Antibiotic exposure in hospitalized pediatric patients in the United States: prevalence and length of stay

Maternal-Fetal Exposure to Antibiotics: Levels, Mother-to-Child Transmission, and Potential Health Risks

Due to its widespread applications in various fields, antibiotics are continuously released into the environment and ultimately enter the human body through diverse routes. Meanwhile, the unreasonable use of antibiotics can also lead to a series of adverse outcomes. Pregnant women and developing fetuses are more susceptible to the influence of external chemicals than adults. The evaluation of antibiotic exposure levels through questionnaire surveys or prescriptions in medical records and biomonitoring-based data shows that antibiotics are frequently prescribed and used by pregnant women around the world. Antibiotics may be transmitted from mothers to their offspring through different pathways, which then adversely affect the health of offspring. However, there has been no comprehensive review on antibiotic exposure and mother-to-child transmission in pregnant women so far. Herein, we summarized the exposure levels of antibiotics in pregnant women and fetuses, the exposure routes of antibiotics to pregnant women, and related influencing factors. In addition, we scrutinized the potential mechanisms and factors influencing the transfer of antibiotics from mother to fetus through placental transmission, and explored the adverse effects of maternal antibiotic exposure on fetal growth and development, neonatal gut microbiota, and subsequent childhood health. Given the widespread use of antibiotics and the health threats posed by their exposure, it is necessary to comprehensively track antibiotics in pregnant women and fetuses in the future, and more in-depth biological studies are needed to reveal and verify the mechanisms of mother-to-child transmission, which is crucial for accurately quantifying and evaluating fetal health status.

Antibiotic use and the development of depression: A systematic review

OBJECTIVE

Increasingly, disruption of the gastrointestinal ecosystem is thought to be involved in the pathogenesis of several medical conditions, including depression. Antibiotics can induce substantial changes in the gastrointestinal microbiota and several lines of evidence suggest that antibiotics exposure may increase the risk of developing depression. This systematic review examined this potential association.

METHODS

PubMed, Ovid EMBASE, CINAHL, and PsychINFO databases, as well as unpublished resources, were searched for studies in humans published from 2000 onwards. The studies needed to consider the connection between antibiotic exposure (either alone or in combination with other antibiotics and medications) and the development of depressive symptoms and/or disorders (in isolation to other psychological conditions).

RESULTS

Nine studies met the eligibility criteria. All were observational in nature. The studies were conducted in different age groups with various indications for receiving antibiotics. Together, these relatively low-quality studies suggest a potential association between antibiotic exposure and subsequent development of depression symptoms. Specifically, studies from the United Kingdom and Sweden indicate that the risk of depression is increased by at least 20%, with the former (over 1 million participants) reporting an increased risk with the number of courses and agents used, that persists with a slow decline over the ten years following exposure.

CONCLUSIONS

The inherent limitations associated with the studies' methodologies make a reliable conclusion difficult. While the risk of antimicrobial resistance may prohibit large randomised clinical trials in healthy individuals, future placebo-controlled trials with antibiotics-based protocols (e.g. for acne) should explore their effect on mental health.

Association between prenatal antibiotics exposure and measures of fetal growth: A repeated-measure study

The abuse of antibiotics in animal husbandry has brought many public health problems, among which the passive use of antibiotics caused by eating food containing residual antibiotics has attracted the most attention. However, few studies have examined the possible adverse effects of prenatal antibiotics exposure on fetal growth and development. In this study, we investigated the associations between prenatal antibiotics exposure and measures of fetal growth. A total of 429 mother-newborn pairs from a birth cohort were enrolled and spot urine samples (N = 1287) were collected during each trimester of pregnancy. Sixteen antibiotics from 7 categories, were selected for the determination of the targeted antibiotics in maternal urines by UHPLC-MS/MS. Fetal growth indicators including newborn birth weight, birth length and gestational age (GA), were obtained from medical record. Sixteen antibiotics were found in 92.3% of the urine samples with detection frequencies ranging from 0.3% to 41.3%. Among the 16 antibiotics detected, we found that the exposure level of ciprofloxacin in the first trimester of pregnancy was negatively correlated with GA (β = -0.17 day, 95% CI, -0.32 to -0.02 day), which would increase the risk of preterm birth (OR=1.05, 95% CI, 1.00, 1.09). The exposure level of norfloxacin in the second trimester of pregnancy was negatively correlated with fetal birth weight (β = -17.56 g, 95% CI, -31.13 to -3.99 g) and birth length (β = -0.05 cm, 95% CI, -0.08 to -0.02 cm), and the exposure level of sulfamethoxazole in the third trimester of pregnancy was negatively correlated with fetal birth length (β = -0.15 cm, 95% CI, -0.29 to -0.02 cm). Our findings suggest that prenatal exposure to norfloxacin and sulfamethoxazole may adversely affect fetal growth and development.

Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission

BACKGROUND AND OBJECTIVES

Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics.

METHODS

We developed models to predict SBI-negative status at PICU admission using vital sign, laboratory, and demographic variables. Children 3-months to 18-years-old admitted to our PICU, between 2011 and 2020, were included if evaluated for infection within 24-hours, stratified by documented antibiotic exposure in the 48-hours prior. Area under the receiver operating characteristic curve (AUROC) was the primary model accuracy measure; secondarily, we calculated the number of SBI-negative children subsequently provided antibiotics in the PICU identified as low-risk by each model.

RESULTS

A total of 15 074 children met inclusion criteria; 4788 (32%) received antibiotics before PICU admission. Of these antibiotic-exposed patients, 2325 of 4788 (49%) had an SBI. Of the 10 286 antibiotic-unexposed patients, 2356 of 10 286 (23%) had an SBI. In antibiotic-exposed children, a radial support vector machine model had the highest AUROC (0.80) for evaluating SBI, identifying 48 of 442 (11%) SBI-negative children provided antibiotics in the PICU who could have been spared a median 3.7 (interquartile range 0.9-9.0) antibiotic-days per patient. In antibiotic-unexposed children, a random forest model performed best, but was less accurate overall (AUROC 0.76), identifying 33 of 469 (7%) SBI-negative children provided antibiotics in the PICU who could have been spared 1.1 (interquartile range 0.9-3.7) antibiotic-days per patient.

CONCLUSIONS

Among children who received antibiotics before PICU admission, machine learning models can identify children at low risk of SBI and potentially reduce antibiotic exposure.