Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study.

BACKGROUND: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with 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 adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19. OBJECTIVE: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration's postmarket surveillance capabilities while minimizing public burden. This study aimed to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify 5 AESIs being monitored by the Center for Disease Control and Prevention for COVID-19 or other vaccines: anaphylaxis, Guillain-Barré syndrome, myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome, and febrile seizure. This study examined whether these phenotypes have sufficiently high positive predictive value (PPV) to ensure that the cases selected for surveillance are reasonably likely to be a postbiologic adverse event. This allows patient privacy, and security concerns for the data sharing of patients who had nonadverse events can be properly accounted for when evaluating the cost-benefit aspect of our approach. METHODS: AESI phenotype algorithms were developed to apply to electronic health record data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. To validate the performance of the algorithms, we applied them to electronic health record data from a US academic health system and provided a sample of cases for clinicians to evaluate. Performance was assessed using PPV. RESULTS: With a PPV of 93.3%, our anaphylaxis algorithm performed the best. The PPVs for our febrile seizure, myocarditis/pericarditis, thrombocytopenia syndrome, and Guillain-Barré syndrome algorithms were 89%, 83.5%, 70.2%, and 47.2%, respectively. CONCLUSIONS: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI postmarket detection.

A highly resolved network reveals the role of terrestrial herbivory in structuring aboveground food webs.

Comparative studies suggest remarkable similarities among food webs across habitats, including systematic changes in their structure with diversity and complexity (scale-dependence). However, historic aboveground terrestrial food webs (ATFWs) have coarsely grouped plants and insects such that these webs are generally small, and herbivory is disproportionately under-represented compared to vertebrate predator-prey interactions. Furthermore, terrestrial herbivory is thought to be structured by unique processes compared to size-structured feeding in other systems. Here, we present the richest ATFW to date, including approximately 580 000 feeding links among approximately 3800 taxonomic species, sourced from approximately 27 000 expert-vetted interaction records annotated as feeding upon one of six different resource types: leaves, flowers, seeds, wood, prey and carrion. By comparison to historical ATFWs and null ecological hypotheses, we show that our temperate forest web displays a potentially unique structure characterized by two properties: (i) a large fraction of carnivory interactions dominated by a small number of hyper-generalist, opportunistic bird and bat predators; and (ii) a smaller fraction of herbivory interactions dominated by a hyper-rich community of insects with variably sized but highly specific diets. We attribute our findings to the large-scale, even resolution of vertebrate, insect and plant guilds in our food web.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Data quality and timeliness analysis for post-vaccination adverse event cases reported through healthcare data exchange to FDA BEST pilot platform.

Introduction: This study is part of the U.S. Food and Drug Administration (FDA)'s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA's postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality. Materials and methods: The study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR. Results: The volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met. Discussion: This study describes the platform's data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

A Survey of Multigenic Protein-Altering Variant Frequency in Familial Exudative Vitreo-Retinopathy (FEVR) Patients by Targeted Sequencing of Seven FEVR-Linked Genes.

While Inherited Retinal Diseases (IRDs) are typically considered rare diseases, Familial Exudative Vitreo-Retinopathy (FEVR) and Norrie Disease (ND) are more rare than retinitis pigmentosa. We wanted to determine if multigenic protein-altering variants are common in FEVR subjects within a set of FEVR-related genes. The potential occurrence of protein-altering variants in two different genes has been documented in a very small percentage of patients, but potential multigenic contributions to FEVR remain unclear. Genes involved in these orphan pediatric retinal diseases are not universally included in available IRD targeted-sequencing panels, and cost is also a factor limiting multigenic-sequence-based testing for these rare conditions. To provide an accurate solution at lower cost, we developed a targeted-sequencing protocol that includes seven genes involved in Familial Exudative Vitreo-Retinopathy (FEVR) and Norrie disease. Seventy-six DNA samples from persons refered to clinic with possible FEVR and some close relatives were sequenced using a novel Oakland-ERI orphan pediatric retinal disease panel (version 2) providing 900 times average read coverage. The seven genes involved in FEVR/ND were: NDP (ChrX), CTNNB1 (Chr3); TSPAN12 (Chr7); KIF11 (Chr10), FZD4 (Chr11), LRP5 (Chr11), ZNF408 (Chr11). A total of 33 variants were found that alter protein sequence, with the following relative distribution: LRP5 13/33 (40%), FZD4 9/33 (27%), ZNF408 6/33 (18%), (KIF11 3/33 (9%), NDP 1/33 (3%), CTNNB1 1/33 (3%). Most protein-altering variants, 85%, were found in three genes: FZD4, LRP5, and ZNF408. Four previously known pathogenic variants were detected in five families and two unrelated individuals. Two novel, likely pathogenic variants were detected in one family (FZD4: Cys450ter), and a likely pathogenic frame shift termination variant was detected in one unrelated individual (LRP5: Ala919CysfsTer67). The average number of genes with protein-altering variants was greater in subjects with confirmed FEVR (1.46, n = 30) compared to subjects confirmed unaffected by FEVR (0.95, n = 20), (p = 0.009). Thirty-four percent of persons sequenced had digenic and trigenic protein-altering variants within this set of FEVR genes, which was much greater than expected in the general population (3.6%), as derived from GnomAD data. While the potential contributions to FEVR are not known for most of the variants in a multigenic context, the high multigenic frequency suggests that potential multigenic contributions to FEVR severity warrant future investigation. The targeted-sequencing format developed will support such exploration by reducing the testing cost to $250 (US) for seven genes and facilitating greater access to genetic testing for families with this very rare inherited retinal disease.

A Revision of the Drosophila spinipes Species Group (Diptera: Drosophilidae).

Two African drosophilids have distinctive protarsal spines in both sexes: Drosophila spinipes Lamb, 1914 from the Seychelles and D. suma Burla, 1954 from the Ivory Coast. Both were later classified in the genus Hirtodrosophila Duda of the Zygothrica genus group, and suma synonymized under spinipes by Tsacas (2006). We resurrect suma from synonymy, define the spinipes species group, and add seven new species in the group from Africa and Madagascar: Drosophila cameroonensis n. sp., D. freidbergi n. sp., D. hypandrilata n. sp., D. jambiya n. sp., D. malagasy n. sp., D. nigrospinipes n. sp., and D. phalloserra n. sp. Another four species are reported but not described. Detailed morphology informs that these anthophilous flies do not belong to the Zygothrica genus group, and they are returned to the genus Drosophila (unplaced to subgenus), albeit primitive members with some very distinctive specialized features. All distribution records, published and new, are presented along with a key to all nine named species.

RelTime Relaxes the Strict Molecular Clock throughout the Phylogeny.

The RelTime method estimates divergence times when evolutionary rates vary among lineages. Theoretical analyses show that RelTime relaxes the strict molecular clock throughout a molecular phylogeny, and it performs well in the analyses of empirical and computer simulated data sets in which evolutionary rates are variable. Lozano-Fernandez et al. (2017) found that the application of RelTime to one metazoan data set (Erwin et al. 2011) produced equal rates for several ancient lineages, which led them to speculate that RelTime imposes a strict molecular clock for deep animal divergences. RelTime does not impose a strict molecular clock. The pattern observed by Lozano-Fernandez et al. (2017) was a result of the use of an option to assign the same rate to lineages in RelTime when the rates are not statistically significantly different. The median rate difference was 5% for many deep metazoan lineages for the Erwin et al. (2011) data set, so the rate equality was not rejected. In fact, RelTime analyses with and without the option to test rate differences produced very similar time estimates. We also found that the Bayesian time estimates vary widely depending on the root priors assigned, and that the use of less restrictive priors produces Bayesian divergence times that are concordant with those from RelTime for the Erwin et al. (2011) data set. Therefore, it is prudent to discuss Bayesian estimates obtained under a range of priors in any discourse about molecular dating, including method comparisons.