Use of Structured Electronic Health Records Data Elements for the Development of Computable Phenotypes to Identify Potential Adverse Events Associated with Intravenous Immunoglobulin Infusion

Jillian H. Hurst (), Amanda Brucker, Congwen Zhao, Hannah Driscoll, Haley P. Hostetler, Michael Phillips, Bari Rosenberg, Marc D. Samsky, Isaac Smith, Megan E. Reller, John J. Strouse, Cindy Ke Zhou, Graça M. Dores, Hui-Lee Wong and Benjamin A. Goldstein
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Jillian H. Hurst: Duke University School of Medicine
Amanda Brucker: Duke University School of Medicine
Congwen Zhao: Duke University School of Medicine
Hannah Driscoll: Duke University School of Medicine
Haley P. Hostetler: Duke University School of Medicine
Michael Phillips: Duke University School of Medicine
Bari Rosenberg: Duke University School of Medicine
Marc D. Samsky: Yale University School of Medicine
Isaac Smith: Duke University School of Medicine
Megan E. Reller: Duke University School of Medicine
John J. Strouse: Duke University School of Medicine
Cindy Ke Zhou: US Food and Drug Administration
Graça M. Dores: US Food and Drug Administration
Hui-Lee Wong: US Food and Drug Administration
Benjamin A. Goldstein: Duke University School of Medicine

Drug Safety, 2023, vol. 46, issue 3, No 7, 309-318

Abstract: Abstract Introduction Detection of adverse reactions to drugs and biologic agents is an important component of regulatory approval and post-market safety evaluation. Real-world data, including insurance claims and electronic health records data, are increasingly used for the evaluation of potential safety outcomes; however, there are different types of data elements available within these data resources, impacting the development and performance of computable phenotypes for the identification of adverse events (AEs) associated with a given therapy. Objective To evaluate the utility of different types of data elements to the performance of computable phenotypes for AEs. Methods We used intravenous immunoglobulin (IVIG) as a model therapeutic agent and conducted a single-center, retrospective study of 3897 individuals who had at least one IVIG administration between 1 January 2014 and 31 December 2019. We identified the potential occurrence of four different AEs, including two proximal AEs (anaphylaxis and heart rate alterations) and two distal AEs (thrombosis and hemolysis). We considered three different computable phenotypes: (1) an International Classification of Disease (ICD)-based phenotype; (2) a phenotype-based on EHR-derived contextual information based on structured data elements, including laboratory values, medication administrations, or vital signs; and (3) a compound phenotype that required both an ICD code for the AE in combination with additional EHR-derived structured data elements. We evaluated the performance of each of these computable phenotypes compared with chart review-based identification of AEs, assessing the positive predictive value (PPV), specificity, and estimated sensitivity of each computable phenotype method. Results Compound computable phenotypes had a high positive predictive value for acute AEs such as anaphylaxis and bradycardia or tachycardia; however, few patients had both ICD codes and the relevant contextual data, which decreased the sensitivity of these computable phenotypes. In contrast, computable phenotypes for distal AEs (i.e., thrombotic events or hemolysis) frequently had ICD codes for these conditions in the absence of an AE due to a prior history of such events, suggesting that patient medical history of AEs negatively impacted the PPV of computable phenotypes based on ICD codes. Conclusions These data provide evidence for the utility of different structured data elements in computable phenotypes for AEs. Such computable phenotypes can be used across different data sources for the detection of infusion-related adverse events.

Date: 2023
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DOI: 10.1007/s40264-023-01276-6

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