Identifying immune signatures of sepsis to increase diagnostic accuracy in very preterm babies

Management of neonates at risk of early onset sepsis: a probability-based approach and recent literature appraisal : Update of the Swiss national guideline of the Swiss Society of Neonatology and the Pediatric Infectious Disease Group Switzerland

In Switzerland and other high-income countries, one out of 3000 to 5000 term and late preterm neonates develops early onset sepsis (EOS) associated with a mortality of around 3%, while incidence and mortality of EOS in very preterm infants are substantially higher. Exposure to antibiotics for suspected EOS is disproportionally high compared to the incidence of EOS with consequences for future health and antimicrobial resistance (AMR). A safe reduction of unnecessary antibiotic treatment has to be a major goal of new management strategies and guidelines. Antibiotics should be administered immediately in situations with clinical signs of septic shock. Group B streptococcus (GBS) and Escherichia coli (E. coli) are the leading pathogens of EOS. Amoxicillin combined with an aminoglycoside remains the first choice for empirical treatment. Serial physical examinations are recommended for all neonates with risk factors for EOS. Neonates without any clinical signs suggestive of EOS should not be treated with antibiotics. In Switzerland, we do not recommend the use of the EOS calculator, a risk stratification tool, due to its unclear impact in a population with an observed antibiotic exposure below 3%. Not all neonates with respiratory distress should be empirically treated with antibiotics. Isolated tachypnea or respiratory distress starting immediately after delivery by elective caesarean section or a clearly assessed alternative explanation than EOS for clinical signs may point towards a low probability of sepsis. On the other hand, unexplained prematurity with risk factors has an inherent higher risk of EOS. Before the start of antibiotic therapy, blood cultures should be drawn with a minimum volume of 1 ml in a single aerobic blood culture bottle. This standard procedure allows antibiotics to be stopped after 24 to 36 h if no pathogen is detected in blood cultures. Current data do not support the use of PCR-based pathogen detection in blood as a standard method. Lumbar puncture is recommended in blood culture-proven EOS, critical illness, or in the presence of neurological symptoms such as seizures or altered consciousness. The accuracy of a single biomarker measurement to distinguish inflammation from infection is low in neonates. Therefore, biomarker guidance is not a standard part of decision-making regarding the start or stop of antibiotic therapy but may be used as part of an algorithm and after appropriate education of health care teams. Every newborn started on antibiotics should be assessed for organ dysfunction with prompt initiation of respiratory and hemodynamic support if needed. An elevated lactate may be a sign of poor perfusion and requires a comprehensive assessment of the clinical condition. Interventions to restore perfusion include fluid boli with crystalloids and catecholamines. Neonates in critical condition should be cared for in a specialized unit. In situations with a low probability of EOS, antibiotics should be stopped as early as possible within the first 24 h after the start of therapy. In cases with microbiologically proven EOS, reassessment and streamlining of antibiotic therapy in neonates is an important step to minimize AMR.

CONCLUSION

 This guideline, developed through a critical review of the literature, facilitates a probability-based approach to the management of neonates at risk of early onset sepsis.

WHAT IS KNOWN

• Neonatal exposure to antibiotics is disproportionally high compared with the incidence of early onset sepsis with implications for future health and antimicrobial resistance.

WHAT IS NEW

• A probability-based approach may facilitate a more balanced management of neonatal sepsis and antibiotic stewardship.

Decreased monocytic HLA-DR in patients with sepsis: Prediction of diagnosis, severity and prognosis

OBJECTIVE

Sepsis is characterized by high incidence and mortality rates, making early recognition and risk stratification critical for preventing delayed treatment and overtreatment. This study investigated the potential of monocytic (m) HLA-DR as a diagnostic and prognostic biomarker of sepsis.

METHODS

In this prospective study, we collected blood in EDTA-anticoagulated tubes within 48 h from patients diagnosed with sepsis or infection and analyzed the percentage of mHLA-DR in peripheral blood mononuclear cells, C-reactive protein, and procalcitonin within 2 h of collection. We gathered clinical and laboratory data, including sex, age, and comorbidities, calculated the number of dysfunctional organs and sequential organ failure assessment (SOFA) score, and recorded the survival status of patients with sepsis on the 30th day after admission.

RESULTS

mHLA-DR levels were lower in patients with sepsis (median 46.60 [interquartile range 23.86-66.51]%) than infection (75.44 [52.13-91.50]%). mHLA-DR could distinguish sepsis from infection with an area under the curve (AUC) of 0.724 (95 %CI 0.624-0.824). Decreased mHLA-DR levels have been found in septic patients with shock or secondary infections. mHLA-DR expression decreased with an increasing number of dysfunctional organs and higher SOFA score. In 30-day non-survivors, mHLA-DR levels were 26.94 (12.06-44.45)%, significantly lower than in survivors (55.20 [24.83-72.37]%). mHLA-DR predicted sepsis prognosis with an AUC of 0.750 (95 %CI 0.623-0.877). When the cut-off value was <52.29 %, the sensitivity and specificity of mHLA-DR for prognosis were 100 % and 52.83 %, respectively. The 30-day survival rate of septic patients with mHLA-DR ≥ 52.29 % was 6.798 (95 %CI 2.075-22.27) times higher than that of patients with mHLA-DR < 52.29 %.

CONCLUSION

mHLA-DR negatively correlates with the severity of sepsis and could be used as a diagnostic and prognostic biomarker for sepsis.

The Immunomodulatory Role of Regulatory T Cells in Preterm Birth and Associated Pregnancy Outcomes

Spontaneous preterm birth (sPTB), defined as live birth before 37 weeks of gestational age, is associated with immune dysregulation and pro-inflammatory conditions that profoundly impact newborn health. The question of immune integrity at the maternal-foetal interface is a focus of recent studies centring not only sPTB but the conditions often affiliated with this outcome. Regulatory T cells (Tregs) play a critical anti-inflammatory role in pregnancy, promoting foetal tolerance and placentation. Due to this gestational role, it is hypothesised that decreased or dysfunctional Tregs may be implicated in cases of sPTB. This review examines studies comparing Treg presence in healthy term pregnancies and those with sPTB-associated conditions. Conflicting findings across different conditions and within sPTB itself have been identified. However, notable findings from the research indicate increased proinflammatory cytokines in pregnancies suffering from premature rupture of membranes (pPROM), chorioamnionitis, infection, preeclampsia, and gestational diabetes (GDM). Additionally, reduced Treg levels were identified in preeclampsia, GDM, and pPROM as well as chorioamnionitis presenting with increased Treg dysfunctionality. Treg deficiencies may contribute to health issues in preterm newborns. Current sPTB treatments are limited, underscoring the potential of in utero therapies targeting inflammation, including T cell interventions. Future research aims to establish consensus on the role of Tregs in sPTB and associated conditions and advancing understanding of mechanisms leading to Treg deficiencies in adverse pregnancy outcomes.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Sepsis is characterized by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility of identifying integrative patterns from clinical parameters, plasma biomarkers, and extensive phenotyping of blood immune cells. While no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90-day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90-day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T-cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical, and clinical parameters.

Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics

Bronchopulmonary dysplasia (BPD), a chronic lung disease predominantly affecting premature infants, poses substantial clinical challenges. This review delves into the promise of biomedical informatics (BMI) in reshaping BPD research and care. We commence by highlighting the escalating prevalence and healthcare impact of BPD, emphasizing the necessity for innovative strategies to comprehend its intricate nature. To this end, we introduce BMI as a potent toolset adept at managing and analyzing extensive, diverse biomedical data. The challenges intrinsic to BPD research are addressed, underscoring the inadequacies of conventional approaches and the compelling need for data-driven solutions. We subsequently explore how BMI can revolutionize BPD research, encompassing genomics and personalized medicine to reveal potential biomarkers and individualized treatment strategies. Predictive analytics emerges as a pivotal facet of BMI, enabling early diagnosis and risk assessment for timely interventions. Moreover, we examine how mobile health technologies facilitate real-time monitoring and enhance patient engagement, ultimately refining BPD management. Ethical and legal considerations surrounding BMI implementation in BPD research are discussed, accentuating issues of privacy, data security, and informed consent. In summation, this review highlights BMI's transformative potential in advancing BPD research, addressing challenges, and opening avenues for personalized medicine and predictive analytics.