Obstetric infections and clinical characteristics of maternal sepsis: a hospital-based retrospective cohort study.

Sepsis is responsible for 50% of intrahospital maternal deaths worldwide. Incidence is increasing in both low and middle-, and high-income countries. There is little data on incidence and clinical outcomes of obstetric infections including maternal sepsis in the Nordic countries. The aims of this study are to give estimates of the occurrence of obstetric infections and maternal sepsis in a Norwegian hospital cohort, assess the quality of management of maternal sepsis cases, and evaluate the usefulness of diagnostic codes to identify maternal sepsis retrospectively. We conducted a retrospective cohort study of pregnant, labouring, post-abortion, and postpartum women. We assessed the accuracy of the diagnostic code most frequently applied for maternal sepsis, O85. We found 7.8% (95% confidence interval 7.1-8.5) infection amongst pregnant, labouring, and postpartum women. The incidence of maternal sepsis was 0.3% (95% confidence interval 0.2-0.5), and the majority of sepsis cases were recorded in the postpartum period. Two thirds of women were given broad-spectrum antibiotics at the time sepsis was diagnosed, but only 15.4% of women with puerperal sepsis were given antimicrobials in accordance with national guidelines. When used retrospectively, obstetric infection codes are insufficient in identifying both maternal and puerperal sepsis, with only 20.3% positive predictive value for both conditions. In conclusion, obstetric infections contribute significantly to maternal morbidity in Norway's second largest maternity hospital. This study provides incidences of maternal infections for hospitalised patients in temporal relation to pregnancy, labour, abortion and the postpartum period, knowledge which is valuable for planning of health care services and allocation of resources. In addition, the study highlights areas where improvement is needed in clinical handling of maternal sepsis. There is need for studies on the management quality and use of correct diagnostic codes in this patient category.

Necrotising soft tissue infections. / Nekrotiserende bløtdelsinfeksjoner.

Necrotising soft tissue infections can affect the skin, subcutaneous tissue, superficial fascia, deep fascia and musculature. The infections are severe, they spread quickly and can result in extensive tissue loss. Although rare, morbidity and mortality rates are high. Early clinical identification is crucial for the outcome, and rapid infection control through surgery and targeted antibiotic treatment is needed to save lives. Few prospective clinical trials have been conducted for the treatment of this type of infection. Specific challenges include rapid identification of the condition and the uncertain efficacy of the various treatment options. In this clinical review article, we describe clinical characteristics, diagnostics and treatment.

Genomic epidemiology of Streptococcus dysgalactiae subsp. equisimilis strains causing invasive disease in Norway during 2018.

Background: Streptococcus dysgalactiae subspecies equisimilis (SDSE) is an emerging global pathogen, yet the epidemiology and population genetics of SDSE species have not been extensively characterized. Methods: We carried out whole genome sequencing to characterize 274 SDSE isolates causing bloodstream infections obtained through national surveillance program in 2018. We conducted multilocus sequence typing (MLST), emm-typing, core genome phylogeny, as well as investigated key features associated with virulence. Moreover, comparison to SDSE from other geographic regions were performed in order to gain more insight in the evolutionary dynamics in SDSE. Results: The phylogenetic analysis indicated a substantial diversity of emm-types and sequence types (STs). Briefly, 17 emm-types and 58 STs were identified that formed 10 clonal complexes (CCs). The predominant ST-types were ST20 (20%), ST17 (17%), and ST29 (11%). While CC17 and CC29 clades showed a substantial heterogeneity with well-separated emm-associated subclades, the CC20 clade harboring the stG62647 emm-type was more homogenous and the most prevalent in the present study. Moreover, we observed notable differences in the distribution of clades within Norway, as well as several disseminated CCs and also distinct geographic variations when compared to data from other countries. We also revealed extensive intra-species recombination events involving surface exposed virulence factors, including the emm gene important for phylogenetic profiling. Conclusion: Recombination events involving the emm as well as other virulence genes in SDSE, are important mechanisms in shaping the genetic variability in the SDSE population, potentially offering selective advantages to certain lineages. The enhanced phylogenetic resolution offered by whole genome sequencing is necessary to identify and delimitate outbreaks, monitor and properly characterize emerging strains, as well as elucidate bacterial population dynamics.

Interplay between human STING genotype and bacterial NADase activity regulates inter-individual disease variability.

Variability in disease severity caused by a microbial pathogen is impacted by each infection representing a unique combination of host and pathogen genomes. Here, we show that the outcome of invasive Streptococcus pyogenes infection is regulated by an interplay between human STING genotype and bacterial NADase activity. S. pyogenes-derived c-di-AMP diffuses via streptolysin O pores into macrophages where it activates STING and the ensuing type I IFN response. However, the enzymatic activity of the NADase variants expressed by invasive strains suppresses STING-mediated type I IFN production. Analysis of patients with necrotizing S. pyogenes soft tissue infection indicates that a STING genotype associated with reduced c-di-AMP-binding capacity combined with high bacterial NADase activity promotes a 'perfect storm' manifested in poor outcome, whereas proficient and uninhibited STING-mediated type I IFN production correlates with protection against host-detrimental inflammation. These results reveal an immune-regulating function for bacterial NADase and provide insight regarding the host-pathogen genotype interplay underlying invasive infection and interindividual disease variability.

Neutrophil-derived reactive agents induce a transient SpeB negative phenotype in Streptococcus pyogenes.

BACKGROUND: Streptococcus pyogenes (group A streptococci; GAS) is the main causative pathogen of monomicrobial necrotizing soft tissue infections (NSTIs). To resist immuno-clearance, GAS adapt their genetic information and/or phenotype to the surrounding environment. Hyper-virulent streptococcal pyrogenic exotoxin B (SpeB) negative variants caused by covRS mutations are enriched during infection. A key driving force for this process is the bacterial Sda1 DNase. METHODS: Bacterial infiltration, immune cell influx, tissue necrosis and inflammation in patient´s biopsies were determined using immunohistochemistry. SpeB secretion and activity by GAS post infections or challenges with reactive agents were determined via Western blot or casein agar and proteolytic activity assays, respectively. Proteome of GAS single colonies and neutrophil secretome were profiled, using mass spectrometry. RESULTS: Here, we identify another strategy resulting in SpeB-negative variants, namely reversible abrogation of SpeB secretion triggered by neutrophil effector molecules. Analysis of NSTI patient tissue biopsies revealed that tissue inflammation, neutrophil influx, and degranulation positively correlate with increasing frequency of SpeB-negative GAS clones. Using single colony proteomics, we show that GAS isolated directly from tissue express but do not secrete SpeB. Once the tissue pressure is lifted, GAS regain SpeB secreting function. Neutrophils were identified as the main immune cells responsible for the observed phenotype. Subsequent analyses identified hydrogen peroxide and hypochlorous acid as reactive agents driving this phenotypic GAS adaptation to the tissue environment. SpeB-negative GAS show improved survival within neutrophils and induce increased degranulation. CONCLUSIONS: Our findings provide new information about GAS fitness and heterogeneity in the soft tissue milieu and provide new potential targets for therapeutic intervention in NSTIs.

Systemic immune activation profiles in streptococcal necrotizing soft tissue infections: A prospective multicenter study.

OBJECTIVE: Early stages with streptococcal necrotizing soft tissue infections (NSTIs) are often difficult to discern from cellulitis. Increased insight into inflammatory responses in streptococcal disease may guide correct interventions and discovery of novel diagnostic targets. METHODS: Plasma levels of 37 mediators, leucocytes and CRP from 102 patients with ß-hemolytic streptococcal NSTI derived from a prospective Scandinavian multicentre study were compared to those of 23 cases of streptococcal cellulitis. Hierarchical cluster analyses were also performed. RESULTS: Differences in mediator levels between NSTI and cellulitis cases were revealed, in particular for IL-1ß, TNFα and CXCL8 (AUC >0.90). Across streptococcal NSTI etiologies, eight biomarkers separated cases with septic shock from those without, and four mediators predicted a severe outcome. CONCLUSION: Several inflammatory mediators and wider profiles were identified as potential biomarkers of NSTI. Associations of biomarker levels to type of infection and outcomes may be utilized to improve patient care and outcomes.

Streptococcus dysgalactiae Bloodstream Infections, Norway, 1999-2021.

Streptococcus dysgalactiae increasingly is recognized as a pathogen of concern for human health. However, longitudinal surveillance data describing temporal trends of S. dysgalactiae are scarce. We retrospectively identified all ß-hemolytic streptococcal bloodstream infections reported in Bergen, in western Norway, during 1999-2021. To explore S. dysgalactiae disease burden in a broader context, we mapped the incidence of all microbial species causing bloodstream infections during 2012-2021. We found S. dysgalactiae incidence rates substantially increased during the study period; by 2021, S. dysgalactiae was the fifth most common pathogen causing bloodstream infections in our region. We noted genotypic shifts and found that the rising trend was related in part to the introduction and expansion of the stG62647 emm-type. S. dysgalactiae is among the most common causes of bloodstream infections in western Norway, and increased surveillance and unambiguous species identification are needed to monitor the disease burden attributable to this pathogen.

Scoring systems for early detection of sepsis on the ward. / Skåringsverktøy for tidlig oppdagelse av sepsis på sengepost.

BACKGROUND: Sepsis is one of the most common causes of death in Norwegian hospitals. The condition is often overlooked with treatment being delayed. Scoring systems to detect patients with sepsis have mostly been studied in emergency departments and more rarely on wards. The objective of this study was to investigate the ability of various clinical scoring systems to identify sepsis in patients on wards. MATERIAL AND METHOD: The ability of the scoring systems SIRS (Systemic Inflammatory Response Syndrome), qSOFA (quick Sequential Organ Failure Assessment) and NEWS2 (National Early Warning Score 2) to identify patients with sepsis was compared in a study of adult patients with clinically suspected severe infection on wards at Haukeland University Hospital in the period March-December 2019. The diagnosis of sepsis was made based on an increase in SOFA (Sequential Organ Failure Assessment) score of 2 or more in the first 24 hours after inclusion. RESULTS: 89 patients with suspected new onset of severe infection were identified. Sepsis was diagnosed in 55 of these patients. SIRS had a sensitivity of 82 % and a specificity of 6 % in diagnosing sepsis, qSOFA had a sensitivity of 20 % and a specificity of 97 %, while NEWS2 with a threshold of an aggregate score ≥ 5 and/or 3 in a single parameter had a sensitivity of 96 % and a specificity of 59 %. Oxygen saturation with a threshold of 95 % was the most sensitive measurement for sepsis. INTERPRETATION: NEWS2 was better suited than qSOFA and SIRS for early detection of sepsis on wards.

Clinical Characteristics and Histopathology in Suspected Necrotizing Soft Tissue Infections.

Background: Necrotizing soft tissue infections (NSTIs) are severe diseases with high morbidity and mortality. The diagnosis is challenging. Several guidelines recommend tissue biopsies as an adjunct diagnostic in routine management, but neither biopsy sampling nor classification is standardized or validated. We studied the quality of tissue biopsy examination as part of routine diagnostics in NSTIs. Methods: This was a retrospective cohort study of adult patients undergoing surgery due to suspected NSTIs in which tissue biopsy was taken as part of routine management. Clinical data were reviewed. The biopsies were evaluated according to a proposed histopathologic classification system and independently assessed by 2 pathologists. Interrater reliability and diagnostic accuracy were determined. Results: Tissue biopsies from 75 patients were examined, 55 NSTIs and 20 non-NSTIs cases. The cohorts were similar in clinical characteristics. Interrater reliability for histopathologic staging was moderate (0.53) and fair (0.37) for diagnosis. The sensitivity of histologic diagnosis was 75% and the specificity 80%. The positive predictive value was 91% and the negative predictive value 53%. Necrotizing Infection Clinical Composite Endpoint (NICCE) success was associated with a more severe histological stage, achieved by 42% and 71% of the cases in stage 1 and 2, respectively (P = .046). Conclusions: Our findings suggest that tissue biopsies have low clinical accuracy. The interrater reliability among experienced pathologists is only fair to moderate. A histopathologically more severe stage was associated with favorable outcome. These findings discourage the use of histopathologic evaluation as part of contemporary management of patients with suspected NSTI.

Decision support system and outcome prediction in a cohort of patients with necrotizing soft-tissue infections.

INTRODUCTION: Necrotizing Soft Tissue Infections (NSTI) are severe infections with high mortality affecting a heterogeneous patient population. There is a need for a clinical decision support system which predicts outcomes and provides treatment recommendations early in the disease course. METHODS: To identify relevant clinical needs, interviews with eight medical professionals (surgeons, intensivists, general practitioner, emergency department physician) were conducted. This resulted in 24 unique questions. Mortality was selected as first endpoint to develop a machine learning (Random Forest) based prediction model. For this purpose, data from the prospective, international INFECT cohort (N = 409) was used. RESULTS: Applying a feature selection procedure based on an unsupervised algorithm (Boruta) to the  > 1000 variables available in INFECT, including baseline, and both NSTI specific and NSTI non-specific clinical data yielded sixteen predictive parameters available on or prior to the first day on the intensive care unit (ICU). Using these sixteen variables 30-day mortality could be accurately predicted (AUC = 0.91, 95% CI 0.88-0.96). Except for age, all variables were related to sepsis (e.g. lactate, urine production, systole). No NSTI-specific variables were identified. Predictions significantly outperformed the SOFA score(p < 0.001, AUC = 0.77, 95% CI 0.69-0.84) and exceeded but did not significantly differ from the SAPS II score (p = 0.07, AUC = 0.88, 95% CI 0.83-0.92). The developed model proved to be stable with AUC  > 0.8 in case of high rates of missing data (50% missing) or when only using very early (<1 h) available variables. CONCLUSIONS: This study shows that mortality can be accurately predicted using a machine learning model. It lays the foundation for a more extensive, multi-endpoint clinical decision support system in which ultimately other outcomes and clinical questions (risk for septic shock, AKI, causative microbe) will be included.

Analysis of host-pathogen gene association networks reveals patient-specific response to streptococcal and polymicrobial necrotising soft tissue infections.

BACKGROUND: Necrotising soft tissue infections (NSTIs) are rapidly progressing bacterial infections usually caused by either several pathogens in unison (polymicrobial infections) or Streptococcus pyogenes (mono-microbial infection). These infections are rare and are associated with high mortality rates. However, the underlying pathogenic mechanisms in this heterogeneous group remain elusive. METHODS: In this study, we built interactomes at both the population and individual levels consisting of host-pathogen interactions inferred from dual RNA-Seq gene transcriptomic profiles of the biopsies from NSTI patients. RESULTS: NSTI type-specific responses in the host were uncovered. The S. pyogenes mono-microbial subnetwork was enriched with host genes annotated with involved in cytokine production and regulation of response to stress. The polymicrobial network consisted of several significant associations between different species (S. pyogenes, Porphyromonas asaccharolytica and Escherichia coli) and host genes. The host genes associated with S. pyogenes in this subnetwork were characterised by cellular response to cytokines. We further found several virulence factors including hyaluronan synthase, Sic1, Isp, SagF, SagG, ScfAB-operon, Fba and genes upstream and downstream of EndoS along with bacterial housekeeping genes interacting with the human stress and immune response in various subnetworks between host and pathogen. CONCLUSIONS: At the population level, we found aetiology-dependent responses showing the potential modes of entry and immune evasion strategies employed by S. pyogenes, congruent with general cellular processes such as differentiation and proliferation. After stratifying the patients based on the subject-specific networks to study the patient-specific response, we observed different patient groups with different collagens, cytoskeleton and actin monomers in association with virulence factors, immunogenic proteins and housekeeping genes which we utilised to postulate differing modes of entry and immune evasion for different bacteria in relationship to the patients' phenotype.

Consistent Biofilm Formation by Streptococcus pyogenes emm 1 Isolated From Patients With Necrotizing Soft Tissue Infections.

OBJECTIVES: Biofilm formation has been demonstrated in muscle and soft tissue samples from patients with necrotizing soft tissue infection (NSTI) caused by Streptococcus pyogenes, but the clinical importance of this observation is not clear. Although M-protein has been shown to be important for in vitro biofilm formation in S. pyogenes, the evidence for an association between emm type and biofilm forming capacity is conflicting. Here we characterize the biofilm forming capacity in a collection of S. pyogenes isolates causing NSTI, and relate this to emm type of the isolates and clinical characteristics of the patients. METHODS: Bacterial isolates and clinical data were obtained from NSTI patients enrolled in a multicenter prospective observational study. Biofilm forming capacity was determined using a microtiter plate assay. RESULTS: Among 57 cases, the three most frequently encountered emm types were emm1 (n = 22), emm3 (n = 13), and emm28 (n = 7). The distribution of biofilm forming capacity in emm1 was qualitatively (narrow-ranged normal distribution) and quantitatively (21/22 isolates in the intermediate range) different from other emm types (wide ranged, multimodal distribution with 5/35 isolates in the same range as emm1). There were no significant associations between biofilm forming capacity and clinical characteristics of the patients. CONCLUSIONS: The biofilm forming capacity of emm1 isolates was uniform and differed significantly from other emm types. The impact of biofilm formation in NSTI caused by S. pyogenes on clinical outcomes remains uncertain.

Discriminatory plasma biomarkers predict specific clinical phenotypes of necrotizing soft-tissue infections.

BACKGROUNDNecrotizing soft-tissue infections (NSTIs) are rapidly progressing infections frequently complicated by septic shock and associated with high mortality. Early diagnosis is critical for patient outcome, but challenging due to vague initial symptoms. Here, we identified predictive biomarkers for NSTI clinical phenotypes and outcomes using a prospective multicenter NSTI patient cohort.METHODSLuminex multiplex assays were used to assess 36 soluble factors in plasma from NSTI patients with positive microbiological cultures (n = 251 and n = 60 in the discovery and validation cohorts, respectively). Control groups for comparative analyses included surgical controls (n = 20), non-NSTI controls (i.e., suspected NSTI with no necrosis detected upon exploratory surgery, n = 20), and sepsis patients (n = 24).RESULTSThrombomodulin was identified as a unique biomarker for detection of NSTI (AUC, 0.95). A distinct profile discriminating mono- (type II) versus polymicrobial (type I) NSTI types was identified based on differential expression of IL-2, IL-10, IL-22, CXCL10, Fas-ligand, and MMP9 (AUC >0.7). While each NSTI type displayed a distinct array of biomarkers predicting septic shock, granulocyte CSF (G-CSF), S100A8, and IL-6 were shared by both types (AUC >0.78). Finally, differential connectivity analysis revealed distinctive networks associated with specific clinical phenotypes.CONCLUSIONSThis study identifies predictive biomarkers for NSTI clinical phenotypes of potential value for diagnostic, prognostic, and therapeutic approaches in NSTIs.TRIAL REGISTRATIONClinicalTrials.gov NCT01790698.FUNDINGCenter for Innovative Medicine (CIMED); Region Stockholm; Swedish Research Council; European Union; Vinnova; Innovation Fund Denmark; Research Council of Norway; Netherlands Organisation for Health Research and Development; DLR Federal Ministry of Education and Research; and Swedish Children's Cancer Foundation.

Risk Factors and Predictors of Mortality in Streptococcal Necrotizing Soft-tissue Infections: A Multicenter Prospective Study.

BACKGROUND: Necrotizing soft-tissue infections (NSTI) are life-threatening conditions often caused by ß-hemolytic streptococci, group A Streptococcus (GAS) in particular. Optimal treatment is contentious. The INFECT cohort includes the largest set of prospectively enrolled streptococcal NSTI cases to date. METHODS: From the INFECT cohort of 409 adults admitted with NSTI to 5 clinical centers in Scandinavia, patients culture-positive for GAS or Streptococcus dysgalactiae (SD) were selected. Risk factors were identified by comparison with a cohort of nonnecrotizing streptococcal cellulitis. The impact of baseline factors and treatment on 90-day mortality was explored using Lasso regression. Whole-genome sequencing of bacterial isolates was used for emm typing and virulence gene profiling. RESULTS: The 126 GAS NSTI cases and 27 cases caused by SD constituted 31% and 7% of the whole NSTI cohort, respectively. When comparing to nonnecrotizing streptococcal cellulitis, streptococcal NSTI was associated to blunt trauma, absence of preexisting skin lesions, and a lower body mass index. Septic shock was significantly more frequent in GAS (65%) compared to SD (41%) and polymicrobial, nonstreptococcal NSTI (46%). Age, male sex, septic shock, and no administration of intravenous immunoglobulin (IVIG) were among factors associated with 90-day mortality. Predominant emm types were emm1, emm3, and emm28 in GAS and stG62647 in SD. CONCLUSIONS: Streptococcal NSTI was associated with several risk factors, including blunt trauma. Septic shock was more frequent in NSTI caused by GAS than in cases due to SD. Factors associated with mortality in GAS NSTI included age, septic shock, and no administration of IVIG.

Necrotizing Soft Tissue Infections: Case Reports, from the Clinician's Perspectives.

Necrotizing soft tissue infections (NSTI) are rapidly spreading and life-threatening infections of skin and soft tissue. Essentially there are two types of NSTI, based on the invasive microorganisms. The speed of development and associated clinical features differ markedly depending on the bacterial etiology. Early recognition, extensive surgical debridement, and appropriate antimicrobials are pivotal for successful management. In this chapter, we present three cases from the INFECT-study population. This study was an international, multicenter, prospective cohort study of adult patients with NSTI. We describe the clinical presentations, pre-, peri-, and postoperative clinical findings, microbiology, and treatment in cases of monobacillary Streptococcus pyogenes necrotizing soft tissue infections NSTI, polymicrobial infection, and an unusual presentation of pelvic monobacillary S. pyogenes infection in an immunocompromised patient.

Beta-Hemolytic Streptococci and Necrotizing Soft Tissue Infections.

ß-hemolytic streptococci are major causes of necrotizing soft tissue infections (NSTIs), Streptococcus pyogenes (group A streptococcus; GAS) in particular. NSTIs caused by Streptococcus dysgalactiae (SD) have also been reported. In the INFECT cohort of 409 NSTIs patients, more than a third of the cases were caused by GAS (31%) or SD (7%). Risk factors of streptococcal NSTIs compared to streptococcal cellulitis have previously been largely unknown. The INFECT study confirmed blunt trauma as an important risk factor. In addition, absence of pre-existing skin lesions and a lower BMI were associated with NSTIs. The study also confirmed that septic shock is more frequent in GAS cases than in other types of NSTIs. Septic shock was also among several predictors of mortality. The role of intravenous immunoglobulin (IVIG) in streptococcal NSTIs has been unclear. In the INFECT cohort, IVIG treatment was associated with increased survival. As in other studies, a significant microbial diversity was observed, but with predominance of a few emm types. Overall, the INFECT study gives a comprehensive and contemporary picture of the clinical characteristics and the microbes involved in streptococcal NSTIs. The reported severity of disease underscores the need for new efforts aimed at identifying novel diagnostic measures and improved treatment.

Microbiological Etiology of Necrotizing Soft Tissue Infections.

Necrotizing soft tissue infections (NSTIs) are severe clinical conditions requiring swift therapeutic intervention, including surgical removal of infected tissue and administration of potent antibiotics. There is wide diversity in the microbial etiologic agents, and tailoring the antibiotic treatment to the offending pathogen is essential. However, the choice of empirical therapy is frequently inadequate, underlining the need for comprehensive and contemporary knowledge on causative pathogens and relevant antimicrobial resistance patterns in NSTIs. Also, studies of the pathogenic mechanisms in different NSTIs are needed, to improve handling of patients through developing patient stratification and tailored therapies. We review the current knowledge on microbial etiology and provide detailed characterizations of the predominant pathogens.

Systems and Precision Medicine in Necrotizing Soft Tissue Infections.

Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.

Emerging Threat of Antimicrobial Resistance in ß-Hemolytic Streptococci.

Highly variable resistance rates to erythromycin and clindamycin have been reported in the ß-hemolytic streptococcal species Streptococcus pyogenes, Streptococcus agalactiae, and Streptococcus dysgalactiae, depending on geographic and temporal context. In the present study we aimed to examine the longitudinal trends of antimicrobial resistance in these three species in a northern European setting. Furthermore, we used whole genome sequencing to identify resistance determinants and the mobile genetic elements involved in their dissemination, as well as elucidate phylogenetic relationships. All cases of invasive ß-hemolytic streptococcal diseases in Health Region Bergen, western Norway, in the period 2004 to 2018 were retrospectively identified, comprising 271, 358, and 280 cases of S. pyogenes, S. agalactiae, and S. dysgalactiae, respectively. Antimicrobial susceptibility testing revealed a gradual but significant increase in erythromycin and clindamycin resistance for S. agalactiae and S. dysgalactiae during the study period. Whole genome sequencing of the erythromycin and clindamycin resistant bacterial population revealed a substantial phylogenetic diversity in S. agalactiae and S. dysgalactiae. However, the mobile genetic elements harboring the resistance determinants showed remarkable intra- and interspecies similarities, suggesting a dissemination of antimicrobial resistance predominantly through conjugative transfer rather than clonal expansion of resistant strains in these two species. Conversely, antimicrobial resistance in S. pyogenes remained low, apart from a transient outbreak of a clindamycin and erythromycin resistant emm11/ST403-clone in 2010-2012. Increased epidemiological attentiveness is warranted to monitor the emerging threat of antimicrobial resistance in ß-hemolytic streptococci, particularly in S. agalactiae and S. dysgalactiae.