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.

Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models.

BACKGROUND: Asthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are unclear. There is a critical need to develop methods to identify children at high-risk for future exacerbation to allow targeted prevention measures. We sought to evaluate the utility of models using spatiotemporally resolved climatic data and individual electronic health records (EHR) in predicting pediatric asthma exacerbations. METHODS: We extracted retrospective EHR data for 5982 children with asthma who had an encounter within the Duke University Health System between January 1, 2014 and December 31, 2019. EHR data were linked to spatially resolved environmental data, and temporally resolved climate, pollution, allergen, and influenza case data. We used xgBoost to build predictive models of asthma exacerbation over 30-180 day time horizons, and evaluated the contributions of different data types to model performance. RESULTS: Models using readily available EHR data performed moderately well, as measured by the area under the receiver operating characteristic curve (AUC 0.730-0.742) over all three time horizons. Inclusion of spatial and temporal data did not significantly improve model performance. Generating a decision rule with a sensitivity of 70% produced a positive predictive value of 13.8% for 180 day outcomes but only 2.9% for 30 day outcomes. CONCLUSIONS: EHR data-based models perform moderately wellover a 30-180 day time horizon to identify children who would benefit from asthma exacerbation prevention measures. Due to the low rate of exacerbations, longer-term models are likely to be most clinically useful. TRIAL REGISTRATION: Not applicable.

Acquired angioedema in B cell lymphoproliferative disease: A retrospective case series.

Acquired angioedema due to C1-inhibitor (C1-INH) deficiency (AAE-C1-INH) is rare and is associated with underlying lymphoproliferative diseases. C1-INH deficiency may be due to neoplastic over-consumption of C1-INH and the generation of anti-C1-INH autoantibodies. Uncovering an occult malignancy can lead to earlier oncology referral and improvement of angioedema after treatment of the underlying lymphoproliferative disorder. We characterized seven patients with C1-INH-AAE that highlights the importance of recognizing the association between C1-INH-AAE and underlying malignancy. In acute attacks, patients may be resistant to C1-INH therapy due to the presence of anti-C1-INH autoantibodies or rapid complement consumption, and may respond better to icatibant or ecallantide, which directly affect bradykinin. Treatment of the underlying malignancy also improves AAE-C1-INH symptoms and supports the role of lymphoproliferative B cells in AAE-C1-INH pathophysiology. Monitoring levels of C4, C1-INH function and level, and C1q may be predictive of AAE-C1-INH control and be used as surrogates for treatment efficacy. With close monitoring, low-dose danazol can be effective for long-term prophylaxis. Annual evaluation in AAE-C1-INH is recommended if an underlying malignancy is not found, as angioedema may precede the development of malignancy by several years. Our single-center study has aided in standardization of comprehensive AAE-C1-INH diagnosis, treatment, and monitoring strategies towards future therapeutic clinical trials.

Intragraft Hyaluronan Increases in Association With Acute Lung Transplant Rejection.

BACKGROUND: Acute perivascular rejection (AR) is common in lung recipients and increases the risk for chronic lung allograft dysfunction (CLAD). Hyaluronan (HA), an extracellular matrix constituent, accumulates in experimental AR and can act as an innate immune agonist, breaking tolerance and potentiating alloimmunity. We previously demonstrated HA accumulates in CLAD after human-lung transplantation. We sought to determine if HA accumulates in the bronchoalveolar lavage fluid (BALF) concurrent with AR in lung recipients. METHODS: The cohort consisted of 126 first adult lung recipients at 5 transplant centers with a total of 373 BALF samples collected within the first posttransplant year. All samples were paired with a lung biopsy from the same bronchoscopy. BALF HA (ng/mL) was quantified by ELISA and log-transformed for analysis. Linear-mixed effect models, adjusted for potential confounders, were used to estimate the association between BALF HA concentration and the presence of AR on biopsy. The association between early posttransplant BALF HA levels and the development of CLAD was explored utilizing tertiles of maximum BALF HA level observed within the first 6 months of transplant. RESULTS: In analyses adjusted for potential confounders, BALF HA concentration was significantly increased in association with AR (change in means on log-scale 0.31; 95% CI, 0.01-0.60; P = 0.044). When considered on the original scale (ng/mL), BALF HA concentrations were 1.36 times (36%) higher, on average, among samples with, versus without, AR. The cumulative incidence of CLAD was numerically higher in individuals in the highest tertiles of BALF HA level within the first 6 months after transplant, as compared with those in the lowest tertile; however, this difference was not statistically significant (P = 0.32). CONCLUSIONS: These results demonstrate accumulation of HA in clinical AR and suggest a mechanism by which innate and adaptive immune activation might interact in the development of AR and CLAD.

Immunity to varicella, measles, and mumps in patients evaluated for lung transplantation.

Vaccine-preventable viral infections are associated with increased risk of morbidity and mortality in post-transplant patients on immunosuppression regimens. Therefore, we studied rates of immunity against vaccine-preventable viruses in lung transplantation (LTx) candidates and their associations with underlying lung disease and clinical characteristics. We retrospectively studied 1025 consecutive adult patients who underwent first-time evaluation for LTx at a single center between January 2016 and October 2018. Viruses studied included varicella zoster (VZV), measles, and mumps. Young age (17-48 years old) was negatively associated with immunity for VZV (OR 4.54, p < .001), measles (OR 15.45, p < .001) and mumps (OR 3.1, p < .001), as compared to those 65+. Many LTx candidates with cystic fibrosis (CF) had undetectable virus-specific antibody titers including: 13.5% for VZV, 19.1% for measles, and 15.7% for mumps with significant odds of undetectable titers for VZV (OR 4.54, p < .001) and measles (OR 2.32, p = .010) as compared to those without CF. Therefore, a substantial number of patients undergoing LTx evaluation had undetectable virus-specific antibody titers. Our results emphasize the importance of screening for immunity to vaccine-preventable infections in this population and the need for revaccination in selected patients to boost their humoral immunity prior to transplantation.