Patterns of Infections among Extremely Preterm Infants.
Krystle M PerezMihai Puia-DumitrescuBryan A ComstockThomas Ragnar WoodDennis E MayockPatrick J HeagertyChristopher M Traudtnull On Behalf Of The Penut ConsortiumPublished in: Journal of clinical medicine (2023)
Infections remain a leading cause of neonatal death, especially among the extremely preterm infants. To evaluate the incidence, pathogenesis, and in-hospital outcomes associated with sepsis among hospitalized extremely preterm infants born at 24-0/7 to 27-6/7 weeks of gestation, we designed a post hoc analysis of data collected prospectively during the Preterm Epo Neuroprotection (PENUT) Trial, NCT #01378273. We analyzed culture positive infection data, as well as type and duration of antibiotic course and described their association with in-hospital morbidities and mortality. Of 936 included infants, 229 (24%) had at least one positive blood culture during their hospitalization. Early onset sepsis (EOS, ≤3 days after birth) occurred in 6% of the infants, with Coagulase negative Staphylococci (CoNS) and Escherichia Coli the most frequent pathogens. Late onset sepsis (LOS, >day 3) occurred in 20% of the infants. Nearly all infants were treated with antibiotics for presumed sepsis at least once during their hospitalization. The risk of confirmed or presumed EOS was lower with increasing birthweight. Confirmed EOS had no significant association with in-hospital outcomes or death while LOS was associated with increased risk of necrotizing enterocolitis and death. Extremely premature infants with presumed sepsis as compared to culture positive sepsis had lower rates of morbidities. In conclusion, the use of antibiotics for presumed sepsis remains much higher than confirmed infection rates. Ongoing work exploring antibiotic stewardship and presumed, culture-negative sepsis in extremely preterm infants is needed.
Keyphrases
- preterm infants
- low birth weight
- septic shock
- acute kidney injury
- early onset
- late onset
- intensive care unit
- gestational age
- escherichia coli
- healthcare
- preterm birth
- electronic health record
- cardiovascular disease
- clinical trial
- big data
- deep learning
- cystic fibrosis
- coronary artery disease
- metabolic syndrome
- machine learning
- pseudomonas aeruginosa
- acute care
- study protocol
- weight loss
- cardiovascular events
- adverse drug
- klebsiella pneumoniae
- gram negative