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PubMed1997

UK National Transplant Database retransplantation in the UK and Republic of Ireland, 1987-1996. UKTSSA National Transplant Database.

At the end of 1996, 1,635 kidney, 17 liver and 24 cardiothoracic recipients were registered on the UK and Republic of Ireland national transplant waiting list to receive a second or subsequent transplant. These patients represented 20%, 9% and 4% of the total separate waiting lists, respectively. This clearly demonstrates a continuing requirement for retransplantation. A study of the characteristics of first and retransplant recipients and their donors shows that retransplant recipients tend to be younger and in the case of liver and heart/lung transplants are less likely to be male. Survival of liver regrafts is significantly worse than first transplant survival. Multifactorial analysis of the factors affecting the outcome of kidney retransplants showed outcome to be associated with the survival time of the first transplant. In the case of kidney transplantation, the younger and the better matched recipients selected for retransplant may at least partially explain the comparable one-year transplant survival estimates for first and retransplants of 81% and 78%, respectively, for the 1986-1993 dataset of 23 centers.

Adolescent↗
PubMed2005

Potential use of routine databases in health technology assessment.

OBJECTIVES: To develop criteria for classifying databases in relation to their potential use in health technology (HT) assessment and to apply them to a list of databases of relevance in the UK. To explore the extent to which prioritized databases could pick up those HTs being assessed by the National Coordinating Centre for Health Technology Assessment (NCCHTA) and the extent to which these databases have been used in HT assessment. To explore the validation of the databases and their cost. DATA SOURCES: Electronic databases. Key literature sources. Experienced users of routine databases. REVIEW METHODS: A 'first principles' examination of the data necessary for each type of HT assessment was carried out, supplemented by literature searches and a historical review. The principal investigators applied the criteria to the databases. Comments of the 'keepers' of the prioritized databases were incorporated. Details of 161 topics funded by the NHS R&D Health Technology Assessment (HTA) programme were reviewed iteratively by the principal investigators. Uses of databases in HTAs were identified by literature searches, which included the title of each prioritized database as a keyword. Annual reports of databases were examined and 'keepers' queried. The validity of each database was assessed using criteria based on a literature search and involvement by the authors in a national academic network. The costs of databases were established from annual reports, enquiries to 'keepers' of databases and 'guesstimates' based on cost per record. For assessing effectiveness, equity and diffusion, routine databases were classified into three broad groups: (1) group I databases, identifying both HTs and health states, (2) group II databases, identifying the HTs, but not a health state, and (3) group III databases, identifying health states, but not an HT. Group I datasets were disaggregated into clinical registries, clinical administrative databases and population-oriented databases. Group III were disaggregated into adverse event reporting, confidential enquiries, disease-only registers and health surveys. RESULTS: Databases in group I can be used not only to assess effectiveness but also to assess diffusion and equity. Databases in group II can only assess diffusion. Group III has restricted scope for assessing HTs, except for analysis of adverse events. For use in costing, databases need to include unit costs or prices. Some databases included unit cost as well as a specific HT. A list of around 270 databases was identified at the level of UK, England and Wales or England (over 1000 including Scotland, Wales and Northern Ireland). Allocation of these to the above groups identified around 60 databases with some potential for HT assessment, roughly half to group I. Eighteen clinical registers were identified as having the greatest potential although the clinical administrative datasets had potential mainly owing to their inclusion of a wide range of technologies. Only two databases were identified that could directly be used in costing. The review of the potential capture of HTs prioritized by the UK's NHS R&D HTA programme showed that only 10% would be captured in these databases, mainly drugs prescribed in primary care. The review of the use of routine databases in any form of HT assessment indicated that clinical registers were mainly used for national comparative audit. Some databases have only been used in annual reports, usually time trend analysis. A few peer-reviewed papers used a clinical register to assess the effectiveness of a technology. Accessibility is suggested as a barrier to using most databases. Clinical administrative databases (group Ib) have mainly been used to build population needs indices and performance indicators. A review of the validity of used databases showed that although internal consistency checks were common, relatively few had any form of external audit. Some comparative audit databases have data scrutinised by participating units. Issues around coverage and coding have, in general, received little attention. NHS funding of databases has been mainly for 'Central Returns' for management purposes, which excludes those databases with the greatest potential for HT assessment. Funding for databases was various, but some are unfunded, relying on goodwill. The estimated total cost of databases in group I plus selected databases from groups II and III has been estimated at pound 50 million or around 0.1% of annual NHS spend. A few databases with limited potential for HT assessment account for the bulk of spending. CONCLUSIONS: Suggestions for policy include clarification of responsibility for the strategic development of databases, improved resourcing, and issues around coding, confidentiality, ownership and access, maintenance of clinical support, optimal use of information technology, filling gaps and remedying deficiencies. Recommendations for researchers include closer policy links between routine data and R&D, and selective investment in the more promising databases. Recommended research topics include optimal capture and coding of the range of HTs, international comparisons of the role, funding and use of routine data in healthcare systems and use of routine database in trials and in modelling. Independent evaluations are recommended for information strategies (such as those around the National Service Frameworks and various collaborations) and for electronic patient and health records.

Biomedical Technology↗
PubMed2006

A database of unique protein sequence identifiers for proteome studies.

In proteome studies, identification of proteins requires searching protein sequence databases. The public protein sequence databases (e.g., NCBInr, UniProt) each contain millions of entries, and private databases add thousands more. Although much of the sequence information in these databases is redundant, each database uses distinct identifiers for the identical protein sequence and often contains unique annotation information. Users of one database obtain a database-specific sequence identifier that is often difficult to reconcile with the identifiers from a different database. When multiple databases are used for searches or the databases being searched are updated frequently, interpreting the protein identifications and associated annotations can be problematic. We have developed a database of unique protein sequence identifiers called Sequence Globally Unique Identifiers (SEGUID) derived from primary protein sequences. These identifiers serve as a common link between multiple sequence databases and are resilient to annotation changes in either public or private databases throughout the lifetime of a given protein sequence. The SEGUID Database can be downloaded (http://bioinformatics.anl.gov/SEGUID/) or easily generated at any site with access to primary protein sequence databases. Since SEGUIDs are stable, predictions based on the primary sequence information (e.g., pI, Mr) can be calculated just once; we have generated approximately 500 different calculations for more than 2.5 million sequences. SEGUIDs are used to integrate MS and 2-DE data with bioinformatics information and provide the opportunity to search multiple protein sequence databases, thereby providing a higher probability of finding the most valid protein identifications.

Amino Acid Sequence↗
PubMed2007

GI Epidemiology: databases for epidemiological studies.

BACKGROUND: Large databases are being increasingly used for examining the epidemiology and outcomes of digestive and liver disorders. The complexity and rigor of the methods used to conduct these studies are often underestimated. AIMS: For the most commonly used databases, we provide a brief description of the contents, highlight strengths and weakness, and provide links for more detailed information. We also present a systematic approach to utilizing large databases for addressing research questions, highlighting commonly encountered study design issues, as well as strategies for resolving these issues. CONCLUSIONS: 1. Research using large databases requires the same essential skills needed to conduct research studies using other data sources. These include a rigorous study design, expertise in analytic methods, and relevant research questions. 2. The completeness and accuracy of information contained in the database must be assessed. Methods for improving the quality and completeness of this information should be considered. 3. Despite similarities among large databases, gaining insight and experience into the structure and content of each database is essential. Key points *Large databases can be a powerful source of information to examine the clinical epidemiology and outcomes of digestive and liver disorders. * Research using large databases requires the same essential skills needed to conduct research studies using other data sources. These include a rigorous study design, expertise in analytic methods, and relevant research questions. * The completeness and accuracy of information contained in the database must be assessed. Methods for improving the quality and completeness of this information should be considered. * Despite similarities among large databases, gaining insight and experience into the structure and content of each database is essential. * Examples of commonly used large databases are presented with a synopsis of information contained in the database, as well as strengths and limitations of using the database for research.

Databases as Topic↗
PubMed2005

The Molecular Biology Database Collection: 2005 update.

The Nucleic Acids Research Molecular Biology Database Collection is a public online resource that lists the databases described in this and previous issues of Nucleic Acids Research together with other databases of value to the biologist and available throughout the world. All databases included in this Collection are freely available to the public. The 2005 update includes 719 databases, 171 more than the 2004 one. The databases are organized in a hierarchical classification that simplifies the process of finding the right database for any given task. The growing number of databases related to immunology, plant and organelle research have been accommodated by separating them into three new categories. The database summaries provide brief descriptions of the databases, contact details, appropriate references and acknowledgements. The online summaries also serve as a venue for the maintainers of each database to introduce database updates and other improvements in the scope and tools. These updates are particularly important for those databases that have not been described in print in the recent past. The database list and summaries are available online at the Nucleic Acids Research web site, http://nar.oupjournals.org/.

Allergy and Immunology↗
PubMed2006

BioWarehouse: a bioinformatics database warehouse toolkit.

BACKGROUND: This article addresses the problem of interoperation of heterogeneous bioinformatics databases. RESULTS: We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. CONCLUSION: BioWarehouse embodies significant progress on the database integration problem for bioinformatics.

Computational Biology↗
PubMedDate not supplied

Development of a database of patients supported by ventricular assist devices.

An increase in the number of patients with end stage heart failure is leading to increased use of ventricular assist devices (VAD). However, sometimes the optimal time point for implantation of left ventricular or biventricular support remains unclear. Data analysis using an electronic database may help to make the decision making process more precise and thus improve outcome. However, it is not easy to find a balance between sufficient comprehensiveness of the data, which are selected from a huge amount of available information, and practicability of database maintenance and data analysis. We developed the Assist Database based on Access for Windows. The Assist Database consists of five main parts: (1) demographic and admission data, diagnosis, goal, and type of VAD; (2) preoperative period; (3) postoperative period up to 30 days; (4) follow-up period; and (5) statistical evaluation. The preoperative and postoperative parts include hemodynamic data; ventilatory support; laboratory results; results from echocardiographic, neurologic, pathologic, and other examinations; medication; and complications. The follow-up part documents readmissions, complications, and outcome. From April 1987 to October 2002, eight different types of VAD were implanted in 654 patients in our institution. Their data were retrospectively added to the Assist Database using medical records and different previously used electronic databases. Since the Assist Database came into routine use, it has been supplied daily with selected data of current patients. On the data entry level, the data arising from medical records are entered either manually via standard forms or automatically from other electronic documentation systems used in our hospital in routine patient care to collect laboratory results, demographic data, blood transfusion data, and operative data and from electronic patient charts via interfaces. The structure of the database is designed to facilitate the data analysis level. The database presented is one of three databases united to form a network. The structure of the Assist Database facilitates comprehensive, time saving data collection, which allows different online data analyses. These analyses may affect the decision making process and thus improve outcome. However, achieving a balance between the volume of available information, the time consumed, and the relevance of the data for further analysis remains difficult. The Assist Database should include information relevant for the decision making process and for the prediction of outcome. In particular, data collection should be focused on patients' preoperative condition and on postoperative organ function and quality of life. Further, different databases (for patients with congestive heart failure, assist device patients, and transplanted patients) should be unified to form a network to avoid the repeated collection of identical data, to save time, and to increase the quality of analysis. In the long-term, multicenter use of the Assist Database could be considered.

Data Collection↗
PubMed1998

A metadata approach to query interoperation between molecular biology databases.

MOTIVATION: Molecular biology databases have been proliferating rapidly. Their heterogeneity and complexity pose a great challenge to efforts in database interoperation. To minimize the efforts of interoperating heterogeneous databases, it is useful to develop a system that lets a user of a particular genomic database access another related database as if the latter is structurally similar to the former. RESULTS: We extend a structurally simple model-the entity-attribute-value (EAV) model-to describe uniformly metadata relating to individual databases. Such metadata, which are necessary for performing database comparisons, include descriptions of primitive database objects (including entities, attributes, domain values and entity relationships) and specification of correspondences among the database objects. We show how to decompose SQL queries and map them from one database to another based on the EAV representation of the basic database objects. A prototype system is implemented to demonstrate query interoperation between two chromosome map databases. AVAILABILITY: Freely available (Cold Fusion source code and an Access database containing the mapping knowledge) upon request from the author. CONTACT: kei.cheung@yale.edu

Chromosome Mapping↗
PubMed2001

The development of a prototype database for the voluntary reporting of occupational exposure data on chemicals.

A prototype occupational exposure database was developed as part of a study to retrospectively collect chemical exposure data from U.K. industry. The data dictionary for the database was constructed using existing recommendations on core data elements developed by working groups from the ACGIH and the European Union. The study also made use of existing job and workplace coding schemes. The practicalities of gathering the data by voluntary donation, its storage in a database, and the transfer of suitably anonymised data to the U.K. Health and Safety Executive's National Exposure Database system were investigated and assessed. Prior to the development, several existing exposure database systems were evaluated for their suitability to store the data from the study. Though of high quality, these were found to be insufficiently flexible for the diversity of datasets encountered and so the prototype exposure database was constructed using a leading database development package. The database was successfully used to gather data and forward it in a suitable format to the U.K. Health and Safety Executive. The published recommendations on occupational exposure databases and the associated coding schemes provided a very useful foundation for designing and implementing the prototype database. However, as data collection proceeded it became clear that the existing recommendations often were poorly understood and misinterpreted, or at least interpreted differently, by different database designers, data collectors, and other users of occupational exposure data. It is suggested that several items in the ACGIH and European Union core recommendations are ambiguous and need to be clarified. Once agreed, the improved database design criteria need to be widely promoted to foster a common understanding and to encourage their use by all those involved in collecting occupational exposure data. Beyond this, recommendations for exposure databases should be augmented to facilitate easy exchange of data between organizations.

Databases, Factual↗
PubMed1996

A protein class database organized with ProSite protein groups and PIR superfamilies.

A protein class (ProClass) database is developed as a "value-added" "second-generation" database organized according to family relationships. The database collects non-redundant protein sequence entries from SwissProt and PIR databases, and classifies them in families defined collectively by the ProSite protein groups and PIR superfamilies. The major objectives of the database are to maximize family information retrieval, to provide speedy family identification, and to help organizing existing protein sequence databases. The database has two sub-databases: PCFam (ProClass Family) to define protein families and provide links to ProSite patterns and PIR superfamilies, and PCSeq (ProClass Sequence) to describe sequence entries and provide links to PCFam, SwissProt, PIR, and ProSite databases. The current ProClass release has a total of 85,165 sequence entries, about half of which are classified in 3072 ProClass families; it also contains 10,431 newly established SwissProt-PIR links. The database can help reveal domain structures of related families, define new ProSite and PIR families, and provide family assignments for unclassified sequence entries. New ProSite and PIR family members are readily identified via database cross-reference, including 9437 SwissProt entries and 8522 PIR entries. False negative family members missed by both ProSite and PIR are detected using a neural network family identification system. The newly identified superfamily memberships are being incorporated into the current PIR database releases in a collaborative effort with the PIR. The ProClass database is accessible through anonymous FTP and on-line search on the World Wide Web.

Amino Acid Sequence↗
PubMed2002

[A study of forensic DNA databases].

OBJECTIVE: The forensic DNA databases are very important for individual identification. In order to evaluate the genetic markers used for a forensic DNA databases and the compatibility between the manual DNA typing system and the automatic DNA typing system, a testing DNA database should be constructed. Also, constructing a testing DNA database can increase our understanding of the issue for forensic DNA databases. METHODS: A total of 1000 specimens, including samples of blood, blood stains, salvia stains, semen stains, mixture stains and muscle tissues, were collected from the public security bureau of Chengdu. The DNA of each specimen was extracted by Chelex method and analyzed using Amp-FLP technique. A total of 8 STR loci, including D3S1358, D9S1118, vWA, D5S818, D16S539, D8S1179, CSF1PO and D20S161 were chosen and employed for DNA typing. Each STR locus was amplified by the polymerase chain reaction PCR and the PCR products were typed with the polyacryamide gel electrophoresis. Typing DNA was carried out by comparing with a human allele ladder. A total of 8 human allele ladders for D3S1358, D9S1118, vWA, D5S818, D16S539, D8S1179, CSF1PO and D20S161 were made in-house. Managing software of the testing DNA database was designed using Microsoft Access. RESULTS: The results of DNA typing in 1000 specimens showed that the total discrimination power of 8 STR loci was over 0.99999999. CONCLUSION: This study show that a forensic DNA database should be useful for search purpose. The total discrimination power over 0.99999999 imply that in principle there is no identical genotype at whole 8 STR loci between two persons from a population with 10000000 individuals. This means that 8 STR loci used in this study are suitable to construct forensic DNA databases in Chengdu of China. The result of DNA typing can be repeated and the data have compatibility between the manual DNA typing system and the automatic DNA typing system. The data search in our testing DNA database can be carried out using only some loci of the set of 8 STR markers. Also, the volume of our testing DNA databases could be enlarged easily. The implication from this study is that the legislation should not be negligent before establishing a forensic DNA database. This DNA database provides a model for establishing the forensic DNA databases in China.

Adolescent↗