CORE ABSTRACT The overall goal of the Data Management and Biostatistics Core (DMBC) is to provide a central source of expertise related to data management and biostatistical analyses for the Sepsis and Critical Illness Research Center (SCIRC). The DMBC will use the SCIRC Database currently implemented at UF Health that includes data from the computerized clinical decision support system for sepsis patients also currently implemented at UF Health, clinical data from the electronic medical record (EPIC) and research data from the research process and biological analyses. The DMBC will also coordinate and integrate efficient and consistent utilization of data and statistical resources across all the projects by providing standard methodology and staff support that are readily accessible to investigators. It will also ensure that all statistical analyses are of high quality and yield valid results. The following are the five specific aims:
Specific Aim #1 : to provide investigators with biostatistical expertise in pre-study and pre-proposal study design and implementation of appropriate statistical methodology to analyze the data.
Specific Aim #2 : to design and implement an integrated data management system that integrates clinical data from electronic medical records with all research-generated data.
Specific Aim #3 : to coordinate efficient and consistent utilization of data and statistical resources across the projects by providing standard methodology and expert staff support that are readily accessible to all investigators.
Specific Aim #4 : to provide expert support for all interim reviews and reporting of data including the development of new statistical methodology if needed to analyze data generated by projects.
Specific Aim #5 : to facilitate dissemination of findings by writing appropriate sections of manuscripts and publishing new methodological developments. The DMBC will provide the technical expertise for the continued development and refinement of a Sepsis and Critical Illness Research Center (SCIRC) database which combines automated, electronic medical record clinical data recovery with validated manual data entry. In addition, the Core provides ongoing oversight of study design and compliance, data collection, transfer and validation, and appropriate statistical analysis and interpretation. Finally, the Core assists with data presentation, manuscript completion and dissemination of the Program products to the greater scientific community.
|Mira, Juan C; Nacionales, Dina C; Loftus, Tyler J et al. (2018) Mouse Injury Model of Polytrauma and Shock. Methods Mol Biol 1717:1-15|
|Loftus, Tyler J; Brakenridge, Scott C; Murphy, Travis W et al. (2018) Anemia and blood transfusion in elderly trauma patients. J Surg Res 229:288-293|
|Sartelli, Massimo; Kluger, Yoram; Ansaloni, Luca et al. (2018) Raising concerns about the Sepsis-3 definitions. World J Emerg Surg 13:6|
|Loftus, Tyler J; Rosenthal, Martin D; Croft, Chasen A et al. (2018) Effect of Time to Operation on Value of Care in Acute Care Surgery. World J Surg 42:2356-2363|
|Loftus, Tyler J; Efron, Philip A; Bala, Trina M et al. (2018) The impact of standardized protocol implementation for surgical damage control and temporary abdominal closure after emergent laparotomy. J Trauma Acute Care Surg :|
|Loftus, Tyler J; Mira, Juan C; Stortz, Julie A et al. (2018) Persistent Inflammation and Anemia among Critically Ill Septic Patients. J Trauma Acute Care Surg :|
|Efron, Philip A; Mohr, Alicia M; Bihorac, Azra et al. (2018) Persistent inflammation, immunosuppression, and catabolism and the development of chronic critical illness after surgery. Surgery 164:178-184|
|Rosenthal, Martin D; Kamel, Amir Y; Rosenthal, Cameron M et al. (2018) Chronic Critical Illness: Application of What We Know. Nutr Clin Pract 33:39-45|
|Loftus, Tyler J; Mohr, Alicia M; Moldawer, Lyle L (2018) Dysregulated myelopoiesis and hematopoietic function following acute physiologic insult. Curr Opin Hematol 25:37-43|
|Bihorac, Azra; Ozrazgat-Baslanti, Tezcan; Ebadi, Ashkan et al. (2018) MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery. Ann Surg :|
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