Steven Anderson’s research while affiliated with U.S. Food and Drug Administration and other places

Background U.S. FDA's Center for Biologics Evaluation and Research (CBER) Biologics Effectiveness and Safety (BEST) Initiative leverages large electronic health records and administrative claims data to conduct active surveillance for CBER‐regulated products. Improved hemovigilance of platelet transfusions provides exposure data for future outcome studies and can identify opportunities to improve management of the limited platelet supply. Methods Platelet utilization in three hospital networks (2012–2018) is summarized from data obtained using Information Standard for Blood and Transplant (ISBT) 128 platelet codes. Transfusion episodes, the number of units transfused, and component characteristics are described. Results Most platelet‐transfused patients (range 59.6%–62.2% across study years for all sites) received platelets once per year and used a small proportion of the total platelets transfused per year (range 18.4%–22.5%). In contrast, a minority of patients were transfused 12 or more times in a given study year (range 4.4%–6.3%) and used a plurality of transfused platelets (range 32.2%–44.4%) per year. The overall ratio of platelets transfused to the number of patients receiving any platelet transfusion was stable over the study period (range 3.9–4.5 platelets/patient) and similar among participating data sources. For all data sources, most transfusion episodes (78%) involved one component per transfusion episode. Conclusion ISBT 128 coding in the BEST Initiative was used to capture platelet transfusion events, component modifications, and characterize aspects of platelet use patterns. These data can be leveraged to identify opportunities for improved management of the platelet supply and provide granular exposure information for future studies of transfusion‐related adverse events.

Background/Aim: We previously reported that late gadolinium enhancement (LGE) on cardiac MRI (CMR) was as high as 82% in pediatric patients with COVID-19 vaccine-associated myocarditis (C-VAM) despite mild clinical symptoms and normal left ventricular function. As LGE can be a harbinger for future adverse events including arrhythmias, heart failure or sudden cardiac death, we sought to identify predictors for LGE in C-VAM, specifically assessing troponin as a screening marker for C-VAM patients at risk for myocardial scarring who could then be referred for a confirmatory CMR with LGE. Methods: In this longitudinal multicenter retrospective observational study across 38 U.S. member institutions of the M yocarditis A fter C OVID V accination (MACiV) study network, 333 patients with C-VAM based on CDC criteria were included from April 2021 to November 2022. Data collected included demographics, laboratory values, clinical and cardiac imaging characteristics and outcomes. Using logistic regression, troponin levels at presentation were assessed as a log transformed continuous variable and categorized into tertiles. Results: The C-VAM patients were predominantly white (67%) adolescent males (91%, 15.7± 2.8 years). There were 216/333 (65%) patients who had both a reported troponin value and had a CMR. On univariate analysis, elevated troponin increased the probability of having LGE (OR=1.29, 95% CI: 1.06, 1.58, p=0.012). Even after controlling for age, race, sex, number of vaccine doses and left ventricular ejection fraction (OR=1.32, 95% CI: 1.06, 1.65, p=0.013). Patients >15 years compared to those ≤15 years of age were 2.94 (95% CI: 1.28, 6.75, p=0.011) times more likely to have LGE at presentation. Patients with troponin levels in the highest tertile compared to lowest tertile were 2.66 times (95% CI: 1.04, 6.83, p=0.042) more likely to have LGE along with a greater involvement > 4 AHA myocardial segments with LGE (p=0.004) Conclusions: Higher troponin values are associated with presence of late gadolinium enhancement on cardiac MRI in patients with COVID-19 vaccine-associated myocarditis. Troponin levels at presentation may facilitate risk stratification and function as a screening tool to identify those C-VAM patients with the greatest likelihood of myocardial scarring, who may benefit from undergoing CMR for tissue characterization.

Importance: Accumulating real-world (RW) evidence of axicabtagene ciloleucel (axi-cel) has demonstrated comparable safety and effectiveness to the pivotal trials in patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL). However, nearly 60% of patients receiving axi-cel eventually experience relapse, with most requiring additional therapies. Being able to identify or forecast patients developing early relapse enables clinicians to consider additional interventions to improve survival outcomes. Objective: This study aimed to develop a clinically interpretable decision tree machine learning (ML) model to identify patients with risks of early relapse of their disease within six months after receiving axi-cel. We also comprehensively evaluated the real-world safety, including cytokine release syndrome (CRS), immune effector cell associated neurotoxicity syndrome (ICANS) and secondary T cell lymphoma incidence, and the effectiveness of axi-cel in a multi-center population across the University of California (UC) Health Systems. Methods: This retrospective study included adult patients with DLBCL receiving axi-cel between October 2017 and December 2023 at five large UC tertiary academic medical centers. Computational pipelines were developed to perform data extractions, statistical analyses, and ML model development and evaluation. The primary endpoints included the survival and safety outcomes of axi-cel and the f1 score, a ML evaluation metric that combines precision and recall measuring the accuracy of the decision tree ML model in predicting early relapse within six months post-CAR-T infusion. The outcome endpoints included the medians and rates of progression-free (PFS) and overall (OS) survival at 18 months, as well as the rates of severe (≥ grade 3) CRS, and severe (≥ grade 3) ICANS. The secondary endpoints included the rates and median durations of other severe adverse events related to hematological profile and organ functions, hazard ratios (HR) of factors associated with PFS using the Cox proportional hazard regression model, and biomarkers associated with severe CRS and ICANS identified using Non-parametric Mann-Whitney tests. Finally, given recent reports, we assessed the frequency of secondary T cell malignancies within the treated population. Results: 335 patients who received axi-cel were included in the study. 203 (60.6%) had aggressive DLBCL and comorbidities that would have excluded them from enrollment in the pivotal ZUMA-1 study of axi-cel. The median follow-up, PFS, and OS were 19.2, 10.1, and 38.4 months, respectively. The 18-month PFS and OS rates were 44.3% (95% CI, 38.2-50.2%) and 61.3% (95% CI, 55.0-67.1%), respectively. Severe CRS and ICANS were observed in 16.1% and 33.4% of patients, respectively. We developed a decision tree ML model using age and four routinely measured laboratory measures (LDH, ferritin, hematocrit, and platelet count). This straightforward ML model achieved a high f1 score of 0.72 at predicting patients experiencing early relapse within six months after receiving axi-cel. Our Cox risk factor analysis found that age ≥ 65 years, lower levels of lactate dehydrogenase (LDH), ferritin, and total bilirubin, and higher circulating platelets, hemoglobin, and eosinophils were associated with longer PFS. Our biomarker analysis found that severe CRS or ICANS were associated with significantly lower post-infusion albumin, prothrombin time, and circulating blood cells including, neutrophils, eosinophils, and leukocytes, but higher levels of LDH, c-reactive protein (CRP), and ferritin. One of 335 patients (0.3%) developed secondary T-cell lymphoma 28 days after CAR-T infusion. Conclusion: This study computationally evaluated the RW performance of axi-cel using a diverse patient population across the UC Health System and further confirmed the safety and benefit of axi-cel in real-world patients. Our interpretable decision tree ML model demonstrated a novel approach to identify patients with risks of developing early relapse, using only age and 4 laboratory tests measured within 24h of CAR-T infusion, which enables clinicians to consider potential interventions in time to further extend survival outcomes of CAR-T therapy. Following confirmation, study findings suggest that our model and decision tree approach has strong potential to guide clinical decision making in patients with high-risk DLBCL.

Background We aimed to study the clinical characteristics, myocardial injury, and longitudinal outcomes of COVID-19 vaccine-associated myocarditis (C-VAM). Methods In this longitudinal retrospective observational cohort multicenter study across 38 hospitals in the United States, 333 patients with C-VAM were compared with 100 patients with multisystem inflammatory syndrome in children (MIS-C). We included patients ≤30 years of age with a clinical diagnosis of acute myocarditis after COVID-19 vaccination based on clinical presentation, abnormal biomarkers and/or cardiovascular imaging findings. Demographics, past medical history, hospital course, biochemistry results, cardiovascular imaging, and follow-up information from April 2021 to November 2022 were collected. The primary outcome was presence of myocardial injury as evidenced by late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) imaging. Findings Patients with C-VAM were predominantly white (67%) adolescent males (91%, 15.7 ± 2.8 years). Their initial clinical course was more likely to be mild (80% vs. 23%, p < 0.001) and cardiac dysfunction was less common (17% vs. 68%, p < 0.0001), compared to MIS-C. In contrast, LGE on CMR was more prevalent in C-VAM (82% vs. 16%, p < 0.001). The probability of LGE was higher in males (OR 3.28 [95% CI: 0.99, 10.6, p = 0.052]), in older patients (>15 years, OR 2.74 [95% CI: 1.28, 5.83, p = 0.009]) and when C-VAM occurred after the first or second dose as compared to the third dose of mRNA vaccine. Mid-term clinical outcomes of C-VAM at a median follow-up of 178 days (IQR 114–285 days) were reassuring. No cardiac deaths or heart transplantations were reported until the time of submission of this report. LGE persisted in 60% of the patients at follow up. Interpretation Myocardial injury at initial presentation and its persistence at follow up, despite a mild initial course and favorable mid-term clinical outcome, warrants continued clinical surveillance and long-term studies in affected patients with C-VAM. Funding The 10.13039/100000038U.S. Food and Drug Administration.

Background Current hemovigilance methods generally rely on survey data or administrative claims data utilizing billing and revenue codes, each of which has limitations. We used electronic health records (EHR) linked to blood bank data to comprehensively characterize red blood cell (RBC) utilization patterns and trends in three healthcare systems participating in the U.S. Food and Drug Administration Center for Biologics Evaluation and Research Biologics Effectiveness and Safety (BEST) initiative. Methods We used Information Standard for Blood and Transplant (ISBT) 128 codes linked to EHR from three healthcare systems data sources to identify and quantify RBC‐transfused individuals, RBC transfusion episodes, transfused RBC units, and processing methods per year during 2012–2018. Results There were 577,822 RBC units transfused among 112,705 patients comprising 345,373 transfusion episodes between 2012 and 2018. Utilization in terms of RBC units and patients increased slightly in one and decreased slightly in the other two healthcare facilities. About 90% of RBC‐transfused patients had 1 (~46%) or 2–5 (~42%)transfusion episodes in 2018. Among the small proportion of patients with ≥12 transfusion episodes per year, approximately 60% of episodes included only one RBC unit. All facilities used leukocyte‐reduced RBCs during the study period whereas irradiated RBC utilization patterns differed across facilities. Discussion ISBT 128 codes and EHRs were used to observe patterns of RBC transfusion and modification methods at the unit level and patient level in three healthcare systems participating in the BEST initiative. This study shows that the ISBT 128 coding system in an EHR environment provides a feasible source for hemovigilance activities.

Background The U.S. FDA authorized the monovalent third primary series or booster doses of COVID-19 mRNA vaccines in August 2021 for persons 18 years and older. Monitoring of outcomes following updated authorizations is critical to evaluate vaccine safety and can provide early detection of rare adverse events (AEs) not identified in pre-licensure trials. Methods We evaluated the risk of 17 AEs following third doses of COVID-19 mRNA vaccines from August 2021 through early 2022 among adults aged 18-64 years in three commercial databases (Optum, Carelon Research, CVS Health) and adults aged >65 years in Medicare Fee-For-Service. We compared observed AE incidence rates to historical (expected) rates prior to the pandemic, estimated incidence rate ratios (IRRs) for the Medicare database and pooled IRR across the three commercial databases. Analyses were also stratified by prior history of COVID-19 diagnosis. Estimates exceeding a pre-defined threshold were considered statistical signals. Results Four AEs met the threshold for statistical signals for BNT162b2 and mRNA-1273 vaccines including Bells Palsy and pulmonary embolism in Medicare, and anaphylaxis and myocarditis/pericarditis in commercial databases. Nine AEs and three AEs signaled among adults with and without prior COVID-19 diagnosis, respectively. Conclusions This early monitoring study identified statistical signals for AEs following third doses of COVID-19 mRNA vaccination. Since this method is intended for screening purposes and generates crude results, results do not establish a causal association between the vaccines and AEs. FDAs public health assessment remains consistent that the benefits of COVID-19 vaccination outweigh the risks of vaccination.

Background Adverse events (AEs) associated with vaccination have traditionally been evaluated by epidemiological studies. More recently, they have gained attention due to the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several AEs of interest to ensure the safety of vaccines, including those for COVID-19. Objective This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the US Food and Drug Administration’s postmarket surveillance capabilities while minimizing the burden of collecting clinical data on suspected postvaccination AEs. The objective of this study was to enhance active surveillance efforts through a pilot platform that can receive automatically reported AE cases through a health care data exchange. Methods We detected cases by sharing and applying computable phenotype algorithms to real-world data in health care providers’ electronic health records databases. Using the fast healthcare interoperability resources standard for secure data transmission, we implemented a computable phenotype algorithm on a new health care system. The study focused on the algorithm's positive predictive value, validated through clinical records, assessing both the time required for implementation and the accuracy of AE detection. Results The algorithm required 200-250 hours to implement and optimize. Of the 6,574,420 clinical encounters across 694,151 patients, 30 cases were identified as potential myocarditis/pericarditis. Of these, 26 cases were retrievable, and 24 underwent clinical validation. In total, 14 cases were confirmed as definite or probable myocarditis/pericarditis, yielding a positive predictive value of 58.3% (95% CI 37.3%-76.9%). These findings underscore the algorithm's capability for real-time detection of AEs, though they also highlight variability in performance across different health care systems. Conclusions The study advocates for the ongoing refinement and application of distributed computable phenotype algorithms to enhance AE detection capabilities. These tools are crucial for comprehensive postmarket surveillance and improved vaccine safety monitoring. The outcomes suggest the need for further optimization to achieve more consistent results across diverse health care settings.

BACKGROUND Adverse events (AEs) associated with vaccination have been evaluated by epidemiological studies, and, more recently, have gained additional attention with the Emergency Use Authorization (EUA) of several COVID-19 vaccines. As part of its responsibility to conduct post-market surveillance, the U.S. Food and Drug Administration (FDA) continues to monitor several AEs of interest to ensure the safety of vaccines, including those for COVID-19. OBJECTIVE This study is part of the Biologics Effectiveness and Safety (BEST) initiative, which aims to improve FDA’s post-market surveillance capabilities while minimizing the burden of collecting clinical data on suspected post-vaccination AEs. This study was designed to enhance active surveillance efforts through a pilot platform that can receive automatically reported AE cases through a healthcare data exchange. METHODS These cases were detected by distributing and applying computable phenotype algorithms to real-world data (RWD) in healthcare organizations’ electronic health records (EHR) databases. Next, data were transmitted to the pilot platform in the Fast Healthcare Interoperability Resources (FHIR) standard for analysis and validation. To assess this platform’s usefulness for detection of AEs, we distributed an algorithm for identifying myocarditis/pericarditis following COVID-19 vaccination to be applied to a new EHR system connected to the healthcare exchange and collected metrics on 1) the length of time necessary to implement the algorithm, 2) the performance of detecting post-vaccination AE using Positive Predicted Value (PPV), and 3) the % of cases with sufficient evidence for clinician validation. RESULTS The algorithm took longer than expected (~200-250 hours) to design, implement, and optimize the query on the partner EHR database. Performance was assessed among cases with sufficient information to meet case validation criteria for myocarditis or pericarditis. Of the 30 potential myocarditis/pericarditis cases selected from a population of ~6.5M clinical encounters in the study period, 26 could be transferred through the exchange, and 24 had sufficient information to meet the case criteria. Of these cases, 14 were validated as definite or probable myocarditis/pericarditis for a PPV of 58.3% (Confidence Interval (CI): 37.3%, 76.9%). CONCLUSIONS Our results support continued research using distributed phenotype algorithms and health data exchange platforms for widespread AE post-market detection and electronic case reporting.