Relationship between antibiotic consumption pattern and antibiotic resistance in neonatal sepsis.

Background and aim: Inappropriate use of antibiotics may increase antimicrobial resistance (AMR) among different microorganisms and may lead to treatment failure in neonatal septicemia. The aim of this study was to recognize the most common microorganisms responsible for neonatal sepsis and to evaluate the trend of change of resistance pattern among microorganisms. Methods: This study was done retrospectively on 344 cases diagnosed with neonatal sepsis, including both early and late onset cases, admitted to the tertiary care teaching hospital of southern India from January 2012 to July 2017. Accordingly, 231 culture positive neonatal sepsis cases were collected from hospital data base and analyzed. Culture positive cases within 72 hours of life were termed as early onset while after 72 hours were late onset. Antibiotics utilization during the period was calculated using WHO AMC tool and reported as (DDD)/100 bed days. Results: Klebsiella pneumoniae with 56 (21.8%) and Coagulase negative Staphylococcus with 52 (20.2%) cases were the most frequent isolated organisms which were responsible for 55.8% and 14.6% of deaths among the study subjects respectively. Amikacin (86.7%), vancomycin (52.3%) and ampicillin (40.6%) were the most used antibiotics in terms of DDD/100 bed days. Conclusion: The results obtained from our study have brought substantial information on the antibiotic resistance pattern among microorganisms causing neonatal sepsis. Moreover, results obtained from this study can be used for designing antibiotic stewardship policies to prevent the emergence of resistance and to improve the treatment outcome.

Identification of potential risk factors for the poor prognosis of neonatal sepsis.

Background and aim: Risk factor-based approach is one of the best approaches employed by middle income countries which are not well facility driven for any disease management. Thus, through this approach, we aim to identify the potential risk factors responsible for the poor outcome in neonatal sepsis. Methods: A case control was conducted retrospectively with neonates admitted to Neonatal Intensive Care Unit during January 2012 to December 2016. Cases were identified using ICD-10 Code from inpatient medical records and demographic, maternal and neonatal details were collected from the medical files. Logistic regression was performed to identify the risk factors associated with mortality in neonatal sepsis. Results: A total of 613 neonates were found to have culture positive sepsis from the 4690 neonates admitted in the Neonatal Intensive Care Unit (NICU). There was a total of 831 episodes in the 613 neonates. The mortality rate in neonates with sepsis was found to be 25.4%. Extremely low birth weight (OR 6.171, CI 3.475-10.957), extreme preterm (OR 5.761, CI 2.612-12.708), very preterm (OR 2.548, CI 1.607-4.042), preeclampsia (OR 1.671, CI 1.091-2.562), acute renal failure (OR 4.939, CI-2.588-9.426), coagulopathy (OR 2.211, CI-1.486-3.289), septic shock (OR 173.522, CI-23.642-1273.59), thrombocytopenia (OR 5.231, CI-3.310-8.268), leukopenia (OR 2.422, CI- 1.473-3.984), CRP > 24 (OR 2.099, CI-1.263-3.487) and abnormal absolute neutrophil count (OR 2.108, CI-1.451-3.062) were some of the significant predictors, identified through risk-based approach, in assessing mortality in neonatal sepsis. Conclusion: Risk-based approach applied was successful in determining plausible important predictors such like extreme low birth weight, extreme preterm, resistance against gram negative infections, preeclampsia, septic shock, hypotension, leukopenia, neutropenia, thrombocytopenia in predicting mortality in neonatal sepsis. These potential risk factors, identified through risk- based approach, can play a pivotal role in assisting clinician to make appropriate and judicious decision.

Prediction modelling in the early detection of neonatal sepsis.

BACKGROUND: Prediction modelling can greatly assist the health-care professionals in the management of diseases, thus sparking interest in neonatal sepsis diagnosis. The main objective of the study was to provide a complete picture of performance of prediction models for early detection of neonatal sepsis. METHODS: PubMed, Scopus, CINAHL databases were searched and articles which used various prediction modelling measures for the early detection of neonatal sepsis were comprehended. Data extraction was carried out based on Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Extricate data consisted of objective, study design, patient characteristics, type of statistical model, predictors, outcome, sample size and location. Prediction model Risk of Bias Assessment Tool was applied to gauge the risk of bias of the articles. RESULTS: An aggregate of ten studies were included in the review among which eight studies had applied logistic regression to build a prediction model, while the remaining two had applied artificial intelligence. Potential predictors like neonatal fever, birth weight, foetal morbidity and gender, cervicovaginitis and maternal age were identified for the early detection of neonatal sepsis. Moreover, birth weight, endotracheal intubation, thyroid hypofunction and umbilical venous catheter were promising factors for predicting late-onset sepsis; while gestational age, intrapartum temperature and antibiotics treatment were utilised as budding prognosticators for early-onset sepsis detection. CONCLUSION: Prediction modelling approaches were able to recognise promising maternal, neonatal and laboratory predictors in the rapid detection of early and late neonatal sepsis and thus, can be considered as a novel way for clinician decision-making towards the disease diagnosis if not used alone, in the years to come.

Cannabinoid receptor 2 activation mitigates lipopolysaccharide-induced neuroinflammation and sickness behavior in mice.

RATIONALE AND OBJECTIVES: Cannabinoid receptor 2 (CB2R) signaling in the brain is associated with the pathophysiology of depression. Sickness behavior, characterized by lessened mobility, social interaction, and depressive behavior, is linked with neuroinflammation, oxidative stress, and immune system. The present study was aimed at evaluating 1-phenylisatin (PI), a CB2R agonist, in sickness behavior. METHODS: Influence of acute and 7-day activation of CB2R using PI in lipopolysaccharide (LPS)-induced sickness behavior was assessed in mice. An acute injection of LPS (1.5 mg/kg) produced a fully developed sickness behavior in animals within 1 h of administration. The behavioral paradigm was assessed by open field test, forced swim test, and tail suspension test. Further, tumor necrosis factor-α (TNF-α), antioxidant enzymes, and lipid peroxidation were measured in the brain to correlate neuroinflammation and oxidative stress with sickness behavior. Both treatments, PI (20 mg/kg) and imipramine (15 mg/kg), were administered orally (once for acute and once daily for 7-day protocols). RESULTS: LPS elevated the brain TNF-α level, augmented oxidative stress, and induced the sickness behavior in mice. Acute and 7-day treatment of mice with PI significantly reduced the LPS-induced sickness behavior. In addition, PI inhibited the neuroinflammation evidenced by a reduction in brain TNF-α and oxidative stress. CONCLUSION: Our data propose that acute and long-term activation of CB2R might prevent neuroinflammation and oxidative stress-associated sickness behavior.