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DDIT4 gene encodes a protein whose main action is to inhibit mTOR under stress conditions whilst several in vitro studies indicate that its expression favors cancer progression. We have previously described that DDIT4 expression is an independent prognostic factor for tripe negative breast cancer resistant to neoadjuvant chemotherapy. We herein report that high DDIT4 expression is related to the outcome (recurrence-free survival, time to progression and overall survival) in several cancer types. We performed in silico analysis in online platforms, in pooled datasets from KM Plotter and meta-analysis of individual datasets from SurvExpress. High levels of DDIT4 were significantly associated with a worse prognosis in acute myeloid leukemia, breast cancer, glioblastoma multiforme, colon, skin and lung cancer. Conversely, a high DDIT4 expression was associated with an improved prognostic in gastric cancer. DDIT4 was not associated with the outcome of ovarian cancers. Analysis with data from the Cell Miner Tool in 60 cancer cell lines indicated that although rapamycin activity was correlated with levels of MTOR, it is not influenced by DDIT4 expression. In summary, DDIT4 might serve as a novel prognostic biomarker in several malignancies. DDIT4 activity could be responsible for resistance to mTOR inhibitors and is a potential candidate for the development of targeted therapy.
The Electronic Medical Records and Genomics (eMERGE) Network is a national consortium that is developing methods and best practices for using the electronic health record (EHR) for genomic medicine and research. We conducted a multi-site survey of information resources to support integration of pharmacogenomics into clinical care. This work aimed to: (a) characterize the diversity of information resource implementation strategies among eMERGE institutions; (b) develop a master template containing content topics of important for genomic medicine (as identified by the DISCERN-Genetics tool); and (c) assess the coverage of content topics among information resources developed by eMERGE institutions. Given that a standard implementation does not exist and sites relied on a diversity of information resources, we identified a need for a national effort to efficiently produce sharable genomic medicine resources capable of being accessed from the EHR. We discuss future areas of work to prepare institutions to use infobuttons for distributing standardized genomic content.
In clinical notes, physicians commonly describe reasons why certain treatments are given. However, this information is not typically available in a computable form. We describe a supervised learning system that is able to predict whether or not a treatment relation exists between any two medical concepts mentioned in clinical notes. To train our prediction model, we manually annotated 958 treatment relations in sentences selected from 6,864 discharge summaries. The features used to indicate the existence of a treatment relation between two medical concepts consisted of lexical and semantic information associated with the two concepts as well as information derived from the MEDication Indication (MEDI) resource and SemRep. The best F1-measure results of our supervised learning system (84.90) were significantly better than the F1-measure results achieved by SemRep (72.34).
BACKGROUND - We developed and validated an automated database case definition for diabetes in children and youth to facilitate pharmacoepidemiologic investigations of medications and the risk of diabetes.
METHODS - The present study was part of an in-progress retrospective cohort study of antipsychotics and diabetes in Tennessee Medicaid enrollees aged 6-24 years. Diabetes was identified from diabetes-related medical care encounters: hospitalizations, outpatient visits, and filled prescriptions. The definition required either a primary inpatient diagnosis or at least two other encounters of different types, most commonly an outpatient diagnosis with a prescription. Type 1 diabetes was defined by insulin prescriptions with at most one oral hypoglycemic prescription; other cases were considered type 2 diabetes. The definition was validated for cohort members in the 15 county region geographically proximate to the investigators. Medical records were reviewed and adjudicated for cases that met the automated database definition as well as for a sample of persons with other diabetes-related medical care encounters.
RESULTS - The study included 64 cases that met the automated database definition. Records were adjudicated for 46 (71.9%), of which 41 (89.1%) met clinical criteria for newly diagnosed diabetes. The positive predictive value for type 1 diabetes was 80.0%. For type 2 and unspecified diabetes combined, the positive predictive value was 83.9%. The estimated sensitivity of the definition, based on adjudication for a sample of 30 cases not meeting the automated database definition, was 64.8%.
CONCLUSION - These results suggest that the automated database case definition for diabetes may be useful for pharmacoepidemiologic studies of medications and diabetes.
Cytobank is a Web-based application for storage, analysis, and sharing of flow cytometry experiments. Researchers use a Web browser to log in and use a wide range of tools developed for basic and advanced flow cytometry. In addition to providing access to standard cytometry tools from any computer, Cytobank creates a platform and community for developing new analysis and publication tools. Figure layouts created on Cytobank are designed to allow transparent access to the underlying experiment annotation and data processing steps. Since all flow cytometry files and analysis data are stored on a central server, experiments and figures can be viewed or edited by anyone with the proper permission, from any computer with Internet access. Once a primary researcher has performed the initial analysis of the data, collaborators can engage in experiment analysis and make their own figure layouts using the gated, compensated experiment files. Cytobank is available to the scientific community at http://www.cytobank.org.
(c) 2010 by John Wiley & Sons, Inc.
BACKGROUND - Managing change has not only been recognized as an important topic in medical informatics, but it has become increasingly important in translational informatics. The move to share data, together with the increasing complexity and volume of the data, has precipitated a transition from locally stored worksheet and flat files to relational data bases with object oriented interfaces for data storage and retrieval. While the transition from simple to complex data structures, mirroring the transition from simple to complex experimental technologies, seems natural, the human factor often fails to be adequately addressed leading to failures in managing change.
METHODS - We describe here a case study in change management applied to an application in translational informatics that touches upon changes in hardware, software, data models, procedures, and terminology standards. We use the classic paper by Riley and Lorenzi to dissect the problems that arose, the solutions that were implemented, and the lessons learned.
RESULTS - The entire project from requirements gathering through completion of migration of the system took three years. Double data entry into the old and new systems persisted for six months. Contributing factors hindering progress and solutions to facilitate managing the change were identified in seven of the areas identified by Riley and Lorenzi: communications, cultural changes in work practice, scope creep, leadership and organizational issues, and training.
CONCLUSIONS - Detailed documentation of the agreed upon requirements for the new system along with ongoing review of the sources of resistance to change as defined by Riley and Lorenzi were the most important steps taken that contributed to the success of the project. Cultural changes in tissue collection mandated by standards requirements introduced by the Cancer Bioinformatics Grid (CaBIG) and excessive reliance on the outgoing system during a lengthy period of dual data entry were the primary sources of resistance to change.
BACKGROUND - Risk factors for the development of acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU) include positive fluid balance, high tidal volumes (TVs), high airway pressures, and transfusion of blood products. However, research examining intraoperative factors such as fluid resuscitation, mechanical ventilation strategies, and blood administration on the postoperative development of ARDS is lacking.
METHODS - We assessed patients admitted to the ICU with postoperative hypoxemic respiratory failure requiring mechanical ventilation for the development of ARDS in the first 7 postoperative days using established clinical and radiological criteria. Data on risk factors for ARDS were obtained from the electronic anesthetic and medical records. Logistic regression was used to examine the independent association between fluid resuscitation, TV per ideal body weight, and number of blood products transfused during surgery and the postoperative development of ARDS, adjusting for important clinical covariates.
RESULTS - Of the 89 patients with postoperative respiratory failure, 25 developed ARDS. Compared with those who received <10 mL/kg/h fluid resuscitation in the operating room, patients receiving >20 mL/kg/h fluid resuscitation had a 3.8 times higher adjusted odds of developing ARDS (P = 0.04), and those receiving 10 to 20 mL/kg/h had a 2.4 times higher adjusted odds of developing ARDS (P = 0.14). TV per ideal body weight and the number of blood units transfused were not associated with ARDS development in this study.
CONCLUSIONS - This cohort study provides evidence to suggest a relationship between intraoperative fluid resuscitation and the development of ARDS. Larger prospective trials are required to confirm these findings.
BACKGROUND AND OBJECTIVES - Randomized, controlled trials (RCTs) are the gold standard for defining causal inferences but are sometimes not feasible because of cost, ethical, or time considerations. We explored the accuracy and potential use of a "simulated trial" through the modeling of a previously published RCT, Die Deutsche Diabetes Dialyse Studie (4D Study), a landmark study that investigated the cardiovascular benefit of atorvastatin use in 1255 patients with ESRD.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS - Using a large historical database of interventions and outcomes in dialysis patients, we conducted an observational model of the 4D Study in dialysis patients who had type 2 diabetes and were prescribed a statin (5144 patients) and matched to a non-statin user (5144 control subjects) before multivariate modeling. Inclusion, exclusion, and outcome parameters of the study, as prespecified by the 4D Study, were strictly modeled in this analysis.
RESULTS - In covariate- and propensity-adjusted Cox regression, statin use (versus nonuse) was associated with a decrease in the composite primary outcome of cardiac death, nonfatal myocardial infarction, and stroke. Statin use was also associated with a decrease in cardiovascular mortality and all cardiac events combined. The hazard ratios in this observational model were numerically comparable to the hazard ratios reported in the 4D Study; however, because of the larger number of patients "enrolled," results in this simulated study achieved statistical significance.
CONCLUSIONS - Statin use was associated with some cardiovascular benefit in a simulated trial of patients with ESRD; however, the size of benefit was considerably smaller than that seen in the general population. Such simulated trials may represent an exploratory, cost-effective option when RCTs are not immediately feasible.