Bayesian Forecasting Tool to Predict the Need for Antidote in Acute Acetaminophen Overdose

Desrochers, Julie; Wojciechowski, Jessica; Gobburu, Jogarao; Gopalakrishnan, Mathangi

doi:10.1002/phar.1972

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…The fourth prediction use case was prediction of the most appropriate treatment option to help guide selection of safe and effective pharmacological therapies. Only a small number of studies reported on this use case [77][78][79] (three [4%] of 67 studies).…”

Section: Reviewmentioning

confidence: 99%

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Syrowatka

Song

Amato

et al. 2022

The Lancet Digital Health

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Section: Reviewmentioning

confidence: 99%

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Syrowatka

Song

Amato

et al. 2022

The Lancet Digital Health

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“…To additionally evaluate the morphine‐MFS model performance, Bayesian forecasting of the MFS responses was performed for the NOWS infants in the test dataset. The Bayesian forecasting approach 33 considered the training model as prior information and individual observed MFS response to forecast future MFS responses in an infant for subsequent time intervals. The forecasting procedure for an infant started after monitoring the infant for the first 36 hours.…”

Section: Methodsmentioning

confidence: 99%

Model‐Based Approach to Improve Clinical Outcomes in Neonates With Opioid Withdrawal Syndrome Using Real‐World Data

Wijekoon

Aduroja

Biggs

et al. 2020

Clin Pharma and Therapeutics

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At least 60% of the neonates with opioid withdrawal syndrome (NOWS) require morphine to control withdrawal symptoms. Currently, the morphine dosing strategies are empiric, not optimal and associated with longer hospital stay. The aim of the study was to develop a quantitative, model-based, real-world data-driven approach to morphine dosing to improve clinical outcomes, such as reducing time on treatment. Longitudinal morphine dose, clinical response (Modified Finnegan Score (MFS)), and baseline risk factors were collected using a retrospective cohort design from the electronic medical records of neonates with NOWS (N = 177) admitted to the University of Maryland Medical Center. A dynamic linear mixed effects model was developed to describe the relationship between MFS and morphine dose adjusting for baseline risk factors using a split-sample data approach (70% training: 30% test). The training model was evaluated in the test dataset using a simulation based approach. Maternal methadone and benzodiazepine use, and race were significant predictors of the MFS response. Positive autocorrelations of 0.56 and 0.12 were estimated between consecutive MFS responses. On an average, for a 1,000 μg increase in the morphine dose, the MFS decreased by 0.3 units. The model evaluation showed that observed and predicted median time on treatment were similar (13.0 vs. 13.8 days). A model-based framework was developed to describe the MFSmorphine dose relationship using real-world data that could potentially be used to develop an adaptive, individualized morphine dosing strategy to improve clinical outcomes in infants with NOWS. Neonatal opioid withdrawal syndrome (NOWS), also known as neonatal abstinence syndrome, is a drug withdrawal syndrome experienced by 55-94% of in utero opioid-exposed neonates shortly after birth. 1 The dramatic increase of opioid use in pregnancy has led to a 5-fold increase in NOWS with an incidence of 1.5-8.0 per 1,000 hospital births from 2004 to 2014. 2-4 On average in the United States, 1 infant is born every

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Pharmacokinetic and Pharmacodynamic Modeling

Kalaria

Wang²,

Gobburu³

2022

Principles and Practice of Clinical Trials

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Bayesian Forecasting Tool to Predict the Need for Antidote in Acute Acetaminophen Overdose

Abstract: The population PK analysis provided a platform for acceptably predicting an individual's concentration-time profile following acute APAP overdose with at least one PAC, and the individual's covariate profile, and can potentially be used for making early NAC administration decisions.

Cited by 8 publications

References 18 publications

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Model‐Based Approach to Improve Clinical Outcomes in Neonates With Opioid Withdrawal Syndrome Using Real‐World Data

Pharmacokinetic and Pharmacodynamic Modeling

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