Signal Detection of Pediatric Drug–Induced Coagulopathy Using Routine Electronic Health Records

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review

INTRODUCTION

Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance.

METHODS

To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices.

RESULTS

We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations.

CONCLUSION

Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Artificial Intelligence-Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study

Background

The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources.

Objective

Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA.

Methods

We developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype's features and functionalities.

Results

Using the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users' individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries.

Conclusions

The novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.

Short-term administration of flunixin meglumine or firocoxib does not alter viscoelastic coagulation profiles in healthy horses

OBJECTIVE

To evaluate the effect of the cyclooxygenase-2-selective NSAID firocoxib, compared to the nonselective NSAID flunixin meglumine on viscoelastic coagulation parameters in healthy horses.

ANIMALS

12 healthy adult mixed-breed horses.

PROCEDURES

Following a crossover protocol, horses were administered flunixin meglumine (1.1 mg/kg, IV, q 12 h for 5 days), allowed a 6-month washout period, and then administered firocoxib (0.3 mg/kg, PO, once, then 0.1 mg/kg, PO, q 24 h for 4 days). Omeprazole (1 mg/kg, PO, q 24 h) was administered concurrently with each NSAID. Viscoelastic coagulation profiles and traditional coagulation parameters (prothrombin time, partial thromboplastin time, and fibrinogen) were measured before and after each treatment.

RESULTS

Viscoelastic coagulation parameters were within reference intervals before and after both treatments. There was a statistically significant difference between treatments for amplitude at 10 minutes after clot time (P = .02) and maximum clot formation (P = .02); however, the magnitude of change was not clinically significant.

CLINICAL RELEVANCE

Short-term administration of flunixin meglumine and firocoxib did not result in significant alteration of viscoelastic coagulation profiles in healthy horses. However, clinicians should be aware of possible coagulopathy secondary to NSAID administration with long-term use or critical illness, and further study is indicated.