Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C)

Objectives

To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).

Materials and Methods

We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics.

Results

We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy.

Discussion

HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence.

Conclusion

We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations

Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate these efforts, we have developed CellMinerCDB: National Center for Advancing Translational Sciences (NCATS; https://discover.nci.nih.gov/rsconnect/cellminercdb_ncats/), which makes available activity information for 2,675 drugs and compounds, including multiple nononcology drugs and 1,866 drugs and compounds unique to the NCATS. CellMinerCDB: NCATS comprises 183 cancer cell lines, with 72 unique to NCATS, including some from previously understudied tissues of origin. Multiple forms of data from different institutes are integrated, including single and combination drug activity, DNA copy number, methylation and mutation, transcriptome, protein levels, histone acetylation and methylation, metabolites, CRISPR, and miscellaneous signatures. Curation of cell lines and drug names enables cross-database (CDB) analyses. Comparison of the datasets is made possible by the overlap between cell lines and drugs across databases. Multiple univariate and multivariate analysis tools are built-in, including linear regression and LASSO. Examples have been presented here for the clinical topoisomerase I (TOP1) inhibitors topotecan and irinotecan/SN-38. This web application provides both substantial new data and significant pharmacogenomic integration, allowing exploration of interrelationships.

SIGNIFICANCE

CellMinerCDB: NCATS provides activity information for 2,675 drugs in 183 cancer cell lines and analysis tools to facilitate pharmacogenomic research and to identify determinants of response.

Who is pregnant? defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C)

Objective

To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).

Materials and Methods

We developed a comprehensive approach, named H ierarchy and rule-based pregnancy episode I nference integrated with P regnancy P rogression S ignatures (HIPPS) and applied it to EHR data in the N3C from 1 January 2018 to 7 April 2022. HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational aging-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated three types of pregnancy cohorts based on the level of precision for gestational aging and pregnancy outcomes for comparison of COVID-19 and other characteristics.

Results

We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, spontaneous abortions), and 23.3% had unknown outcomes. We were able to estimate start dates within one week of precision for 431,173 (52.8%) episodes. 66,019 (8.1%) episodes had incident COVID-19 during pregnancy. Across varying COVID-19 cohorts, patient characteristics were generally similar though pregnancy outcomes differed.

Discussion

HIPPS provides support for pregnancy-related variables based on EHR data for researchers to define pregnancy cohorts. Our approach performed well based on clinician validation.

Conclusion

We have developed a novel and robust approach for inferring pregnancy episodes and gestational aging that addresses data inconsistency and missingness in EHR data.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study

BACKGROUND

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.

METHODS

A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis.

RESULTS

Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations.

CONCLUSIONS

Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset

Objective

The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing.

Materials and Methods

The National COVID Cohort Collaborative (N3C) table of laboratory measurement data—over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test.

Results

Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors’ records lacked units).

Discussion

The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference.

Conclusion

The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative

OBJECTIVE

In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.

MATERIALS AND METHODS

We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements.

RESULTS

Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback.

DISCUSSION

We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate.

CONCLUSION

By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Characteristics, Outcomes, and Severity Risk Factors Associated With SARS-CoV-2 Infection Among Children in the US National COVID Cohort Collaborative

Importance

Understanding of SARS-CoV-2 infection in US children has been limited by the lack of large, multicenter studies with granular data.

Objective

To examine the characteristics, changes over time, outcomes, and severity risk factors of children with SARS-CoV-2 within the National COVID Cohort Collaborative (N3C).

Design, Setting, and Participants

A prospective cohort study of encounters with end dates before September 24, 2021, was conducted at 56 N3C facilities throughout the US. Participants included children younger than 19 years at initial SARS-CoV-2 testing.

Main Outcomes and Measures

Case incidence and severity over time, demographic and comorbidity severity risk factors, vital sign and laboratory trajectories, clinical outcomes, and acute COVID-19 vs multisystem inflammatory syndrome in children (MIS-C), and Delta vs pre-Delta variant differences for children with SARS-CoV-2.

Results

A total of 1 068 410 children were tested for SARS-CoV-2 and 167 262 test results (15.6%) were positive (82 882 [49.6%] girls; median age, 11.9 [IQR, 6.0-16.1] years). Among the 10 245 children (6.1%) who were hospitalized, 1423 (13.9%) met the criteria for severe disease: mechanical ventilation (796 [7.8%]), vasopressor-inotropic support (868 [8.5%]), extracorporeal membrane oxygenation (42 [0.4%]), or death (131 [1.3%]). Male sex (odds ratio [OR], 1.37; 95% CI, 1.21-1.56), Black/African American race (OR, 1.25; 95% CI, 1.06-1.47), obesity (OR, 1.19; 95% CI, 1.01-1.41), and several pediatric complex chronic condition (PCCC) subcategories were associated with higher severity disease. Vital signs and many laboratory test values from the day of admission were predictive of peak disease severity. Variables associated with increased odds for MIS-C vs acute COVID-19 included male sex (OR, 1.59; 95% CI, 1.33-1.90), Black/African American race (OR, 1.44; 95% CI, 1.17-1.77), younger than 12 years (OR, 1.81; 95% CI, 1.51-2.18), obesity (OR, 1.76; 95% CI, 1.40-2.22), and not having a pediatric complex chronic condition (OR, 0.72; 95% CI, 0.65-0.80). The children with MIS-C had a more inflammatory laboratory profile and severe clinical phenotype, with higher rates of invasive ventilation (117 of 707 [16.5%] vs 514 of 8241 [6.2%]; P < .001) and need for vasoactive-inotropic support (191 of 707 [27.0%] vs 426 of 8241 [5.2%]; P < .001) compared with those who had acute COVID-19. Comparing children during the Delta vs pre-Delta eras, there was no significant change in hospitalization rate (1738 [6.0%] vs 8507 [6.2%]; P = .18) and lower odds for severe disease (179 [10.3%] vs 1242 [14.6%]) (decreased by a factor of 0.67; 95% CI, 0.57-0.79; P < .001).

Conclusions and Relevance

In this cohort study of US children with SARS-CoV-2, there were observed differences in demographic characteristics, preexisting comorbidities, and initial vital sign and laboratory values between severity subgroups. Taken together, these results suggest that early identification of children likely to progress to severe disease could be achieved using readily available data elements from the day of admission. Further work is needed to translate this knowledge into improved outcomes.

STRIDE: a command-line HMM-based identifier and sub-classifier of Plasmodium falciparum RIFIN and STEVOR variant surface antigen families

Background

RIFINs and STEVORs are variant surface antigens expressed by P. falciparum that play roles in severe malaria pathogenesis and immune evasion. These two highly diverse multigene families feature multiple paralogs, making their classification challenging using traditional bioinformatic methods.

Results

STRIDE (STevor and RIfin iDEntifier) is an HMM-based, command-line program that automates the identification and classification of RIFIN and STEVOR protein sequences in the malaria parasite Plasmodium falciparum. STRIDE is more sensitive in detecting RIFINs and STEVORs than available PFAM and TIGRFAM tools and reports RIFIN subtypes and the number of sequences with a FHEYDER amino acid motif, which has been associated with severe malaria pathogenesis.

Conclusions

STRIDE will be beneficial to malaria research groups analyzing genome sequences and transcripts of clinical field isolates, providing insight into parasite biology and virulence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04515-8.

Children with SARS-CoV-2 in the National COVID Cohort Collaborative (N3C)

IMPORTANCE

SARS-CoV-2.

OBJECTIVE

To determine the characteristics, changes over time, outcomes, and severity risk factors of SARS-CoV-2 affected children within the National COVID Cohort Collaborative (N3C).

DESIGN

Prospective cohort study of patient encounters with end dates before May 27th, 2021.

SETTING

45 N3C institutions.

PARTICIPANTS

Children <19-years-old at initial SARS-CoV-2 testing.

MAIN OUTCOMES AND MEASURES

Case incidence and severity over time, demographic and comorbidity severity risk factors, vital sign and laboratory trajectories, clinical outcomes, and acute COVID-19 vs MIS-C contrasts for children infected with SARS-CoV-2.

RESULTS

728,047 children in the N3C were tested for SARS-CoV-2; of these, 91,865 (12.6%) were positive. Among the 5,213 (6%) hospitalized children, 685 (13%) met criteria for severe disease: mechanical ventilation (7%), vasopressor/inotropic support (7%), ECMO (0.6%), or death/discharge to hospice (1.1%). Male gender, African American race, older age, and several pediatric complex chronic condition (PCCC) subcategories were associated with higher clinical severity (p ≤ 0.05). Vital signs (all p≤0.002) and many laboratory tests from the first day of hospitalization were predictive of peak disease severity. Children with severe (vs moderate) disease were more likely to receive antimicrobials (71% vs 32%, p<0.001) and immunomodulatory medications (53% vs 16%, p<0.001). Compared to those with acute COVID-19, children with MIS-C were more likely to be male, Black/African American, 1-to-12-years-old, and less likely to have asthma, diabetes, or a PCCC (p < 0.04). MIS-C cases demonstrated a more inflammatory laboratory profile and more severe clinical phenotype with higher rates of invasive ventilation (12% vs 6%) and need for vasoactive-inotropic support (31% vs 6%) compared to acute COVID-19 cases, respectively (p<0.03).

CONCLUSIONS

In the largest U.S. SARS-CoV-2-positive pediatric cohort to date, we observed differences in demographics, pre-existing comorbidities, and initial vital sign and laboratory test values between severity subgroups. Taken together, these results suggest that early identification of children likely to progress to severe disease could be achieved using readily available data elements from the day of admission. Further work is needed to translate this knowledge into improved outcomes.

Genomic comparison of Trypanosoma conorhini and Trypanosoma rangeli to Trypanosoma cruzi strains of high and low virulence

Background

Trypanosoma conorhini and Trypanosoma rangeli, like Trypanosoma cruzi, are kinetoplastid protist parasites of mammals displaying divergent hosts, geographic ranges and lifestyles. Largely nonpathogenic T. rangeli and T. conorhini represent clades that are phylogenetically closely related to the T. cruzi and T. cruzi-like taxa and provide insights into the evolution of pathogenicity in those parasites. T. rangeli, like T. cruzi is endemic in many Latin American countries, whereas T. conorhini is tropicopolitan. T. rangeli and T. conorhini are exclusively extracellular, while T. cruzi has an intracellular stage in the mammalian host.

Results

Here we provide the first comprehensive sequence analysis of T. rangeli AM80 and T. conorhini 025E, and provide a comparison of their genomes to those of T. cruzi G and T. cruzi CL, respectively members of T. cruzi lineages TcI and TcVI. We report de novo assembled genome sequences of the low-virulent T. cruzi G, T. rangeli AM80, and T. conorhini 025E ranging from ~ 21–25 Mbp, with ~ 10,000 to 13,000 genes, and for the highly virulent and hybrid T. cruzi CL we present a ~ 65 Mbp in-house assembled haplotyped genome with ~ 12,500 genes per haplotype. Single copy orthologs of the two T. cruzi strains exhibited ~ 97% amino acid identity, and ~ 78% identity to proteins of T. rangeli or T. conorhini. Proteins of the latter two organisms exhibited ~ 84% identity. T. cruzi CL exhibited the highest heterozygosity. T. rangeli and T. conorhini displayed greater metabolic capabilities for utilization of complex carbohydrates, and contained fewer retrotransposons and multigene family copies, i.e. trans-sialidases, mucins, DGF-1, and MASP, compared to T. cruzi.

Conclusions

Our analyses of the T. rangeli and T. conorhini genomes closely reflected their phylogenetic proximity to the T. cruzi clade, and were largely consistent with their divergent life cycles. Our results provide a greater context for understanding the life cycles, host range expansion, immunity evasion, and pathogenesis of these trypanosomatids.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5112-0) contains supplementary material, which is available to authorized users.

Comparative transcriptomic analysis of Gardnerella vaginalis biofilms vs. planktonic cultures using RNA-seq

Bacterial vaginosis is the most common gynecological disorder affecting women of reproductive age. Bacterial vaginosis is frequently associated with the development of a Gardnerella vaginalis biofilm. Recent data indicates that G. vaginalis biofilms are more tolerant to antibiotics and are able to incorporate other bacterial vaginosis -associated species, yielding a multi-species biofilm. However, despite its apparent role in bacterial vaginosis, little is known regarding the molecular determinants involved in biofilm formation by G. vaginalis. To gain insight into the role of G. vaginalis in the pathogenesis of bacterial vaginosis, we carried out comparative transcriptomic analysis between planktonic and biofilm phenotypes, using RNA-sequencing. Significant differences were found in the expression levels of 815 genes. A detailed analysis of the results obtained was performed based on direct and functional gene interactions. Similar to other bacterial species, expression of genes involved in antimicrobial resistance were elevated in biofilm cells. In addition, our data indicate that G. vaginalis biofilms assume a characteristic response to stress and starvation conditions. The abundance of transcripts encoding proteins involved in glucose and carbon metabolism was reduced in biofilms. Surprisingly, transcript levels of vaginolysin were reduced in biofilms relative to planktonic cultures. Overall, our data revealed that gene-regulated processes in G. vaginalis biofilms resulted in a protected form of bacterial growth, characterized by low metabolic activity. This phenotype may contribute towards the chronic and recurrent nature of bacterial vaginosis. This suggests that G. vaginalis is capable of drastically adjusting its phenotype through an extensive change of gene expression.