The risk of serious bacterial infections among young ex-premature infants with fever

Rate of Urinary Tract Infections, Bacteremia, and Meningitis in Preterm and Term Infants

BACKGROUND AND OBJECTIVES

There are very limited data on the rate of urinary tract infections (UTI), bacteremia, and meningitis in preterm infants with fever. Many of the studies on the incidence of these infections excluded preterm infants. This study compared the rate of these infections in preterm infants born at 32-36 weeks to term infants born at 37-42 weeks.

METHODS

A multicenter observational cohort study was conducted to evaluate rates of UTI, bacteremia, and meningitis in term and preterm infants 8-60 days of age with a diagnosis of fever from 2016 through 2022 using encounter data from children's hospitals in the Pediatric Health Information System.

RESULTS

There were 19 507 total febrile infants identified, of which 2162 were preterm and 17 345 were term. Preterm infants had a lower rate of UTI than term infants (1.8% confidence interval [CI] [1.3-2.5] vs 3.0% CI [2.7-3.2], P = .001). Preterm and term infants did not have statistically different rates of bacteremia (1.5% CI [1.3-1.7] vs 1.2% CI [0.8-1.8], P = .44) or meningitis (0.16% CI [0.1-0.2] vs 0.05% CI [0-0.2], P = .36).

CONCLUSIONS

There was no difference in the rate of bacteremia or meningitis between term and preterm infants in a large multicenter cohort of febrile infants. Preterm infants had a lower rate of UTI than term infants. This is the first multicenter study to compare UTI, bacteremia, and meningitis between term and preterm febrile infants.

A diagnostic model based on routine blood examination for serious bacterial infections in neonates-a cross-sectional study

Routine blood examination is an easy way to examine infectious diseases. This study is aimed to develop a model to diagnose serious bacterial infections (SBI) in ICU neonates based on routine blood parameters. This was a cross-sectional study, and data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III). SBI was defined as suffering from one of the following: pyelonephritis, bacteraemia, bacterial meningitis, sepsis, pneumonia, cellulitis, and osteomyelitis. Variables with statistical significance in the univariate logistic regression analysis and log systemic immune-inflammatory index (SII) were used to develop the model. The area under the curve (AUC) was calculated to assess the performance of the model. A total of 1,880 participants were finally included for analysis. Weight, haemoglobin, mean corpuscular volume, white blood cell, monocyte, premature delivery, and log SII were selected to develop the model. The developed model showed a good performance to diagnose SBI for ICU neonates, with an AUC of 0.812 (95% confidence interval (CI): 0.737-0.888). A nomogram was developed to make this model visualise. In conclusion, our model based on routine blood parameters performed well in the diagnosis of neonatal SBI, which may be helpful for clinicians to improve treatment recommendations.