Comparison of antipsychotic drug use in children and adolescents in the Netherlands before and during the COVID-19 pandemic

This study aims to describe the patterns and trends in antipsychotic prescription among Dutch youth before and during the corona virus disease 2019 (COVID-19) pandemic (between 2017 and 2022). The study specifically aims to determine whether there has been an increase or decrease in antipsychotic prescription among this population, and whether there are any differences in prescription patterns among different age and sex groups. The study utilized the IADB database, which is a pharmacy prescription database containing dispensing data from approximately 120 community pharmacies in the Netherlands, to analyze the monthly prevalence and incidence rates of antipsychotic prescription among Dutch youth before and during the pandemic. The study also examined the prescribing patterns of the five most commonly used antipsychotics and conducted an autoregressive integrated moving average (ARIMA) analysis using data prior to the pandemic, to predict the expected prevalence rate during the pandemic. The prescription rate of antipsychotics for Dutch youth was slightly affected by the pandemic, with a monthly prevalence of 4.56 [4.50-4.62] per 1000 youths before COVID-19 pandemic and 4.64 [4.59-4.69] during the pandemic. A significant increase in prevalence was observed among adolescent girls aged 13-19 years. The monthly incidence rate remained stable overall, but rose for adolescent girls aged 13-19 years. Aripiprazole, and Quetiapine had higher monthly prevalence rates during the pandemic, while Risperidone and Pipamperon had lower rates. Similarly, the monthly incidence rates of Aripiprazole and Olanzapine went up, while Risperidone went down. Furthermore, the results from the ARIMA analysis revealed that despite the pandemic, the monthly prevalence rate of antipsychotic prescription was within expectation. The findings of this study suggest that there has been a moderate increase in antipsychotic prescription among Dutch youth during the COVID-19 pandemic, particularly in adolescent females aged 13-19 years. However, the study also suggests that factors beyond the pandemic may be contributing to the rise in antipsychotic prescription in Dutch youth

Comparison of antipsychotic drug use in children and adolescents in the Netherlands before and during the COVID-19 pandemic

This study aims to describe the patterns and trends in antipsychotic prescription among Dutch youth before and during the corona virus disease 2019 (COVID-19) pandemic (between 2017 and 2022). The study specifically aims to determine whether there has been an increase or decrease in antipsychotic prescription among this population, and whether there are any differences in prescription patterns among different age and sex groups. The study utilized the IADB database, which is a pharmacy prescription database containing dispensing data from approximately 120 community pharmacies in the Netherlands, to analyze the monthly prevalence and incidence rates of antipsychotic prescription among Dutch youth before and during the pandemic. The study also examined the prescribing patterns of the five most commonly used antipsychotics and conducted an autoregressive integrated moving average (ARIMA) analysis using data prior to the pandemic, to predict the expected prevalence rate during the pandemic. The prescription rate of antipsychotics for Dutch youth was slightly affected by the pandemic, with a monthly prevalence of 4.56 [4.50-4.62] per 1000 youths before COVID-19 pandemic and 4.64 [4.59-4.69] during the pandemic. A significant increase in prevalence was observed among adolescent girls aged 13-19 years. The monthly incidence rate remained stable overall, but rose for adolescent girls aged 13-19 years. Aripiprazole, and Quetiapine had higher monthly prevalence rates during the pandemic, while Risperidone and Pipamperon had lower rates. Similarly, the monthly incidence rates of Aripiprazole and Olanzapine went up, while Risperidone went down. Furthermore, the results from the ARIMA analysis revealed that despite the pandemic, the monthly prevalence rate of antipsychotic prescription was within expectation. The findings of this study suggest that there has been a moderate increase in antipsychotic prescription among Dutch youth during the COVID-19 pandemic, particularly in adolescent females aged 13-19 years. However, the study also suggests that factors beyond the pandemic may be contributing to the rise in antipsychotic prescription in Dutch youth

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.</p

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.</p

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.Statistic