Insights from a Rapidly Implemented COVID-19 Biobank Using Electronic Consent and Informatics Tools
Biobanking during the COVID-19 pandemic presented unique challenges regarding patient enrollment, sample collection, and experimental analysis. This report details the ways in which we rapidly overcame those challenges to create a robust database of clinical information and patient samples while maintaining clinician and researcher safety. We developed a pipeline using REDCap (Research Electronic Data Capture) to coordinate electronic informed consent, sample collection, immunological assay execution, and data analysis for biobanking samples from patients with COVID-19. We then integrated immunological assay data with clinical data extracted from the electronic health record to link study parameters with clinical readouts. Of the 193 inpatients who participated in this study, 138 consented electronically and 56 provided paper consent. We collected and banked blood samples to measure circulating cytokines and chemokines, peripheral immune cell composition and activation status, anti-COVID-19 antibodies, and germline gene polymorphisms. In addition, we collected DNA and RNA from nasopharyngeal swabs to assess viral titer and microbiome composition by 16S sequencing. The rapid spread and contagious nature of COVID-19 required special considerations and innovative solutions to biobank samples quickly while protecting researchers and clinicians. Overall, this workflow and computational pipeline allowed for comprehensive immune profiling of 193 inpatients infected with COVID-19, as well as 89 outpatients, 157 patients receiving curbside COVID-19 testing, and 86 healthy controls. We describe a novel electronic framework for biobanking and analyzing patient samples during COVID-19, and present insights and strategies that can be applied more broadly to other biobank studies.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
|---|---|
| Enthalten in: |
Biopreservation and biobanking - 21(2023), 2 vom: 01. Apr., Seite 166-175 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Higgs, Emily F [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Biobank |
|---|
| Anmerkungen: |
Date Completed 21.04.2023 Date Revised 02.04.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1089/bio.2021.0169 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM342928163 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM342928163 | ||
| 003 | DE-627 | ||
| 005 | 20240402235142.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1089/bio.2021.0169 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1360.xml |
| 035 | |a (DE-627)NLM342928163 | ||
| 035 | |a (NLM)35771982 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Higgs, Emily F |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Insights from a Rapidly Implemented COVID-19 Biobank Using Electronic Consent and Informatics Tools |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 21.04.2023 | ||
| 500 | |a Date Revised 02.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Biobanking during the COVID-19 pandemic presented unique challenges regarding patient enrollment, sample collection, and experimental analysis. This report details the ways in which we rapidly overcame those challenges to create a robust database of clinical information and patient samples while maintaining clinician and researcher safety. We developed a pipeline using REDCap (Research Electronic Data Capture) to coordinate electronic informed consent, sample collection, immunological assay execution, and data analysis for biobanking samples from patients with COVID-19. We then integrated immunological assay data with clinical data extracted from the electronic health record to link study parameters with clinical readouts. Of the 193 inpatients who participated in this study, 138 consented electronically and 56 provided paper consent. We collected and banked blood samples to measure circulating cytokines and chemokines, peripheral immune cell composition and activation status, anti-COVID-19 antibodies, and germline gene polymorphisms. In addition, we collected DNA and RNA from nasopharyngeal swabs to assess viral titer and microbiome composition by 16S sequencing. The rapid spread and contagious nature of COVID-19 required special considerations and innovative solutions to biobank samples quickly while protecting researchers and clinicians. Overall, this workflow and computational pipeline allowed for comprehensive immune profiling of 193 inpatients infected with COVID-19, as well as 89 outpatients, 157 patients receiving curbside COVID-19 testing, and 86 healthy controls. We describe a novel electronic framework for biobanking and analyzing patient samples during COVID-19, and present insights and strategies that can be applied more broadly to other biobank studies | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a REDCap | |
| 650 | 4 | |a biobank | |
| 650 | 4 | |a electronic health record | |
| 700 | 1 | |a Flood, Blake A |e verfasserin |4 aut | |
| 700 | 1 | |a Pyzer, Athalia R |e verfasserin |4 aut | |
| 700 | 1 | |a Rouhani, Sherin J |e verfasserin |4 aut | |
| 700 | 1 | |a Trujillo, Jonathan A |e verfasserin |4 aut | |
| 700 | 1 | |a Gajewski, Thomas F |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Biopreservation and biobanking |d 2008 |g 21(2023), 2 vom: 01. Apr., Seite 166-175 |w (DE-627)NLM209842369 |x 1947-5543 |7 nnns |
| 773 | 1 | 8 | |g volume:21 |g year:2023 |g number:2 |g day:01 |g month:04 |g pages:166-175 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1089/bio.2021.0169 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 21 |j 2023 |e 2 |b 01 |c 04 |h 166-175 | ||