than previously described, mainly due to high rates of misidentifications.

Bacteremic sepsis leads to higher mortality when adjusting for confounders with propensity score matching

One can falsely assume that it is well known that bacteremia is associated with higher mortality in sepsis. Only a handful of studies specifically focus on the comparison of culture-negative and culture-positive sepsis with different conclusions depending on study design. The aim of this study was to describe outcome for critically ill patients with either culture-positive or -negative sepsis in a clinical review. We also aimed to identify subphenotypes of sepsis with culture status included as candidate clinical variables. Out of 784 patients treated in intensive care with a sepsis diagnosis, blood cultures were missing in 140 excluded patients and 95 excluded patients did not fulfill a sepsis diagnosis. Of 549 included patients, 295 (54%) had bacteremia, 90 (16%) were non-bacteremic but with relevant pathogens detected and in 164 (30%) no relevant pathogen was detected. After adjusting for confounders, 90-day mortality was higher in bacteremic patients, 47%, than in non-bacteremic patients, 36%, p = 0.04. We identified 8 subphenotypes, with different mortality rates, where pathogen detection in microbial samples were important for subphenotype distinction and outcome. In conclusion, bacteremic patients had higher mortality than their non-bacteremic counter-parts and bacteremia is more common in sepsis when studied in a clinical review. For reducing population heterogeneity and improve the outcome of trials and treatment for sepsis, distinction of subphenotypes might be useful and pathogen detection an important factor.

Early detection of sepsis using artificial intelligence: a scoping review protocol

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence.

METHODS

The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O'Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain.

ETHICS AND DISSEMINATION

The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

Impact of different consensus definition criteria on sepsis diagnosis in a cohort of critically ill patients-Insights from a new mathematical probabilistic approach to mortality-based validation of sepsis criteria

Background

Sepsis-3 definition uses SOFA score to discriminate sepsis from uncomplicated infection, replacing SIRS criteria that were criticized for being inaccurate. Eligibility of sepsis-3 criteria for sepsis diagnosis and the applied validation methodology using mortality as endpoint are topic of ongoing debate. We assessed the impact of different criteria on sepsis diagnosis in our ICU and devised a mathematical approach for mortality-based validation of sepsis criteria. As infectious status is often unclear at clinical deterioration, we integrated non-infected patients into analysis.

Methods

Suspected infection, SOFA and SIRS were captured for an ICU cohort of a university center over one year. For raw scores (SIRS/SOFA) and sepsis criteria (SIRS≥2/SOFA≥2/SOFA_change≥2) frequencies and associations with in-hospital mortality were assessed. Using a mathematical approach, we estimated the correlation between sepsis and in-hospital mortality serving as reference for evaluation of observed mortality correlations of sepsis criteria.

Results

Of 791 patients, 369 (47%) were infected and 422 (53%) non-infected, with an in-hospital mortality of 39% and 15%. SIRS≥2 indicated sepsis in 90% of infected patients, SOFA≥2 in 99% and SOFA_change≥2 in 77%. In non-infected patients, SIRS, SOFA and SOFA_change were ≥2 in 78%, 88% and 58%. In AUROC analyses neither SOFA nor SIRS displayed superior mortality discrimination in infected compared to non-infected patients. The mathematically estimated correlation of sepsis and in-hospital mortality was 0.10 in infected and 0 in non-infected patients. Among sepsis criteria, solely SIRS≥2 agreed with expected correlations in both subgroups (infected: r = 0.19; non-infected: r = 0.02).

Conclusions

SOFA≥2 yielded a more liberal sepsis diagnosis than SIRS≥2. None of the criteria showed an infection specific occurrence that would be essential for reliable sepsis detection. However, SIRS≥2 matched the mortality association pattern of a valid sepsis criterion, whereas SOFA-based criteria did not. With this study, we establish a mathematical approach to mortality-based evaluation of sepsis criteria.

Immature platelet indices alongside procalcitonin for sensitive and specific identification of bacteremia in the intensive care unit

Hematological markers that can be rapidly analyzed and regularly monitored during a patient's stay on ICU, and that can identify bacterial causes of sepsis are being extensively sought. The significance of platelets in early immunological responses provides justification for assessing their usefulness in the identification of bacteremia amongst sepsis patients. In this preliminary study, the full blood count, including the platelet count by impedance (PLT-I), Immature Platelet Fraction (IPF%) and absolute immature platelet count (AIPC), were analyzed in eighty-two sepsis patients daily over the first 5 days stay on ICU. C-Reactive Protein (CRP), procalcitonin (PCT), and lactate were also analyzed daily. Blood cultures confirmed or excluded the presence of bacteremia. PCT provided the earliest indicator of bacteremia, with significant differences between the two cohorts on day 1. The change in IPF% and AIPC from day 1 to day 2 (Δ IPF% and Δ AIPC) provided the most accurate indication; A combination of Δ IPF% and day 2 PCT, provided a positive predictive value and negative predictive value of 100% and 96.10%, respectively. These data provide strong justification for larger multi-center validation studies to confirm the usefulness of these platelet indices during the assessment of sepsis on the ICU.

Clinical Utilization of Blood and Urine Cultures and Incidences of Bacteremia and Bacteriuria in a Hospital in Thailand

Background

To effectively treat sepsis and urinary tract infection (UTI), blood and urine cultures should be used appropriately and relative to incidences of bacteremia and bacteriuria. This study aimed to investigate the use of blood and urine cultures and incidences of bacteremia and bacteriuria in a hospital in Thailand.

Material/Methods

Medical records of patients admitted from 2016 to 2018 were randomly selected and data in the records were anonymously extracted for investigation.

Results

From 12 000 records, data on blood and urine cultures were extracted from 9% and 4% of them, respectively. The negative rate of blood culture was 87.48%. Bacteremia was detected in 10.22%. The positive rate of urine culture was 27.38% and the contamination rate was 31.26%. Escherichia coli was the most common cause of community-onset bacteremia and bacteriuria. Methicillin-resistant coagulase-negative staphylococci and Acinetobacter baumannii were the most common cause of hospital-acquired bacteremia while yeasts were the most common cause of hospital-acquired UTI.

Conclusions

A high negative rate of blood culture may result not only from its low sensitivity but also from liberal test use to identify sepsis in some conditions. Improper urine collection is the main problem with use of urine culture.

Factors associated with sepsis risk in immune-mediated inflammatory diseases receiving tumor necrosis factor inhibitors: a nationwide study

Background:

Risk factors for sepsis have not been assessed in patients receiving tumor necrosis factor-alpha inhibitors (TNFi) for immune-mediated inflammatory diseases (IMIDs) who are vulnerable to serious/hospitalized infections.

Methods:

Data from 2003–2017 were obtained from Taiwan’s National Health Insurance Research Database to identify patients receiving TNFi, including etanercept, adalimumab, and golimumab, for IMIDs including rheumatoid arthritis (RA), ankylosing spondylitis (AS), psoriasis (PsO), psoriatic arthritis (PsA), Crohn’s disease (CD), and ulcerative colitis (UC). To investigate risk factors for sepsis, we used the Sepsis-3 definition and calculated hazard ratios (HRs) with 95% confidence intervals (CIs) using Cox regression analysis.

Results:

There were 17,764 patients (mean age 49.3 ± 14.3 years; females, 57.6%) receiving TNFi for IMIDs, including RA (58.6%), AS (19.1%), PsO (15.1%), PsA (2.5%), CD (3.0%), and UC (1.7%). The overall incidence rate of sepsis was 1088 per 100,000 person-years. After adjustment for potential confounders, recent sepsis within 3 months before TNFi initiation (HR, 2.35; 95% CI, 1.73–3.20), CD (HR, 3.36; 95% CI 2.11–5.34; reference group: AS) and glucocorticoid use (prednisolone-equivalent dose, mg/day HR, 1.05; 95% CI, 1.05–1.06) were associated with the risk of sepsis. Intriguingly, golimumab users appeared to have a lower risk of sepsis compared with etanercept users (HR, 0.56; 95% CI, 0.38–0.83). In addition, socioeconomic status, including urbanization level and insured amount, was associated with sepsis in a dose-response manner.

Conclusions:

Recent sepsis, CD, concomitant glucocorticoid use, and low socioeconomic status, which were associated with an increased risk of sepsis, are crucial for individualized risk management plans.