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

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.

Understanding Immunotherapy Terminology: An Analysis of Provider-Patient Conversations.

BACKGROUND: Immunotherapy terminology is complex and can be difficult for patients to understand, threatening informed consent. The aims of this exploratory study are to determine whether patients understand immunotherapy terminology and if the provider defining the term improves patient understanding. METHODS: Conversations between oncology providers and patients discussing immunotherapy were observed(n=39), and technical terms used were noted. With consent, patients were interviewed post-conversation to assess their understanding of these terms(n=39). Comparisons of the terms were conducted using chi-square tests, Fisher's exact tests, or ANOVA where appropriate. RESULTS: 'Immunotherapy' was the most difficult for participants to understand with 48.7% (19/39) correctly defining immunotherapy. 'Immunotherapy agents' was understood 53.8% (14/26) of the time. 'Immune system' was well understood (88.5%;23/26). Providers defined immunotherapy in 97.4% of conversations. There was no correlation between having immunotherapy defined in the conversation, and the likelihood of a correct definition (p=0.487). 'Immune system' was defined in 92.3% of conversations (n=26), and defining it in the conversation was correlated with increased patient understanding (p=0.009). CONCLUSION: Our results indicate that patients have difficulty understanding some immunotherapy terminology. Since patient understanding of key terminology is crucial for informed consent and patient care, it is essential to implement interventions to improve understanding.