Towards understanding global patterns of antimicrobial use and resistance in neonatal sepsis: Insights from the NeoAMR network

CITATION:Li G, Bielicki JA,Ahmed ASMNU, et al.Arch Dis Child Epub ahead of print: [please include DayMonth Year]. doi:10.1136/archdischild-2019-316816Objective To gain an understanding of the variation in available resources and clinical practices between neonatal units (NNUs) in the low-income and middle-income country (LMIC) setting to inform the design of an observational study on the burden of unit-level antimicrobial resistance (AMR). Design A web-based survey using a REDCap database was circulated to NNUs participating in the Neonatal AMR research network. The survey included questions about NNU funding structure, size, admission rates, access to supportive therapies, empirical antimicrobial guidelines and period prevalence of neonatal blood culture isolates and their resistance patterns. Setting 39 NNUs from 12 countries. Patients Any neonate admitted to one of the participating NNUs. Interventions This was an observational cohort study. Results The number of live births per unit ranged from 513 to 27 700 over the 12-month study period, with the number of neonatal cots ranging from 12 to 110. The proportion of preterm admissions <32 weeks ranged from 0% to 19%, and the majority of units (26/39, 66%) use Essential Medicines List ‘Access’ antimicrobials as their first-line treatment in neonatal sepsis. Cephalosporin resistance rates in Gram-negative isolates ranged from 26% to 84%, and carbapenem resistance rates ranged from 0% to 81%. Glycopeptide resistance rates among Gram-positive isolates ranged from 0% to 45%. Conclusion AMR is already a significant issue in NNUs worldwide. The apparent burden of AMR in a given NNU in the LMIC setting can be influenced by a range of factors which will vary substantially between NNUs. These variations must be considered when designing interventions to improve neonatal mortality globally

Rapid Bacteria Detection from Patients' Blood Bypassing Classical Bacterial Culturing

Sepsis is a life-threatening condition mostly caused by a bacterial infection resulting in inflammatory reaction and organ dysfunction if not treated effectively. Rapid identification of the causing bacterial pathogen already in the early stage of bacteremia is therefore vital. Current technologies still rely on time-consuming procedures including bacterial culturing up to 72 h. Our approach is based on ultra-rapid and highly sensitive nanomechanical sensor arrays. In measurements we observe two clearly distinguishable distributions consisting of samples with bacteria and without bacteria respectively. Compressive surface stress indicates the presence of bacteria. For this proof-of-concept, we extracted total RNA from EDTA whole blood samples from patients with blood-culture-confirmed bacteremia, which is the reference standard in diagnostics. We determined the presence or absence of bacterial RNA in the sample through 16S-rRNA hybridization and species-specific probes using nanomechanical sensor arrays. Via both probes, we identified two clinically highly-relevant bacterial species i.e., Escherichia coli and Staphylococcus aureus down to an equivalent of 20 CFU per milliliter EDTA whole blood. The dynamic range of three orders of magnitude covers most clinical cases. We correctly identified all patient samples regarding the presence or absence of bacteria. We envision our technology as an important contribution to early and sensitive sepsis diagnosis directly from blood without requirement for cultivation. This would be a game changer in diagnostics, as no commercial PCR or POCT device currently exists who can do this

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

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

Randomised placebo-controlled multicentre effectiveness trial of adjunct betamethasone therapy in hospitalised children with community-acquired pneumonia: a trial protocol for the KIDS-STEP trial

IntroductionCommunity-acquired pneumonia (CAP) causes around 10 hospitalisations per 1000 child-years, each associated with an average 13 non-routine days experienced and more than 4 parent workdays lost. In adults, steroid treatment shortens time to clinical stabilisation without an increase in complications in patients with CAP. However, despite promising data from observational studies, there is a lack of high-quality evidence for the use of steroids.Methods and analysisThe KIDS-STEP trial is a multicentre, randomised, double-blind, placebo-controlled superiority trial of betamethasone treatment on outcome of hospitalised children with CAP. Children are enrolled in paediatric emergency departments of hospitals across Switzerland and randomised to adjunct oral betamethasone for 2 days or matching placebo in addition to standard of care treatment. The co-primary outcomes are the proportion of children clinically stable 48 hours after randomisation and the proportion of children with CAP-related readmission within 28 days after randomisation. Secondary outcomes include length of hospital stay, time away from routine childcare and healthcare utilisation and total antibiotic prescriptions within 28 days from randomisation.Each of the co-primary outcomes will be analysed separately. We will test clinical stability rates using a proportion test; to test non-inferiority in readmission rates, we will construct 1−α % CI of the estimated difference and test if it contains the pre-defined margin of 7%. Success is conditional on both tests. A simulation-based sample size estimation determined that recruiting 700 patients will ensure a power of 80% for the study.Ethics and disseminationThe trial protocol and materials were approved by ethics committees in Switzerland (lead: Ethikkommission Nordwest und Zentralschweiz) and the regulatory authority Swissmedic. Participants and caregivers provide informed consent prior to study procedures commencing. The trial results will be published in peer-reviewed journals and at national and international conferences. Key messages will also be disseminated via press and social media where appropriate.Trial registration numberNCT03474991 and SNCTP000002864

Neonatal invasive candidiasis in low-and-middle-income countries: data from the NeoOBS study

Neonatal invasive candidiasis (NIC) has significant morbidity and mortality. Reports have shown a different profile of those neonates affected with NIC and of fluconazole resistant Candida spp. isolates in low-and-middle-income -countries (LMICs) compared to high-income-countries (HIC). We describe the epidemiology, Candida spp. distribution, treatment and outcomes of neonates with NIC from LMICs enrolled in a global, prospective, longitudinal, observational cohort study (NeoOBS) of hospitalised infants < 60 days postnatal age with sepsis (August 2018-February 2021). 127 neonates from 14 hospitals in 8 countries with Candida spp. isolated from blood culture were included. Median gestational age of affected neonates was 30 weeks (IQR: 28-34) and median birth weight was 1270 g (IQR: 990-1692). Only a minority had high risk criteria, such as being born < 28 weeks, 19% (24/127), or birth weight < 1000 g, 27% (34/127). The most common Candida species were C. albicans (n = 45, 35%), C. parapsilosis (n = 38, 30%) and Candida auris (n = 18, 14%). The majority of C. albicans isolates were fluconazole susceptible, whereas 59% of C. parapsilosis isolates were fluconazole resistant. Amphotericin B was the most common antifungal used [74% (78/105)], followed by fluconazole [22% (23/105)]. Death by day 28 post-enrolment was 22% (28/127). To our knowledge, this is the largest multi-country cohort of NIC in LMICs. Most of the neonates would not have been considered at high risk for NIC in HICs. A substantial proportion of isolates was resistant to first choice fluconazole. Understanding the burden of NIC in LMIC is essential to guide future research and treatment guidelines

WHO global research priorities for antimicrobial resistance in human health

The WHO research agenda for antimicrobial resistance (AMR) in human health has identified 40 research priorities to be addressed by the year 2030. These priorities focus on bacterial and fungal pathogens of crucial importance in addressing AMR, including drug-resistant pathogens causing tuberculosis. These research priorities encompass the entire people-centred journey, covering prevention, diagnosis, and treatment of antimicrobial-resistant infections, in addition to addressing the overarching knowledge gaps in AMR epidemiology, burden and drivers, policies and regulations, and awareness and education. The research priorities were identified through a multistage process, starting with a comprehensive scoping review of knowledge gaps, with expert inputs gathered through a survey and open call. The priority setting involved a rigorous modified Child Health and Nutrition Research Initiative approach, ensuring global representation and applicability of the findings. The ultimate goal of this research agenda is to encourage research and investment in the generation of evidence to better understand AMR dynamics and facilitate policy translation for reducing the burden and consequences of AMR

Rapid Bacteria Detection from Patients' Blood Bypassing Classical Bacterial Culturing

Sepsis is a life-threatening condition mostly caused by a bacterial infection resulting in inflammatory reaction and organ dysfunction if not treated effectively. Rapid identification of the causing bacterial pathogen already in the early stage of bacteremia is therefore vital. Current technologies still rely on time-consuming procedures including bacterial culturing up to 72 h. Our approach is based on ultra-rapid and highly sensitive nanomechanical sensor arrays. In measurements we observe two clearly distinguishable distributions consisting of samples with bacteria and without bacteria respectively. Compressive surface stress indicates the presence of bacteria. For this proof-of-concept, we extracted total RNA from EDTA whole blood samples from patients with blood-culture-confirmed bacteremia, which is the reference standard in diagnostics. We determined the presence or absence of bacterial RNA in the sample through 16S-rRNA hybridization and species-specific probes using nanomechanical sensor arrays. Via both probes, we identified two clinically highly-relevant bacterial species i.e., Escherichia coli and Staphylococcus aureus down to an equivalent of 20 CFU per milliliter EDTA whole blood. The dynamic range of three orders of magnitude covers most clinical cases. We correctly identified all patient samples regarding the presence or absence of bacteria. We envision our technology as an important contribution to early and sensitive sepsis diagnosis directly from blood without requirement for cultivation. This would be a game changer in diagnostics, as no commercial PCR or POCT device currently exists who can do this