Current and recommended practices for evaluating adverse drug events using electronic health records: A systematic review

Ng, Ding Quan; Dang, Emily; Chen, Lijie; Nguyen, Mary Thuy; Nguyen, Michael Ky Nguyen; Samman, Sarah; Nguyen, Tiffany M.; Cadiz, Christine; Nguyen, Lee S.; Chan, Alexandre

doi:10.1002/jac5.1524

Cited by 4 publications

(3 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…46 A previous report recommended not to use category III ADE labels. 79 However, we argue that—when applied in a causal prediction modeling framework —category III ADE labels could serve as rapidly accessible indicators of actual ADEs. Moreover, this approach does not require formal causality assessments prior to fitting the models (as is the case for category II).…”

Section: Discussionmentioning

confidence: 95%

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Kom

Dongelmans

Keizer

et al. 2023

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

Objective We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients. Materials and Methods We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Results Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated. Conclusions Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.

show abstract

Section: Discussionmentioning

confidence: 95%

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Kom

Dongelmans

Keizer

et al. 2023

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

show abstract

“…Poor quality data collected from EHR sources designed for non-research methods, or missing data, may lead to selection bias and information bias. Therefore, we applied recommended practices to address these inherent limitations, employing strategies such as defining the index date as the first drug exposure date to reduce the risk of immortal time bias [ 28 ]. We also designed the follow-up period carefully and treated drug exposure as a time-varying feature, considering factors such gaps in medication records and initiation of other drugs, rather than assuming initial exposure remains the same throughout the follow-up period.…”

Section: Discussionmentioning

confidence: 99%

“…All healthcare data were stored using appropriate standard OMOP concept IDs across different domains (e.g., SNOMED codes for “Condition” domain, and RxNorm for active ingredients in the “Drug” domain). Thus, the appropriate OMOP concept IDs for bleeding were translated from validated ICD-9-CM and ICD-10-CM codes for bleeding [ 25 , 26 ], excluding trauma-related bleeding events, using the concept set builder toolkit in the Observational Health Data Sciences and Informatics ATLAS program [ 27 ] and applying the recommended practices to define ADEs [ 28 ]. The OMOP concept IDs are presented in eTable 2 of the Supplement.…”

Section: Methodsmentioning

confidence: 99%

Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study

Goyal

Zhang

et al. 2023

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

Introduction Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding. Methods The AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians’ input and comprised sociodemographic, lifestyle, comorbidities, and medication use information. We identified bleeding events with validated EHR algorithms and applied logistic regression, decision tree, random forest, and extreme gradient boost to predict bleeding during SSRI exposure. We assessed model performance with area under the receiver operating characteristic curve statistic (AUC) and defined clinically significant features as resulting in > 0.01 decline in AUC after removal from the model, in three of four ML models. Results There were 10,362 participants exposed to SSRIs, with 9.6% experiencing a bleeding event during SSRI exposure. For each SSRI, performance across all four ML models was relatively consistent. AUCs from the best models ranged 0.632–0.698. Clinically significant features included health literacy for escitalopram, and bleeding history and socioeconomic status for all SSRIs. Conclusions We demonstrated feasibility of predicting ADEs using ML. Incorporating genomic features and drug interactions with deep learning models may improve ADE prediction.

show abstract

Machine-learning-based adverse drug event prediction from observational health data: A review

Denck

Özkırımlı

Wang

2023

Drug Discovery Today

View full text Add to dashboard Cite

Current and recommended practices for evaluating adverse drug events using electronic health records: A systematic review

Cited by 4 publications

References 65 publications

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study

Machine-learning-based adverse drug event prediction from observational health data: A review

Contact Info

Product

Resources

About