Late onset sepsis: comparison between coagulase-negative staphylococci and other bacteria in the neonatal intensive care unit

Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age

BACKGROUND

Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation.

AIM

This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4 years of age using ML algorithms.

METHOD

A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked.

RESULTS

The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration.

CONCLUSION

An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.

Assessing Diagnostic Performance of Molecular Culture for Neonatal Sepsis: Protocol of the CHAMPIONS Study

Managing neonatal sepsis is challenging due to nonspecific clinical signs, hematological markers with poor accuracy, and a lengthy turnaround time for the identification of microorganisms. Delaying the initiation of antibiotics in truly infected infants can lead to severe morbidity and mortality. Therefore, decisions regarding empiric antibiotic treatment are risk stratified, which exposes many uninfected infants to antibiotics. This causes gut microbiota perturbation, unnecessary hospital admissions, and the generation of multi-resistant organisms. High-speed diagnostic assays could expedite discontinuation or avert the initiation of antibiotics in uninfected infants. This study will evaluate the diagnostic performance of molecular culture (MC), a rapid broad-range PCR-based bacterial profiling technique, for diagnosing neonatal sepsis in infants below 90 days old. A multi-center prospective observational cohort study will include infants evaluated for early and late-onset sepsis. Routine evaluation for suspected sepsis includes microbiological cultures of blood. Additionally, blood for MC will be collected. For early-onset sepsis, umbilical cord blood may be used alternatively. Primary outcome is the agreement between MC and conventional blood culture results. Secondary outcome is the agreement of both assays with clinical sepsis using four different, commonly used definitions. Faster diagnostic pathways for sepsis may reduce antibiotic exposure time. Broad-range molecular assays may identify pathogens undetectable by conventional methods. Employment of umbilical cord blood samples for early-onset sepsis diagnosis can resolve challenges in collecting adequate blood volume and could further expedite treatment decisions.

Estimating antibiotic coverage from linked microbiological and clinical data from the Swiss Paediatric Sepsis Study to support empiric antibiotic regimen selection

In light of rising antibiotic resistance, better methods for selection of empiric antibiotic treatment based on clinical and microbiological data are needed. Most guidelines target specific clinical infections, and variably adjust empiric antibiotic selection by certain patient characteristics. Coverage estimates reflect the probability that an antibiotic regimen will be active against the causative pathogen once confirmed and can provide an objective basis for empiric regimen selection. Coverage can be estimated for specific infections using a weighted incidence syndromic combination antibiograms (WISCAs) framework. However, no comprehensive data combining clinical and microbiological data for specific clinical syndromes are available in Switzerland. We therefore describe estimating coverage from semi-deterministically linked routine microbiological and cohort data of hospitalised children with sepsis. Coverage estimates were generated for each hospital and separately pooling data across ten contributing hospitals for five pre-defined patient risk groups. Data from 1,082 patients collected during the Swiss Paediatric Sepsis Study (SPSS) 2011-2015 were included. Preterm neonates were the most commonly represented group, and half of infants and children had a comorbidity. 67% of neonatal sepsis cases were hospital-acquired late-onset whereas in children 76% of infections were community-acquired. Escherichia coli, Coagulase-negative staphylococci (CoNS) and Staphylococcus aureus were the most common pathogens. At all hospitals, ceftazidime plus amikacin regimen had the lowest coverage, and coverage of amoxicillin plus gentamicin and meropenem were generally comparable. Coverage was improved when vancomycin was included in the regimen, reflecting uncertainty about the empirically targeted pathogen spectrum. Children with community-acquired infections had high coverage overall. It is feasible to estimate coverage of common empiric antibiotic regimens from linked data. Pooling data by patient risk groups with similar expected pathogen and susceptibility profiles may improve coverage estimate precision, supporting better differentiation of coverage between regimens. Identification of data sources, selection of regimens and consideration of pathogens to target for improved empiric coverage is important.

Molecular Epidemiology of Neonatal-Associated Staphylococcus haemolyticus Reveals Endemic Outbreak

Staphylococcus haemolyticus is a major cause of late-onset sepsis in neonates, and endemic clones are often multidrug-resistant. The bacteria can also act as a genetic reservoir for more pathogenic bacteria. Molecular epidemiology is important in understanding bacterial pathogenicity and preventing infection. To describe the molecular epidemiology of S. haemolyticus isolated from neonatal blood cultures at a Swedish neonatal intensive care unit (NICU) over 4 decades, including antibiotic resistance genes (ARGs), virulence factors, and comparison to international isolates. Isolates were whole-genome sequenced, and single nucleotide polymorphisms in the core genome were used to map the relatedness. The occurrence of previously described ARGs and virulence genes were investigated. Disc diffusion and gradient tests were used to determine phenotypic resistance. The results revealed a clonal outbreak of S. haemolyticus at this NICU during the 1990s. Multidrug resistance was present in 28 (82%) of all isolates and concomitant resistance to aminoglycoside and methicillin occurred in 27 (79%). No isolates were vancomycin resistant. Genes encoding ARGs and virulence factors occurred frequently. The isolates in the outbreak were more homogenous in their genotypic and phenotypic patterns. Genotypic and phenotypic resistance combinations were consistent. Pathogenic traits previously described in S. haemolyticus occurred frequently in the present isolates, perhaps due to the hospital selection pressure resulting in epidemiological success. The clonal outbreak revealed by this study emphasizes the importance of adhering to hygiene procedures in order to prevent future endemic outbreaks. IMPORTANCE This study investigated the relatedness of Staphylococcus haemolyticus isolated from neonatal blood and revealed a clonal outbreak in the 1990s at a Swedish neonatal intensive care unit. The outbreak clone has earlier been isolated in Japan and Norway. Virulence and antibiotic resistance genes previously associated with clinical S. haemolyticus were frequently occuring in the present study as well. The majority of the isolates were multidrug-resistant. These traits should be considered important for S. haemolyticus epidemiological success and are probably caused by the hospital selection pressure. Thus, this study emphasizes the importance of restrictive antibiotic use and following the hygiene procedures, to prevent further antibiotic resistance spread and future endemic outbreaks.

Optimization of Vancomycin Initial Dose in Term and Preterm Neonates by Machine Learning

INTRODUCTION

Vancomycin is one of the antibiotics most used in neonates. Continuous infusion has many advantages over intermittent infusions, but no consensus has been achieved regarding the optimal initial dose. The objectives of this study were: to develop a Machine learning (ML) algorithm based on pharmacokinetic profiles obtained by Monte Carlo simulations using a population pharmacokinetic model (POPPK) from the literature, in order to derive the best vancomycin initial dose in preterm and term neonates, and to compare ML performances with those of an literature equation (LE) derived from a POPPK previously published.

MATERIALS AND METHODS

The parameters of a previously published POPPK model of vancomycin in children and neonates were used in the mrgsolve R package to simulate 1900 PK profiles. ML algorithms were developed from these simulations using Xgboost, GLMNET and MARS in parallel, benchmarked and used to calculate the ML first dose. Performances were evaluated in a second simulation set and in an external set of 82 real patients and compared to those of a LE.

RESULTS

The Xgboost algorithm yielded numerically best performances and target attainment rates: 46.9% in the second simulation set of 400-600 AUC/MIC ratio vs. 41.4% for the LE model (p = 0.0018); and 35.3% vs. 28% in real patients (p = 0.401), respectively). The Xgboost model resulted in less AUC/MIC > 600, thus decreasing the risk of nephrotoxicity.

CONCLUSION

The Xgboost algorithm developed to estimate the initial dose of vancomycin in term or preterm infants has better performances than a previous validated LE and should be evaluated prospectively.

Infections in the NICU: Neonatal sepsis

Neonatal infections remain an important cause of neonatal morbidity and mortality worldwide. Neonatal sepsis is a systemic infection that can be classified as early-onset or late-onset pending the timing of presentation. The pathophysiology and causative pathogens of neonatal sepsis vary, with early-onset sepsis being associated with a vertically transmitted infection from mother to neonate versus late onset sepsis being commonly associated with nosocomial infections. The signs and symptoms of neonatal sepsis mimic those associated with prematurity, making timely diagnosis difficult for treating clinicians. The management of neonatal sepsis is centered around obtaining adequate culture data and initiation of broad-spectrum parenteral antibiotics. Controversies surrounding the management of neonatal sepsis include the administration of empiric antibiotics, given recent clinical studies associating early antibiotic use with clinical sequelae such as late-onset sepsis, necrotizing enterocolitis, and death in the preterm, low-birthweight infant population.

Neonatal Sepsis in a Resource-Limited Setting: Causative Microorganisms and Antimicrobial Susceptibility Profile

Background

Empiric treatment of suspected neonatal sepsis must be based on data on setting-specific causative pathogens and their respective susceptibilities to antimicrobials, as well as universal treatment guidelines. This approach will ensure better therapeutic outcomes and reduce mortality.

Objectives

The objectives of this study were to determine the bacteriological profile and antibiotic susceptibility pattern of isolated microorganisms responsible for neonatal sepsis in a regional hospital in Ghana.

Methods

This was a retrospective study that assessed causative microorganisms and antimicrobial susceptibility profiles of neonates suspected of sepsis at the Greater Accra Regional Hospital from January 2018 to December 2019. Blood culture was done using a fully automated BACTEC 9240 blood culture system. Bacteria isolates were identified by Gram staining and conventional biochemical methods. Antimicrobial susceptibility testing was done by Kirby–Bauer's disc diffusion method, and interpretations were carried out according to clinical and laboratory standards. Culture and antibiotic sensitivity reports were obtained and the data subsequently analyzed.

Results

Of 2514 blood samples collected from neonates suspected of neonatal sepsis, 528 (21.0%) of the samples were found to be culture-positive. The majority of these positive cultures were from male neonates (68.9%). A total of 11 different pathogens were isolated, of which Gram-positive organisms had a preponderance of 72.0% over Gram-negative organisms (28.0%). Staphylococcus epidermidis was the most common pathogen identified, accounting for 60.0% of isolates. The most prevalent Gram-negative bacteria were Klebsiella spp. (13.6%). Most Gram-positive microorganisms showed sensitivity to amikacin, meropenem, vancomycin, and piperacillin/tazobactam. Gram-positive isolates were found to be resistant to ampicillin and penicillin, but moderately susceptible to flucloxacillin. Most Gram-negative isolates were sensitive to meropenem.

Conclusion

The prevalence of culture-proven sepsis was 21.0%. The most prevalent Gram-negative bacteria were Klebsiella spp. As there is some level of antibiotic resistance observed in the current study, it is necessary for routine microbial analysis of samples and their antibiogram.

Neonatal Sepsis: A Review of Pathophysiology and Current Management Strategies

BACKGROUND

Early-onset sepsis, occurring within 72 hours of birth, and late-onset sepsis, occurring after this time period, present serious risks for neonates. While culture-based screening and intrapartum antibiotics have decreased the number of early-onset cases, sepsis remains a top cause of neonatal morbidity and mortality in the United States.

PURPOSE

To provide a review of neonatal sepsis by identifying its associated risk factors and most common causative pathogens, reviewing features of the term and preterm neonatal immune systems that increase vulnerability to infection, describing previous and the most current management recommendations, and discussing relevant implications for the neonatal nurse and novice neonatal nurse practitioner.

METHODS/SEARCH STRATEGY

An integrative review of literature was conducted using key words in CINAHL, Google Scholar, and PubMed.

FINDINGS/RESULTS

Group B streptococcus and Escherichia coli are the most common pathogens in early-onset sepsis, while Coagulase-negative staphylococci comprise the majority of cases in late-onset. The neonatal immune system is vulnerable due to characteristics including decreased cellular activity, underdeveloped complement systems, preferential anti-inflammatory responses, and insufficient pathogenic memory. Blood cultures remain the criterion standard of diagnosis, with several other adjunct tests under investigation for clinical use. The recent development of the sepsis calculator has been a useful tool in the management of early-onset cases.

IMPLICATIONS FOR PRACTICE

It is vital to understand the mechanisms behind the neonate's elevated risk for infection and to implement evidence-based management.

IMPLICATIONS FOR RESEARCH

Research needs exist for diagnostic methods that deliver timely and sensitive results. A tool similar to the sepsis calculator does not exist for preterm infants or late-onset sepsis, groups for which antibiotic stewardship is not as well practiced.Video Abstract available athttps://journals.lww.com/advancesinneonatalcare/Pages/videogallery.aspx?autoPlay=false&videoId=40.

Diagnosis of Neonatal Late-Onset Infection in Very Preterm Infant: Inter-Observer Agreement and International Classifications

Background: The definition of late-onset bacterial sepsis (LOS) in very preterm infants is not unified. The objective was to assess the concordance of LOS diagnosis between experts in neonatal infection and international classifications and to evaluate the potential impact on heart rate variability and rate of “bronchopulmonary dysplasia or death”. Methods: A retrospective (2017–2020) multicenter study including hospitalized infants born before 31 weeks of gestation with intention to treat at least 5-days with antibiotics was performed. LOS was classified as “certain or probable” or “doubtful” independently by five experts and according to four international classifications with concordance assessed by Fleiss’s kappa test. Results: LOS was suspected at seven days (IQR: 5–11) of life in 48 infants. Following expert classification, 36 of them (75%) were considered as “certain or probable” (kappa = 0.41). Following international classification, this number varied from 13 to 46 (kappa = −0.08). Using the expert classification, “bronchopulmonary dysplasia or death” occurred less frequently in the doubtful group (25% vs. 78%, p < 0.001). Differences existed in HRV changes between the two groups. Conclusion: The definition of LOS is not consensual with a low international and moderate inter-observer agreement. This affects the evaluation of associated organ dysfunction and prognosis.

Prevalence and antibiogram of coagulase negative Staphylococci in bioaerosols from different indoors of a university in India

BACKGROUND

Staphylococci species are the major constituents of infectious bioaerosols, particularly methicillin-resistant Staphylococci (MRS) have serious health impacts. Here, the bacterial burden was quantified, especially prevalence of MRS in bioaerosols collected from indoors of Dr. B.R. Ambedkar Central Library (DBRACL) and Central Laboratory Animal Resources (CLAR) of Jawaharlal Nehru University, New Delhi, India. Air samplings from DBRACL and CLAR were done using the settle plate method and SKC biosampler, respectively.

RESULTS

This study showed a maximum 6757 CFU/m²/hr of bacterial load in the DBRACL reading room, while unacceptable bacterial loads (> 1000 CFU/m³ of air) at different sites of CLAR. Further, at both the sampling sites the predominance of coagulase negative Staphylococci (CNS) was observed. A total 22 and 35 Staphylococci isolates were isolated from DBRACL and CLAR bioaerosols, respectively. Majority (16/22) of the Staphylococcal isolates from DBRACL belonged to human-associated Staphylococci where S. haemolyticus (5/22) was the most dominating species. However, in CLAR facility centre, animal-associated Staphylococci (19/35) were dominating, where S. xylosus (12/35) was the most dominating species. Further, antibiotic sensitivity tests revealed 41% MRS and 73% multidrug resistant (MDR) among airborne Staphylococci from DBRACL indoor bioaerosols. Similarly, in CLAR facility, approximately, 66% Staphylococci isolates were methicillin resistant, out of which 2 isolates showed high MIC value ≥ 16 μg/mL. Further, we confirmed the presence of 49% multidrug resistant Staphylococci in the indoor air of CLAR facility.

CONCLUSIONS

This study suggested that the exposure of workers and students in CLAR to such a high concentration of drug-resistant Staphylococci should not be undermined, as these bacterial concentrations are the direct representative of inhalable particulate matter (PM_2.5) as per collection procedure. Simultaneously, passive sampling from DBRACL assessed the risks due to microbial contamination in particle agglomerates, which may deposit on the crucial surfaces such as wounds/ cuts or on the frequently used items.