Early-life antibiotic use and risk of asthma and eczema: results of a discordant twin study.

RATIONALE: Early-life antibiotic use has been associated with the development of atopic diseases, but the aetiology remains unclear. To elucidate the aetiology, we used a discordant twin design to control for genetic and environmental confounding. METHODS: We conducted a retrospective cohort study in twins aged 3-10 years from the Netherlands Twin Register (NTR, n=35 365) and a replication study in twins aged 9 years from the Childhood and Adolescent Twin Study in Sweden (CATSS, n=7916). Antibiotic use was recorded at age 0-2 years. Doctor-diagnosed asthma and eczema were reported by parents when children were aged 3-12 years in both cohorts. Individuals were included in unmatched analyses and in co-twin control analyses with disease discordant twin pairs. RESULTS: Early-life antibiotic use was associated with increased risk of asthma (NTR OR 1.34, 95% CI 1.28-1.41; CATSS OR 1.45, 95% CI 1.34-1.56) and eczema (NTR OR 1.08, 95% CI 1.03-1.13; CATSS OR 1.07, 95% CI 1.01-1.14) in unmatched analyses. Co-twin analyses in monozygotic and dizygotic twin pairs showed similar results for asthma (NTR OR 1.54, 95% CI 1.20-1.98; CATSS OR 2.00, 95% CI 1.28-3.13), but opposing results for eczema in the NTR (OR 0.99, 95% CI 0.80-1.25) and the CATSS (OR 1.67, 95% CI 1.12-2.49). The risk of asthma increased for antibiotics prescribed for respiratory infections (CATSS OR 1.45, 95% CI 1.34-1.56), but not for antibiotics commonly used for urinary tract/skin infections (CATSS OR 1.02, 95% CI 0.88-1.17). CONCLUSION: Children exposed to early-life antibiotic use, particularly prescribed for respiratory infections, may be at higher risk of asthma. This risk can still be observed when correcting for genetic and environmental factors. Our results could not elucidate whether the relationship between early-life antibiotic use and eczema is confounded by familial and genetic factors.

Predicting Beta-Lactam Target Non-Attainment in ICU Patients at Treatment Initiation: Development and External Validation of Three Novel (Machine Learning) Models.

In the intensive care unit (ICU), infection-related mortality is high. Although adequate antibiotic treatment is essential in infections, beta-lactam target non-attainment occurs in up to 45% of ICU patients, which is associated with a lower likelihood of clinical success. To optimize antibiotic treatment, we aimed to develop beta-lactam target non-attainment prediction models in ICU patients. Patients from two multicenter studies were included, with intravenous intermittent beta-lactam antibiotics administered and blood samples drawn within 12-36 h after antibiotic initiation. Beta-lactam target non-attainment models were developed and validated using random forest (RF), logistic regression (LR), and naïve Bayes (NB) models from 376 patients. External validation was performed on 150 ICU patients. We assessed performance by measuring discrimination, calibration, and net benefit at the default threshold probability of 0.20. Age, sex, serum creatinine, and type of beta-lactam antibiotic were found to be predictive of beta-lactam target non-attainment. In the external validation, the RF, LR, and NB models confirmed good discrimination with an area under the curve of 0.79 [95% CI 0.72-0.86], 0.80 [95% CI 0.73-0.87], and 0.75 [95% CI 0.67-0.82], respectively, and net benefit in the RF and LR models. We developed prediction models for beta-lactam target non-attainment within 12-36 h after antibiotic initiation in ICU patients. These online-accessible models use readily available patient variables and help optimize antibiotic treatment. The RF and LR models showed the best performance among the three models tested.