Insight Into Neonatal Sepsis: An Overview

Bacterial profile, antibiotic susceptibility patterns and associated factors among neonates suspected of omphalitis in Arba Minch, Southern Ethiopia

Neonatal omphalitis is a true medical emergency that can rapidly progress to sepsis and even death if not promptly diagnosed and treated appropriately. Empirical therapy is widely practised in this case, too, in low-income countries like Ethiopia, paving the way for severe drug resistance and posing serious challenges. To determine the magnitude, bacterial profile, antibiotic susceptibility patterns, and associated factors of omphalitis among clinically suspected neonates attending four government health institutions in Arba Minch, southern Ethiopia, during the second half of 2023. A facility-based multi-centred cross-sectional study was conducted among 379 clinically suspected neonates who attended the inpatient/outpatient departments and intensive care units of four government health institutes of Arba Minch from June 1 to December 28, 2023. Study participants were consecutively recruited, and their demographic and clinical data were collected using a pre-tested questionnaire. Samples were aseptically collected and inoculated onto a series of bacterial culture media, namely MacConkey agar, mannitol salt agar, chocolate, and blood agar, and different species were identified by means of several biochemical tests. Antibiotic susceptibility tests were performed through the Kirby-Bauer disc diffusion technique. Bivariable and multivariable logistic regression models (SPSS version 25) were utilized to analyze the association between dependent and independent variables; P-values ≤ 0.05 were considered statistically significant. The overall number of clinically suspected and culture-confirmed cases of omphalitis, respectively, were 379 and 130 per 1000 live births. Gram-positive (GPB) and Gram-negative bacteria (GNB) were detected in 50.4% (n = 71) and 49.6% (n = 70) of cases, respectively. The often isolated bacteria were S. aureus, 53.5% (n = 38), and E. coli, 47.1% (n = 33); GNB were highly resistant (> 60%) to tetracycline, sulfamethoxazole-trimethoprim, and ampicillin. The overall multi-drug resistance was 34.7% (n = 49); methicillin-resistant S. aureus was 34.1% (n = 14). The GNB isolates comprised extended-spectrum beta-lactamase, 15.7% (n = 11), and carbapenemase, 10% (n = 7) producers. The lack of hand washing practices [AOR = 2.08, (95% CI 1.26-3.41), P value = 0.004] and lower gestation period (< 37 weeks) [AOR = 2.3, (95% CI 1.33-3.93), P value = 0.003] were statistically associated. The overall prevalence of omphalitis was higher; WHO-prioritised drug-resistant bacterial pathogens were also detected. This study underscores the importance of factors such as maternal/caregiver hand hygiene and antenatal care. Thus, a more comprehensive approach towards the management of omphalitis employing precise diagnostic tools and an antimicrobial stewardship program is essential in all the four study settings.

Umbilical cord blood level of interleukins used as a predictor of early-onset neonatal sepsis: a comprehensive review

Neonatal sepsis (NS) is a major cause of morbidity and mortality in both preterm and term infants; early-onset NS (EONS) occurs in newborns within the first 72 h of life. Cytokines are messengers with low molecular weight that are produced by macrophages and lymphocytes in response to antigenic stimulations or products of inflammation. Different interleukins (IL) have higher values in EONS, when detected from peripheral venous blood. This review aims to analyze if the cytokines determined from the umbilical cord blood (UCB) of newborns may help in the rapid and accurate diagnosis of EONS in newborns originating from pregnancies with maternal-fetal infectious risk. Three databases, namely, PubMed, Scopus, and Web of Science, were searched for original research articles that assessed the relationship between interleukins and EONS. The search results retrieved a number of 18 articles that complied with the inclusion and exclusion criteria. Some studies report that neonates with EONS had higher umbilical plasma levels of cytokines such as IL-1ß, IL-6, IL-8, IL-10, IL-18, and IL-27. However, results are controversial, as many authors failed to establish the cut-off values of cytokines detected from UCB that may predict EONS. The main limitations of the current studies remain the small study samples, the heterogeneous population, and the lack of stratification of the studied population according to gestational age (GA). The cytokines that seem to be more accurate in the early diagnosis of EONS, as reported by the majority of the studies, are IL-6 and IL-8. The level of these cytokines may guide clinicians in the careful administration of antibiotics, thus aiding in the overall reduction of antimicrobial resistance.

Treatment outcomes and its associated factors among neonates admitted with sepsis in Hiwot Fana Comprehensive Specialized University Hospital, Harar, Ethiopia

Background

Sepsis in the neonatal period is a major health challenge in neonatal medicine because of its potential for rapid progression to multi-organ dysfunction, leading to higher morbidity and mortality. Although efforts have been made to advance the outcomes of neonates admitted to hospitals, there is a paucity of data regarding neonatal sepsis treatment outcomes in the study setting. Hence, the study aimed to assess outcomes and prognostic factors of sepsis among neonatal patients admitted to the neonatal intensive care unit in Hiwot Fana Comprehensive Specialized University Hospital in Ethiopia.

Methods

A facility-based cross-sectional study was conducted among 311 neonates with sepsis admitted from 1 January 2021 to 30 December 2023. Neonates were selected using systematic random sampling. Relevant data were extracted from medical records using a checklist. The data were entered into EpiData version 4.6 and analyzed using STATA version 17. Bivariable and multivariable logistic regression analyses were performed to identify factors associated with the outcome variable.

Results

Eighty-four of 311 patients (27.8%) (95% CI: 22.7%-32.9%) died, while 218 (72.2%) were discharged after improvement. In the multivariable logistic regression analysis, low white blood cell (WBC) count [adjusted odds ratio (AOR) = 4.24, 95% CI: 1.5-12.5], desaturation (aOR = 3.00, 95% CI: 1.6-5.5), pre-term birth (aOR = 2.14, 95% CI: 1.1-4.0), lack of maternal antenatal care (ANC) follow-up (aOR = 2.4, 95% CI: 1.2-4.7), and chorioamnionitis (aOR = 2.8, 95% CI: 1.2-6.5) were significantly associated with neonatal sepsis mortality.

Conclusion

Approximately one-quarter of patients with neonatal sepsis died. The significant prognostic factors for sepsis were found to be low WBC count, desaturation, lack of ANC visits, and chorioamnionitis. Implementing targeted therapeutic interventions and addressing these prognostic factors could improve treatment outcomes.

Antibiotic Prescription Practice and Resistance Patterns of Bacterial Isolates from a Neonatal Intensive Care Unit: A Retrospective Study from Jordan

Background/Objectives: Neonatal sepsis is a systemic inflammation in neonates caused by bacteria, viruses, or fungi that can progress into severe conditions. In developing countries, neonatal sepsis is a major cause of mortality and a major public health issue with a high prevalence. This study aims to evaluate the antibiotic prescription practice and resistance patterns of bacterial isolates from the neonatal intensive care unit (NICU) at the largest governmental hospital in Amman, Jordan. Methods: This was a retrospective cross-sectional study. The antibiotic prescription practice and resistance patterns of bacterial isolates from the NICU at Al Basheer Government Hospital in Amman, Jordan, were evaluated. The hospital's microbiology lab database and medical records were the sources of the retrospective data collection. Results: A total of 266 neonates treated with antibiotics were assessed. The findings showed that most neonates had late-onset sepsis (LOS) (65.4%). The penicillin group of antibiotics (ampicillin) was the most highly prescribed first empiric antibiotic for LOS and early-onset sepsis (EOS) (61.7%). Aminoglycosides (60.9%) were the most prescribed antibiotics as a second empiric treatment for EOS and LOS. The culture results showed that resistance to antibiotics was as follows: 15.4% of the culture samples were resistant to penicillin (Micrococcus and Viridans streptococci), 13.9% were resistant to cefotaxime (Klebsiella pneumoniae and Viridans streptococci), 13.2% were resistant to cefoxitin (Klebsiella pneumoniae and Staphylococcus epidermidis), and 12.4% were resistant to oxacillin (Klebsiella pneumoniae and Staphylococcus epidermidis). Conclusions: This retrospective study sheds light on the antibiotic prescription practice and resistance patterns of bacterial isolates from newborns with sepsis. The results highlight the high rates of antibiotic resistance. These findings underline the urgent need for improved antibiotic stewardship and infection control strategies to prevent resistance from spreading further.

Autopsy-related histomorphological findings in neonatal sepsis: a narrative review

Neonatal sepsis is an important concern in the field of neonatology, contributing significantly to morbidity and mortality among newborns worldwide. Despite progress in medical care, the accurate diagnosis and comprehension of the pathological underpinnings of neonatal sepsis continue to present challenges. Conventional diagnostic autopsy (CDA) provides unique opportunities to gain insights into the histomorphological alterations associated with neonatal sepsis. There is a paucity of literature regarding autopsy-related histomorphological features in neonatal sepsis in various organs. This narrative review aims to glean data from published literature concerning autopsy-related histomorphological findings in neonatal sepsis, which would aid in understanding organ-related pathological changes and assisting pathologists in determining the exact cause of death.

A scoping review on pediatric sepsis prediction technologies in healthcare

This scoping review evaluates recent advancements in data-driven technologies for predicting non-neonatal pediatric sepsis, including artificial intelligence, machine learning, and other methodologies. Of the 27 included studies, 23 (85%) were single-center investigations, and 16 (59%) used logistic regression. Notably, 20 (74%) studies used datasets with a low prevalence of sepsis-related outcomes, with area under the receiver operating characteristic scores ranging from 0.56 to 0.99. Prediction time points varied widely, and development characteristics, performance metrics, implementation outcomes, and considerations for human factors-especially workflow integration and clinical judgment-were inconsistently reported. The variations in endpoint definitions highlight the potential significance of the 2024 consensus criteria in future development. Future research should strengthen the involvement of clinical users to enhance the understanding and integration of human factors in designing and evaluating these technologies, ultimately aiming for safe and effective integration in pediatric healthcare.

Constructing a predictive model for early-onset sepsis in neonatal intensive care unit newborns based on SHapley Additive exPlanations explainable machine learning

Background

The clinical characteristics of neonatal sepsis (NS) are subtle and non-specific, posing a serious threat to the lives of newborn infants. Early-onset sepsis (EOS) is sepsis that occurs within 72 hours after birth, with a high mortality rate. Identifying key factors of NS and conducting early diagnosis are of great practical significance. Thus, we developed a robust machine learning (ML) model for the early prediction of EOS in neonates admitted to the neonatal intensive care unit (NICU), investigated the pivotal risk factors associated with EOS development, and provided interpretable insights into the model's predictions.

Methods

A retrospective cohort study was conducted. This includes 668 newborns (EOS and non-EOS) admitted to the NICU of Bozhou People's Hospital from January to December 2023, excluding 72 newborns born more than three days ago and 166 newborns with medical record data missing more than 30%. Finally, 430 newborns (EOS and non-EOS) were included in the study. Clinical case data were meticulously analyzed, and the dataset was randomly partitioned, allocating 75% for model training and the remaining 25% for test. Data preprocessing was meticulously performed using R language, and the least absolute shrinkage and selection operator (LASSO) regression was implemented to select salient features, mitigating the risk of overfitting. Six ML models were leveraged to forecast the incidence of EOS in neonates. The predictive performance of these models was rigorously evaluated using the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Furthermore, the SHapley Additive exPlanations (SHAP) framework was employed to provide intuitive explanations for the predictions made by the Categorical Boosting (CatBoost) model, which emerged as the top performer.

Results

The ROC area under the curve (ROCAUC) of six ML models, CatBoost, random forest (RF), eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR) all exceeded 0.900 on the test set. Especially the CatBoost model exhibited superior performance, with favorable outcomes in calibration, decision curve analysis (DCA), and learning curves. Notably, the ROCAUC attained 0.975, and the area under the PR curve (PRAUC) reached 0.947, signifying a high degree of predictive accuracy. Utilizing the SHAP method, seven key features were identified and ranked by their importance: respiratory rate (RR), procalcitonin (PCT), nasal congestion (NC), yellow staining (YS), white blood cell count (WBC), fever, and amniotic fluid turbidity (AFT).

Conclusions

By constructing a precision-oriented ML model and harnessing the SHAP method for interpretability, this study effectively identified crucial risk factors for EOS development in neonates. This approach enables early prediction of EOS risk, thereby facilitating timely and targeted clinical interventions for precise diagnosis and treatment.

Phenotypic bacterial epidemiology and antimicrobial resistance profiles in neonatal sepsis at Jimma medical center, Ethiopia: Insights from prospective study

BACKGROUND

Epidemiological profiles and the rundown crisis of antimicrobial resistance from bacterial isolates in neonatal sepsis compel regular surveillance to enhance data-driven decision-making. Accordingly, this study aimed to assess the phenotypic epidemiology and antimicrobial resistance profiles of bacteria isolated from clinically suspected neonatal sepsis in Ethiopia.

METHODS

A total of 342 neonates suspected of clinical sepsis were randomly included in a prospective observational study conducted at the neonatal intensive care unit (NICU) of Jimma medical center (JMC) from May 2022 to July 2023. Blood samples were collected from each neonate and subjected to a culture test for identification of bacterial isolates and their antibiotic resistance profiles following the standardized guidelines. The laboratory results, along with relevant clinical data, were recorded using WHONET and analyzed using STATA software.

RESULTS

Out of the 342 blood samples that were analyzed, 138 samples (40.4%, 95% CI: 35.1-45.6, P<0.01) exhibited proven bacterial infection. The infection rates were notably higher in males with 85/138 (61.6%, 95% CI: 53.4-69.8, P<0.01) and neonates aged 0-3 days with 81/138 (58.7%, 95% CI: 50.5-66.9, P<0.01). The majority of the infections were attributed to Gram-negative bacteria, accounting for 101/138(73.2%, 95% CI: 65.6-80.7) cases, with 69/101(68.3%, 95% CI: 63.8-72.8) cases involving ESBL-producing strains, while Gram-positive bacteria were responsible for 26.8% (95% CI: 19.3-34.4) of the infections. The predominant isolates included Klebsiella pneumoniae (37.7%, 95% CI: 29.6-45.8), Coagulase-negative Staphylococci (CoNs) (20.3%, 95% CI: 13.6-27.0), and Acinetobacter species (11.6%, 95% CI: 6.0-17.1). Of the total cases, 43/72 (59.7%, 95% CI: 48.4-71.1, P<0.01) resulted in mortality, with 28/72 (38.9%, 95% CI: 27.70-50.1, P<0.03) deaths linked to Extended-Spectrum Beta-Lactamase (ESBL)-producing strains. Klebsiella pneumoniae displayed high resistance rates to trimethoprim-sulfamethoxazole (100%), ceftriaxone (100%), cefotaxime (98.1%), ceftazidime (90.4%), and gentamicin (84.6%). Acinetobacter species showed resistance to ampicillin (100%), cefotaxime (100%), trimethoprim-sulfamethoxazole (75%), ceftazidime (68.8%), chloramphenicol (68.8%), and ceftriaxone (68.8%). Likewise, CoNs displayed resistance to ampicillin (100%), penicillin (100%), cefotaxime (86.0%), gentamicin (57.2%), and oxacillin (32.2%). Multidrug resistance was observed in 88.4% (95% CI: 81.8-93.0) of isolates, with ESBL-producers significantly contributing (49.3%, 95% CI: 45.1-53.5). Furthermore, 23.0% (95% CI: 15.8-31.6) exhibited a prevalent resistance pattern to seven distinct antibiotic classes.

CONCLUSION

The prevalence and mortality rates of neonatal sepsis were significantly high at JMC, with a notable surge in antibiotic and multidrug resistance among bacterial strains isolated from infected neonates, specifically ESBL-producers. These resistant strains have a significant impact on infection rates and resistance profiles, highlighting the requisite for enhanced diagnostic and antimicrobial stewardship, stringent infection control, and further molecular characterization of isolates to enhance neonatal survival.

Humanized Mouse Models of Bacterial Infections

Bacterial infections continue to represent a significant healthcare burden worldwide, causing considerable mortality and morbidity every year. The emergence of multidrug-resistant bacterial strains continues to rise, posing serious risks to controlling global disease outbreaks. To develop novel and more effective treatment and vaccination programs, there is a need for clinically relevant small animal models. Since multiple bacterial species have human-specific tropism for numerous virulence factors and toxins, conventional mouse models do not fully represent human disease. Several human disease characteristic phenotypes, such as lung granulomas in the case of Mycobacterium tuberculosis infections, are absent in standard mouse models. Alternatively, certain pathogens, such as Salmonella enterica serovar typhi and Staphylococcus aureus, can be well tolerated in mice and cleared quickly. To address this, multiple groups have developed humanized mouse models and observed enhanced susceptibility to infection and a more faithful recapitulation of human disease. In the last two decades, multiple humanized mouse models have been developed to attempt to recapitulate the human immune system in a small animal model. In this review, we first discuss the history of immunodeficient mice that has enabled the engraftment of human tissue and the engraftment methods currently used in the field. We then highlight how humanized mouse models successfully uncovered critical human immune responses to various bacterial infections, including Salmonella enterica serovar Typhi, Mycobacterium tuberculosis, and Staphylococcus aureus.

Microbiological Characteristics and Resistance Patterns in a Neonatal Intensive Care Unit: A Retrospective Surveillance Study

BACKGROUND AND OBJECTIVE

This study aims to assess the prevalence and antimicrobial susceptibility patterns of bacterial infections associated with both early-onset sepsis (EOS) and late-onset sepsis (LOS).

METHODOLOGY

This descriptive retrospective surveillance research was conducted on all neonates admitted to the neonatal ICU with bacterial sepsis, where positive cultures were isolated from sterile sites (either cerebrospinal fluid or blood) at Tawam Hospital, Al Ain, Emirate of Abu Dhabi, UAE, from January 2012 and December 2021. Antimicrobial susceptibility analysis was performed.

RESULTS

The incidence of LOS (94.43%) was higher compared to EOS (5.56%). The most prevalent isolates (59.2%) were gram-positive bacteria, with gram-negative bacteria accounting for 40.8%. The leading isolates included coagulase-negative Staphylococci (CONS, 40.98%), Klebsiella (16.04%), Staphylococcus aureus (8.46%), Escherichia coli (8.24%), Pseudomonas (7.57%), and Group B Streptococcus (GBS, 5.12%). CONS were predominant in LOS cases (42.9%), while GBS was the main pathogen in EOS cases (44%).

CONCLUSIONS

We observed reduced resistance levels of CONS against ampicillin, benzylpenicillin, clindamycin, erythromycin, fusidic acid, gentamicin, oxacillin, rifampicin, and trimethoprim/sulfa. S. aureus exhibited increased resistance to erythromycin, fusidic acid, gentamicin, and levofloxacin, while E. coli demonstrated decreased resistance against cephalothin, gentamicin, and trimethoprim/sulfa. The antibiotics currently employed empirically appear to provide adequate coverage against the most prevalent bacteria causing early- and late-onset neonatal infections.