Relationship between adverse drug reactions and unlicensed/off-label drug use in hospitalized children (EREMI): A study protocol

Unlicensed/Off-Label Drug Prescriptions at Hospital Discharge in Children: An Observational Study Using Routinely Collected Health Data

BACKGROUND

Unlicensed and off-label (UL/OL) prescriptions have been associated with an increased risk of drug-related problems. Data of their prevalence at hospital discharge remain insufficient. We aimed to describe the prevalence of UL/OL drugs in outpatient prescriptions at discharge in children.

METHODS

We conducted a retrospective study using the routinely collected health data of children at discharge from 2014 to 2016. The primary reference source for determining licensed labelling was the summaries of product characteristics (SPCs) in a French industry-independent formulary named Thériaque. We described the characteristics of UL/OL prescriptions at discharge and looked for predictors of UL/OL prescriptions.

RESULTS

We included 2536 prescriptions of 479 children. Licensed, OL, and UL prescriptions accounted for 58.6% (95% CI: 56.7-60.5), 39.2% (95% CI: 37.3-41.1), and 2.3% (95% CI: 1.7-2.9), respectively. A total of 323 (74%) children received at least one UL/OL drug. Among the licensed drugs, bronchodilators (8.8%) and analgesics (8.6%), and among the OL drugs, antibiotics (2.8%), were the most prescribed. The younger age of the children and higher number of drugs they received increased the probability of UL/OL prescriptions (unadjusted p-value of ≤0.05).

CONCLUSION

The prevalence of UL/OL prescriptions is about 40% at discharge from a pediatric university hospital in France.

Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT

Knowledge extraction from rich text in online health communities can supplement and improve the existing knowledge base, supporting evidence-based medicine and clinical decision making. The extracted time series health management data of users can help users with similar conditions when managing their health. By annotating four relationships, this study constructed a deep learning model, BERT-BiGRU-ATT, to extract disease-medication relationships. A Chinese-pretrained BERT model was used to generate word embeddings for the question-and-answer data from online health communities in China. In addition, the bidirectional gated recurrent unit, combined with an attention mechanism, was employed to capture sequence context features and then to classify text related to diseases and drugs using a softmax classifier and to obtain the time series data provided by users. By using various word embedding training experiments and comparisons with classical models, the superiority of our model in relation to extraction was verified. Based on the knowledge extraction, the evolution of a user's disease progression was analyzed according to the time series data provided by users to further analyze the evolution of the user's disease progression. BERT word embedding, GRU, and attention mechanisms in our research play major roles in knowledge extraction. The knowledge extraction results obtained are expected to supplement and improve the existing knowledge base, assist doctors' diagnosis, and help users with dynamic lifecycle health management, such as user disease treatment management. In future studies, a co-reference resolution can be introduced to further improve the effect of extracting the relationships among diseases, drugs, and drug effects.

Drug-related risk of hospital readmission in children with chronic diseases, a systematic review

BACKGROUND

Drug-related problems (DRPs) are one of the leading causes of hospital readmissions. Children with chronic diseases are more likely to experience DRPs than adults. The burden and characteristics of drug-related readmissions at and after hospital discharge in children remain unclear.

OBJECTIVE

We aimed to summarize the impact of DRPs at and after hospital discharge on the risk of readmissions in children with chronic diseases.

METHODS

We conducted a systematic review searching PubMed from inception until January 2022. Study selection criteria were studies assessing the impact of different factors at discharge and after discharge on the risk of hospital readmissions in children with chronic diseases, reporting an assessment of DRPs. DRP could be the only risk factor assessed or one among others. Included studies were assessed with the Risk of Bias in Non-Randomized Studies - of Exposure (ROBINS-E) tool. We summarized the qualitative impact of the reported DRPs on hospital readmission as conclusive (significant association) or inconclusive.

RESULTS

Of the 4734 studies initially identified, 13 met inclusion criteria. Eleven studies were retrospective, using electronic health records. The studies assessed the impact of DRPs at or after discharge according to the type of medication (in 6 studies), number of medication (in 5 studies) and medication nonadherence (in 2 studies). From the 44 reported associations between DRPs and the risk of readmission 26 (59% [95% CI, 43%-73%]) were conclusive, of which 81% increased the risk and 19% decreased the risk, and 17 (39% [95% CI, 24%-55%]) were inconclusive.

CONCLUSION

The impact of DRPs on hospital readmissions in children with chronic diseases displayed conflicting results, estimated associations having potentially a serious risk of bias. We need more evidence with a lower risk of bias.

Serious Adverse Drug Reactions in Children and Adolescents Treated On- and Off-Label with Antidepressants and Antipsychotics in Clinical Practice

INTRODUCTION

Despite the growing evidence base for psychotropic drug treatment in pediatric patients, knowledge about the benefit-risk ratio in clinical practice remains limited. The 'Therapeutic Drug Monitoring (TDM)-VIGIL' study aimed to evaluate serious adverse drug reactions (ADRs) in children and adolescents treated with antidepressants and/or antipsychotics in approved ('on-label'), and off-label use in clinical practice.

METHODS

Psychiatric pediatric patients aged 6-18 years treated with antidepressants and/or antipsychotics either on-label or off-label were prospectively followed between October 2014 and December 2018 within a multicenter trial. Follow-up included standardized assessments of response, serious ADRs and therapeutic drug monitoring.

RESULTS

710 youth (age=14.6±2.2 years, female=66.6%) were observed for 5.5 months on average; 76.3% received antidepressants, 47.5% antipsychotics, and 25.2% both. Altogether, 55.2% of the treatment episodes with antidepressants and 80.7% with antipsychotics were off-label. Serious ADRs occurred in 8.3% (95%CI=6.4-10.6%) of patients, mainly being psychiatric adverse reactions (77.4%), predominantly suicidal ideation and behavior. The risk of serious ADRs was not significantly different between patients using psychotropics off-label and on-label (antidepressants: 8.1% vs. 11.3%, p=0.16; antipsychotics: 8.7% vs 7.5%, p=0.67). Serious ADRs occurred in 16.6% of patients who were suicidal at enrollment versus 5.6% of patients who were not suicidal (relative risk 3.0, 95%CI=1.9-4.9).

CONCLUSION

Off-label use of antidepressants and antipsychotics in youth was not a risk factor for the occurrence of serious ADRs in a closely monitored clinical setting. Results from large naturalistic trials like ours can contribute to bridging the gap between knowledge from randomized controlled trials and real-world clinical settings.