Liquid materials for biomedical research: a highly IT-integrated and automated biobanking solution

Abstract Various information technology (IT) infrastructures for biobanking, networks of biobanks and biomaterial management are described in the literature. As pre-analytical variables play a major role in the downstream interpretation of clinical as well as research results, their documentation is essential. A description for mainly automated documentation of the complete life-cycle of each biospecimen is lacking so far. Here, the example taken is from the University Medical Center Göttingen (UMG), where the workflow of liquid biomaterials is standardized between the central laboratory and the central biobank. The workflow of liquid biomaterials from sample withdrawal to long-term storage in a biobank was analyzed. Essential data such as time and temperature for processing and freezing can be automatically collected. The proposed solution involves only one major interface between the main IT systems of the laboratory and the biobank. It is key to talk to all the involved stakeholders to ensure a functional and accepted solution. Although IT components differ widely between clinics, the proposed way of documenting the complete life-cycle of each biospecimen can be transferred to other university medical centers. The complete documentation of the life-cycle of each biospecimen ensures a good interpretability of downstream routine as well as research results.

[1]  Shonali Paul,et al.  The State of Cloud-Based Biospecimen and Biobank Data Management Tools. , 2017, Biopreservation and biobanking.

[2]  Ulrich Sax,et al.  Architecture of a Biomedical Informatics Research Data Management Pipeline , 2016, MIE.

[3]  Umberto Nanni,et al.  Standard preanalytical coding for biospecimens: review and implementation of the Sample PREanalytical Code (SPREC). , 2012, Biopreservation and biobanking.

[4]  BetsouFay,et al.  Standard PREanalytical Code version 3.0. , 2018, Biopreservation and biobanking.

[5]  M. Neumaier,et al.  MS-based monitoring of proteolytic decay of synthetic reporter peptides for quality control of plasma and serum specimens. , 2013, American journal of clinical pathology.

[6]  Marc Cuggia,et al.  Integrating Biobank Data into a Clinical Data Research Network: The IBCB Project , 2018, MIE.

[7]  S. Costelloe,et al.  Preanalytical errors in medical laboratories: a review of the available methodologies of data collection and analysis , 2017, Annals of clinical biochemistry.

[8]  J. Malm,et al.  Semi-automated biobank sample processing with a 384 high density sample tube robot used in cancer and cardiovascular studies , 2015, Clinical and Translational Medicine.

[9]  M. Hummel,et al.  Anforderungen an eine standortübergreifende Biobanken-IT-Infrastruktur , 2018, Der Pathologe.

[10]  A. Šimundić,et al.  The EFLM strategy for harmonization of the preanalytical phase , 2017, Clinical chemistry and laboratory medicine.

[11]  H. Yamada,et al.  Clinical Data Interchange Standards Consortium Standardization of Biobank Data: A Feasibility Study. , 2016, Biopreservation and biobanking.

[12]  K Helbing,et al.  Managing sensitive phenotypic data and biomaterial in large-scale collaborative psychiatric genetic research projects: practical considerations , 2012, Molecular Psychiatry.

[13]  Johann Eder,et al.  IT Solutions for Privacy Protection in Biobanking , 2012, Public Health Genomics.

[14]  T Ganslandt,et al.  IT Infrastructure Components for Biobanking , 2010, Applied Clinical Informatics.

[15]  I. Cockburn,et al.  The Economics of Reproducibility in Preclinical Research , 2015, PLoS biology.

[16]  F. Betsou,et al.  Standard Preanalytical Coding for Biospecimens: Defining the Sample PREanalytical Code , 2010, Cancer Epidemiology, Biomarkers & Prevention.

[17]  Nora Nikolac,et al.  Preanalytical quality improvement: in quality we trust , 2013, Clinical chemistry and laboratory medicine.

[18]  A. Šimundić,et al.  The role of European Federation of Clinical Chemistry and Laboratory Medicine Working Group for Preanalytical Phase in standardization and harmonization of the preanalytical phase in Europe , 2016, Annals of clinical biochemistry.

[19]  Hans-Ulrich Prokosch,et al.  Designing and implementing a biobanking IT framework for multiple research scenarios. , 2012, Studies in health technology and informatics.

[20]  Erica E Benson,et al.  Is there a protocol for using the SPREC? , 2013, Biopreservation and biobanking.

[21]  Mathias Brochhausen,et al.  OBIB-a novel ontology for biobanking , 2016, Journal of Biomedical Semantics.

[22]  Jim Vaught,et al.  Preanalytical variables affecting the integrity of human biospecimens in biobanking. , 2015, Clinical chemistry.

[23]  Christian Gieger,et al.  Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research , 2015, European Journal of Human Genetics.

[24]  Anthony Larsson The Need for Research Infrastructures: A Narrative Review of Large-Scale Research Infrastructures in Biobanking. , 2017, Biopreservation and biobanking.

[25]  A-Jin Lee,et al.  Effects of one directional pneumatic tube system on routine hematology and chemistry parameters; A validation study at a tertiary care hospital , 2017, Practical laboratory medicine.

[26]  F. Ückert,et al.  Exploiting Distributed, Heterogeneous and Sensitive Data Stocks while Maintaining the Owner’s Data Sovereignty , 2015, Methods of Information in Medicine.

[27]  Cord Spreckelsen,et al.  Towards Sustainable Data Management in Professional Biobanking , 2015, eHealth.

[28]  Petr Holub,et al.  Toward Global Biobank Integration by Implementation of the Minimum Information About BIobank Data Sharing (MIABIS 2.0 Core). , 2016, Biopreservation and biobanking.

[29]  Hans-Ulrich Prokosch,et al.  Proof-of-Concept Integration of Heterogeneous Biobank IT Infrastructures into a Hybrid Biobanking Network , 2017, GMDS.

[30]  S. D. Sales,et al.  Designing an automated blood fractionation system. , 2008, International journal of epidemiology.

[31]  Patrick F. Sullivan,et al.  LifeGene—a large prospective population-based study of global relevance , 2010, European Journal of Epidemiology.

[32]  S. Green,et al.  The cost of poor blood specimen quality and errors in preanalytical processes. , 2013, Clinical biochemistry.