Swab samples from a hospital neonatal intensive care unit (NICU)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA432186 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6651840_1.fastq.gz | Fastqsanger.gz | |||
| SRR6651840_2.fastq.gz | Fastqsanger.gz | |||
| SRR6651850_1.fastq.gz | Fastqsanger.gz | |||
| SRR6651850_2.fastq.gz | Fastqsanger.gz | |||
| SRR6651860_1.fastq.gz | Fastqsanger.gz |
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Project description:Pichia kudriavzevii causes life-threatening infections in immune compromised hosts including hospitalized neonates. This pathogen is resistant to fluconazole while uncommon, strains resistant to multiple antifungal drugs voriconazole, amphotericin B and echinocandins have been reported in healthcare environments. Understanding how P. kudriavzevii spread, persist, and adapt to healthcare settings could help us develop better management strategies. In this study, whole genome sequencing identifies multiple outbreaks of bloodstream infections caused by P. kudriavzevii in a single neonatal intensive care unit (NICU) over five years. Interestingly, two genetically diverse clusters of P. kudriavzevii population showed frequent loss of heterozygosity (LOH) events between two temporal samples. The first outbreak cluster (during 2015-16) showed LOH at chromosomes 1, 4 and 5 and the other outbreak cluster (year 2020) exhibited marked LOH at chromosome 2. The circulation of two separate strain clusters of P. kudriavzevii suggests nosocomial transmission in the NICU in different time periods. Further, to evaluate the gene expression difference between isolates from two clusters, we compared the transcriptomic profiles of three isolates of cluster I and II and exhibiting distinct fluconazole MICs. While no difference was found at the azole target gene ERG11 or the ATP-binding cassette (ABC) transporter genes, differences in transcript abundance were found between the two isolates in genes coding for cell division and filamentation, repressor of ABC gene, FCR1 and ERG5 gene involved in ergosterol biosynthesis pathway. Our study indicates significant diversity, persistence, and rapid evolution of P. kudriavzevii within a single NICU.
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Project description:Serratia marcescens outbreak characterized by third-generation sequencing
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Project description:BackgroundPreterm infants often require non-invasive breathing support while their lungs and control of respiration are still developing. Non-invasive neurally adjusted ventilatory assist (NIV-NAVA) is an emerging technology that allows infants to breathe spontaneously while receiving support breaths proportional to their effort. This study describes the first Australian Neonatal Intensive Care Unit (NICU) experience of NIV-NAVA.MethodsRetrospective cohort study of infants admitted to a major tertiary NICU between October 2017 and April 2021 supported with NIV-NAVA. Infants were divided into three groups based on the indication to initiate NIV-NAVA (post-extubation; apnoea; escalation). Successful application of NIV-NAVA was based on the need for re-intubation within 48 h of application.ResultsThere were 169 NIV-NAVA episodes in 122 infants (82 post-extubation; 21 apnoea; 66 escalation). The median (range) gestational age at birth was 25 + 5 weeks (23 + 1 to 43 + 3 weeks) and median (range) birthweight was 963 g (365-4320 g). At NIV-NAVA application, mean (SD) age was 17 days (18.2), and median (range) weight was 850 g (501-4310 g). Infants did not require intubation within 48 h in 145/169 (85.2%) episodes [72/82 (87.8%) extubation; 21/21 (100%) apnoea; 52/66 (78.8%) escalation).ConclusionNIV-NAVA was successfully integrated for the three main indications (escalation; post-extubation; apnoea). Prospective clinical trials are still required to establish its effectiveness versus other modes of non-invasive support.
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Project description:Having an interpretable dynamic length-of-stay (LOS) model can help hospital administrators and clinicians make better decisions and improve the quality of care. The widespread implementation of electronic medical record (EMR) systems has enabled hospitals to collect massive amounts of health data. However, how to integrate this deluge of data into healthcare operations remains unclear. We propose a framework grounded in established clinical knowledge to model patients' lengths-of-stay. In particular, we impose expert knowledge when grouping raw clinical data into medically meaningful variables, which summarize patients' health trajectories. We use dynamic predictive models to output patients' remaining lengths-of-stay (RLOS), future discharges, and census probability distributions based on their health trajectories up to the current stay. Evaluated with large-scale EMR data, the dynamic model significantly improves predictive power over the performance of any model in previous literature and remains medically interpretable.
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Project description:Background: There is a growing move to provide care for premature infants in a single family, private room neonatal intensive care unit (NICU) in place of the traditional shared space, open bay NICU. The resultant effect on the developing neonatal microbiota is unknown. Study Design: Stool and groin skin swabs were collected from infants in a shared-space NICU (old NICU) and a single-family room NICU (new NICU) on the same hospital campus. Metagenomic sequencing was performed and data analyzed by CosmosID bioinformatics software package. Results: There were no significant differences between the cohorts in gestational age, length of stay, and delivery mode; infants in the old NICU received significantly more antibiotics (p = 0.03). Differentially abundant antimicrobial resistance genes and virulence associated genes were found between the cohorts in stool and skin, with more differentially abundant antimicrobial resistance genes in the new NICU. The entire bacterial microbiota analyzed to the genus level significantly differed between cohorts in skin (p = 0.0001) but not in stool samples. There was no difference in alpha diversity between the two cohorts. DNA viruses and fungi were detected but did not differ between cohorts. Conclusion: Differences were seen in the resistome and virulome between the two cohorts with more differentially abundant antimicrobial resistance genes in the new NICU. This highlights the influence that different NICU environments can have on the neonatal microbiota. Whether the differences were due to the new NICU being a single-family NICU or located in a newly constructed building warrants exploration. Long term health outcomes from the differences observed must be followed longitudinally.
| S-EPMC6026636 | biostudies-literature
Project description:IntroductionThe neonatal intensive care unit (NICU) is often seen as off-limits by preclinical medical students. The NICU cuddler curriculum is a service-learning curriculum that invited preclinical medical students into the NICU to engage with and learn from one of the hospital's most vulnerable populations: neonates. The purpose of this preclinical experience was to provide students with exposure to the NICU and an opportunity to engage with babies, families, and the NICU staff, in order to improve students' clinical and communication skills.MethodsFirst- and second- year medical students applied and were selected for participation. Participants cuddled neonates in the NICU for at least 10 hours, attended didactic sessions relevant to neonatal care, and debriefed with an attending each semester. The curriculum was evaluated via qualitative analysis and postparticipation surveys.ResultsTo date, a total of 73 students have participated in the NICU cuddler curriculum. Qualitative analysis revealed students felt included in patient care, empowered in their understanding of the social determinants of health, and useful in their role. A postsurvey of clinical medical students following participation revealed the sustained impact of this program.DiscussionThis service-learning curriculum for preclinical medical students has the potential to enhance student understanding of the social determinants of health, increase exposure to the NICU, and promote interprofessional collaboration, ultimately increasing preparedness of students for their clinical years.
| S-EPMC7809928 | biostudies-literature
Project description:AimTo investigate the frequency, etiology, and current management strategies for diarrhea in newborn.MethodsRetrospective, nationwide study involving 5801 subjects observed in neonatal intensive care units during 3 years. The main anamnesis and demographic characteristics, etiology and characteristics of diarrhea, nutritional and therapeutic management, clinical outcomes were evaluated.ResultsThirty-nine cases of diarrhea (36 acute, 3 chronic) were identified. The occurrence rate of diarrhea was 6.72 per 1000 hospitalized newborn. Etiology was defined in 29 of 39 newborn (74.3%): food allergy (20.5%), gastrointestinal infections (17.9%), antibiotic-associated diarrhea (12.8%), congenital defects of ion transport (5.1%), withdrawal syndrome (5.1%), Hirschsprung's disease (2.5%), parenteral diarrhea (2.5%), cystic fibrosis (2.5%), and metabolic disorders (2.5%). Three patients died due to complications related to diarrhea (7.7%). In 19 of 39 patients (48.7%), rehydration was performed exclusively by the enteral route.ConclusionDiarrhea in neonates is a challenging clinical condition due to the possible heterogeneous etiologies and severe outcomes. Specific guidelines are advocated in order to optimize management of diarrhea in this particular setting.
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Project description:BackgroundTo define a method for identifying neonatal intensive care unit (NICU) admissions using administrative claims data.MethodsThis was a retrospective cohort study using claims from Optum's de-identified Clinformatics® Data Mart Database (CDM) from 2016 -2020. We developed a definition to identify NICU admissions using a list of codes from the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), Current Procedural Terminology (CPT), and revenue codes frequently associated with NICU admissions. We compared agreement between codes using Kappa statistics and calculated positive predictive values (PPV) and 95% confidence intervals (CI).ResultsOn average, revenue codes (3.3%) alone identified more NICU hospitalizations compared to CPT codes alone (1.5%), whereas the use of CPT and revenue (8.9%) and CPT or revenue codes (13.7%) captured the most NICU hospitalizations, which aligns with rates of preterm birth. Gestational age alone (4.2%) and birthweight codes alone (2.0%) identified the least number of potential NICU hospitalizations. Setting CPT codes as the standard and revenue codes as the "test,", revenue codes resulted in identifying 86% of NICU admissions (sensitivity) and 97% of non-NICU admissions (specificity).ConclusionsUsing administrative data, we developed a robust definition for identifying neonatal admissions. The identified definition of NICU codes is easily adaptable, repeatable, and flexible for use in other datasets.
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Project description:BackgroundThe neonatal period is the most vulnerable time for survival in which children face the highest risk of dying in their lives. Neonatal mortality (NM) remains a global public concern, especially in sub-Saharan African (SSA) countries. Although, better progress has been made in reducing NM before 2016, Ethiopia is currently one of the top ten countries affected by NM. Studies are limited to secondary data extraction in Ethiopia which focus only on survival status during admission, and no study has been conducted in the study area in particular.ObjectiveTo assess the survival status and predictors of neonatal mortality among neonates admitted to the NICU of WURH and Nekemte Specialized Hospital, Western Ethiopia.MethodsAn institution-based prospective cohort study was conducted among a cohort of 412 neonates admitted to the NICU of WURH and Nekemte Specialized Hospital from September 1, 2020 to December 30, 2020. All neonates consecutively admitted to the NICU of the two hospitals during the study period were included in the study. Data entry was performed using Epidata version 3.0 and the analysis was performed using STATA version 14. A Kaplan Meier survival curve was constructed to estimate the cumulative survival probability. A cox proportional hazards regression model was used to identify the predictors of NM. Hazard Ratios with 95% CI were computed and all the predictors associated with the outcome variable at p-value ≤ 0.05 in the multivariable cox proportional hazards analysis were declared as a significant predictor of NM.ResultsA total of 412 neonates were followed for a median of 27 days with an IQR of 22-28 days. During the follow-up period, a total of 9249 person day observations (PDO) were detected. At the end of follow-up, 15.3% of neonates died with an overall incidence rate of death 6.81/1000 PDO. The median time to death was 10 days, and the highest incidence rate of death was observed during the first week of the neonatal period. The study found that rural residence (AHR = 2.04, 95%CI: 1.14, 3.66), lack of ANC visits (AHR = 7.77, 95%CI: 3.99, 15.11), neonatal hypothermia (AHR = 3.04, 95%CI: 1.36, 6.80), and delayed initiation of breastfeeding (AHR = 2.26, 95% CI: 1.12, 4.56) as independent predictors of NM. However, a decreased number of pregnancies decrease the risk of NM.Conclusions and recommendationsThe incidence rate of neonatal death was high particularly in the first week of life in the study area. The study found that lack of ANC visit, neonatal hypothermia, increased number of pregnancies, rural residence, and delayed initiation of breastfeeding positively predicted NM. Therefore, there is a need to encourage programs that enhance ANC visits for pregnant mothers and community-based neonatal survival strategies, particularly for countryside mothers.
| S-EPMC9337704 | biostudies-literature