Improving the Diagnosis of Acute Heart Failure Using a Validated Prediction Model

ObjectivesWe sought to derive and validate a prediction model by using N-terminal pro–B-type natriuretic peptide (NT-proBNP) and clinical variables to improve the diagnosis of acute heart failure (AHF).BackgroundThe optimal way of using natriuretic peptides to enhance the diagnosis of AHF remains uncertain.MethodsPhysician estimates of probability of AHF in 500 patients treated in the emergency department from the multicenter IMPROVE CHF (Improved Management of Patients With Congestive Heart Failure) trial recruited between December 2004 and December 2005 were classified into low (0% to 20%), intermediate (21% to 79%), or high (80% to 100%) probability for AHF and then compared with the blinded adjudicated AHF diagnosis. Likelihood ratios were calculated and multiple logistic regression incorporated covariates into an AHF prediction model that was validated internally by the use of bootstrapping and externally by applying the model to another 573 patients from the separate PRIDE (N-Terminal Pro-BNP Investigation of Dyspnea in the Emergency Department) study of the use of NT-proBNP in patients with dyspnea.ResultsLikelihood ratios for AHF with NT-proBNP were 0.11 (95% confidence interval [CI]: 0.06 to 0.19) for cut-point values <300 pg/ml; increasing to 3.43 (95% CI: 2.34 to 5.03) for values 2,700 to 8,099 pg/ml, and 12.80 (95% CI: 5.21 to 31.45) for values ≥8,100 pg/ml. Variables used to predict AHF were age, pre-test probability, and log NT-proBNP. When applied to the external data by use of its adjudicated final diagnosis as the gold standard, the model appropriately reclassified 44% of patients by intermediate clinical probability to either low or high probability of AHF with negligible (<2%) inappropriate redirection.ConclusionsA diagnostic prediction model for AHF that incorporates both clinical assessment and NT-proBNP has been derived and validated and has excellent diagnostic accuracy, especially in cases with indeterminate likelihood for AHF

Diabetes in pregnancy among indigenous women in Australia, Canada, New Zealand, and the United States: a method for systematic review of studies with different designs

<p>Abstract</p> <p>Background</p> <p>Diabetes in pregnancy, which includes gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM), is associated with poor outcomes for both mother and infant during pregnancy, at birth and in the longer term. Recent international guidelines recommend changes to the current GDM screening criteria. While some controversy remains, there appears to be consensus that women at high risk of T2DM, including indigenous women, should be offered screening for GDM early in pregnancy, rather than waiting until 24-28 weeks as is current practice. A range of criteria should be considered before changing screening practice in a population sub-group, including: prevalence, current practice, acceptability and whether adequate treatment pathways and follow-up systems are available. There are also specific issues related to screening in pregnancy and indigenous populations. The evidence that these criteria are met for indigenous populations is yet to be reported. A range of study designs can be considered to generate relevant evidence for these issues, including epidemiological, observational, qualitative, and intervention studies, which are not usually included within a single systematic review. The aim of this paper is to describe the methods we used to systematically review studies of different designs and present the evidence in a pragmatic format for policy discussion.</p> <p>Methods/Design</p> <p>The inclusion criteria will be broad to ensure inclusion of the critical perspectives of indigenous women. Abstracts of the search results will be reviewed by two persons; the full texts of all potentially eligible papers will be reviewed by one person, and 10% will be checked by a second person for validation. Data extraction will be standardised, using existing tools to identify risks for bias in intervention, measurement, qualitative studies and reviews; and adapting criteria for appraising risk for bias in descriptive studies. External validity (generalisability) will also be appraised. The main findings will be synthesised according to the criteria for population-based screening and summarised in an adapted "GRADE" tool.</p> <p>Discussion</p> <p>This will be the first systematic review of all the published literature on diabetes in pregnancy among indigenous women. The method provides a pragmatic approach for synthesizing relevant evidence from a range of study designs to inform the current policy discussion.</p

Data Curation and Distribution in Support of Cornell University's Upper Susquehanna Agricultural Ecology Program

Paper presented at DigCCurr2007, an international symposium on Digital Curation. Held April 18-20, 2007 at the University of North Carolina, Chapel Hill NC.Effective documentation, curation, and provision of access to scientific data are essential to derive the full benefit of research data, both for participants in specific research projects and for the entire scientific community. Academic research libraries are positioned to be important partners in such endeavors, although success will depend in part on expanding and changing the customary roles of, and relationships between, researchers and libraries. Cornell University's Albert R. Mann Library is collaborating with the Upper Susquehanna Agricultural Ecology Program at Cornell to document and distribute the group's research data. In addition to collecting data and developing numeric and spatial models, the research group has access to approximately thirty years worth of observational data for their research sites, which are of significant value to environmental scientists. The approach includes identifying and using discipline-specific metadata standards in order to facilitate participation in discipline-specific data and metadata sharing initiatives, at the discretion of individual researchers. Training is provided for project collaborators in the use of existing metadata creation tools to create documentation for their datasets. ?Pre-publication? data and metadata are stored in a database accessible only by project members, to facilitate early sharing and collaboration within the group. Complete, documented data sets and complete metadata records will then be deposited in Cornell's DSpace installation. As a test case, the historic data sets are being formatted and documented for deposit in DSpace. A public web portal provides information about the project and participants, as well as a future means of access to project datasets.National Science Foundation grant number 0437603 to Janet McCue and Barbara Lus

DataStaR: Using the Semantic Web approach for Data Curation

Paper presented at the 6th International Conference on Digital Curation. December 6-8, 2010. Chicago, IL.In disciplines as varied as medicine, social sciences, and economics, data and its analysis are an essential part of researchers’ contributions to their respective fields. While sharing research data for review and analysis presents new opportunities for furthering research, capturing this data in digital form and providing the digital infrastructure for sharing data and metadata pose several challenges. This paper reviews the motivations behind and design of the Data Staging Repository (DataStaR) platform that targets specific portions of the research data curation lifecycle (Higgins, 2008): data and metadata capture and sharing prior to publication and publication to permanent archival repositories. The goal of DataStaR is to support both the sharing and publishing of data while at the same time enabling metadata creation without imposing additional overhead for researchers and librarians (Steinhart, 2010). Furthermore, DataStaR is intended to provide cross-disciplinary support by being able to integrate different domain-specific metadata schemas according to researchers’ needs. DataStaR’s strategy of a usable interface coupled with metadata flexibility allows for a more scaleable solution for data sharing, publication and metadata reuse.This material is based upon work supported by the National Science Foundation under Grant No. III- 0712989. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

DataStaR: Science Metadata Schemas Meet the Semantic Web

Brian Caruso, Brian Lowe, and Gail Steinhart will present on the DataStaR project, which is building a Data Staging Repository to help scientists describe their research data and publish to discipline-based or institutional repositories. The team will explain the purpose and function of the staging repository, with emphasis on the metadata editor and infrastructure. DataStaR stores metadata using the Resource Description Framework (RDF) and Web Ontology Language (OWL) while producing schema-compliant XML metadata files for deposition in today's repositories. The goal is to build a system that anticipates widespread adoption of Semantic Web technologies while remaining compatible with existing science metadata initiatives.This material is based upon work supported by the National Science Foundation under Grant No. III-0712989. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

SGER: Planning Information Infrastructure Through a New Library-Research Partnership [final report]

document contains most significant sections of a final report to NSF submitted 2007-01-21.The purpose of this Small Grant for Exploratory Research was to explore the issues surrounding a new type of collaboration between scientists and research libraries to support the preservation, discovery, and sharing of primary research data. With recent advances in computing and telecommunications technology, the stage is set for a major shift in the way science is conducted. Researchers and funding agencies are recognizing that data can be valuable for purposes beyond the studies in which they were originally collected, and some agencies are requiring data sharing plans as prerequisites for funding support. There is, however, a lack of established infrastructure to support the services necessary for handling research data. This grant investigated the premise that research libraries might serve as natural partners in addressing the data management needs of the communities they serve.This material is based upon work supported by the National Science Foundation, Grant No. 0437603. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

DataStaR: Using the Semantic Web approach for Data Curation

In disciplines as varied as medicine, social sciences, and economics, data and their analyses are essential parts of researchers’ contributions to their respective fields. While sharing research data for review and analysis presents new opportunities for furthering research, capturing these data in digital forms and providing the digital infrastructure for sharing data and metadata pose several challenges. This paper reviews the motivations behind and design of the Data Staging Repository (DataStaR) platform that targets specific portions of the research data curation lifecycle: data and metadata capture and sharing prior to publication, and publication to permanent archival repositories. The goal of DataStaR is to support both the sharing and publishing of data while at the same time enabling metadata creation without imposing additional overheads for researchers and librarians. Furthermore, DataStaR is intended to provide cross-disciplinary support by being able to integrate different domain-specific metadata schemas according to researchers’ needs. DataStaR’s strategy of a usable interface coupled with metadata flexibility allows for a more scaleable solution for data sharing, publication, and metadata reuse

Abstract Data Curation and Distribution in Support of Cornell University&apos;s Upper Susquehanna Agricultural Ecology Program

Effective documentation, curation, and provision of access to scientific data are essential to derive the full benefit of research data, both for participants in specific research projects and for the entire scientific community. Academic research libraries are positioned to be important partners in such endeavors, although success will depend in part on expanding and changing the customary roles of, and relationships between, researchers and libraries. Cornell University’s Albert R. Mann Library is collaborating with the Upper Susquehanna Agricultural Ecology Program at Cornell to document and distribute the group’s research data. In addition to collecting data and developing numeric and spatial models, the research group has access to approximately thirty years worth of observational data for their research sites, which are of significant value to environmental scientists. The approach includes identifying and using discipline-specific metadata standards in order to facilitate participation in discipline-specific data and metadata sharing initiatives, at the discretion of individual researchers. Training is provided for project collaborators in the use of existing metadata creation tools to create documentation for their datasets. “Pre-publication ” data and metadata are stored in a database accessible only by project members, to facilitate early sharing and collaboration within the group