scholarly journals Do vascular risk factors contribute to the prevalence of pressure ulcer in veterans with spinal cord injury?

2011 ◽  
Vol 34 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Meheroz Hoshang Rabadi ◽  
Andrea S. Vincent
2012 ◽  
Vol 19 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Patricia Wilczweski ◽  
Dawnetta Grimm ◽  
Anastasia Gianakis ◽  
Bridget Gill ◽  
Wendy Sarver ◽  
...  

Spinal Cord ◽  
2009 ◽  
Vol 47 (9) ◽  
pp. 651-661 ◽  
Author(s):  
A Gélis ◽  
A Dupeyron ◽  
P Legros ◽  
C Benaïm ◽  
J Pelissier ◽  
...  

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 651-658
Author(s):  
Kath M Bogie ◽  
Steven K Roggenkamp ◽  
Ningzhou Zeng ◽  
Jacinta M Seton ◽  
Katelyn R Schwartz ◽  
...  

ABSTRACT Background Pressure injuries (PrI) are serious complications for many with spinal cord injury (SCI), significantly burdening health care systems, in particular the Veterans Health Administration. Clinical practice guidelines (CPG) provide recommendations. However, many risk factors span multiple domains. Effective prioritization of CPG recommendations has been identified as a need. Bioinformatics facilitates clinical decision support for complex challenges. The Veteran’s Administration Informatics and Computing Infrastructure provides access to electronic health record (EHR) data for all Veterans Health Administration health care encounters. The overall study objective was to expand our prototype structural model of environmental, social, and clinical factors and develop the foundation for resource which will provide weighted systemic insight into PrI risk in veterans with SCI. Methods The SCI PrI Resource (SCI-PIR) includes three integrated modules: (1) the SCIPUDSphere multidomain database of veterans’ EHR data extracted from October 2010 to September 2015 for ICD-9-CM coding consistency together with tissue health profiles, (2) the Spinal Cord Injury Pressure Ulcer and Deep Tissue Injury Ontology (SCIPUDO) developed from the cohort’s free text clinical note (Text Integration Utility) notes, and (3) the clinical user interface for direct SCI-PIR query. Results The SCI-PIR contains relevant EHR data for a study cohort of 36,626 veterans with SCI, representing 10% to 14% of the U.S. population with SCI. Extracted datasets include SCI diagnostics, demographics, comorbidities, rurality, medications, and laboratory tests. Many terminology variations for non-coded input data were found. SCIPUDO facilitates robust information extraction from over six million Text Integration Utility notes annually for the study cohort. Visual widgets in the clinical user interface can be directly populated with SCIPUDO terms, allowing patient-specific query construction. Conclusion The SCI-PIR contains valuable clinical data based on CPG-identified risk factors, providing a basis for personalized PrI risk management following SCI. Understanding the relative impact of risk factors supports PrI management for veterans with SCI. Personalized interactive programs can enhance best practices by decreasing both initial PrI formation and readmission rates due to PrI recurrence for veterans with SCI.


PM&R ◽  
2017 ◽  
Vol 10 (6) ◽  
pp. 573-586 ◽  
Author(s):  
Kerstin Hug ◽  
Caroline Stumm ◽  
Isabelle Debecker ◽  
Carolina Saskia Fellinghauer ◽  
Claudio Peter ◽  
...  

2008 ◽  
Vol 31 (5) ◽  
pp. 551-559 ◽  
Author(s):  
Marylou Guihan ◽  
Susan Garber ◽  
Charles Bombardier ◽  
Barry Goldstein ◽  
Lishan Holmes ◽  
...  

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Yaga Szlachcic ◽  
Rodney H Adkins ◽  
Jamie C Reiter ◽  
Yanjie Li ◽  
Howard N Hodis

Introduction: Physical activity is presumed to improve cardiovascular disease (CVD), of which carotid artery intima-media thickness (CIMT) is a common indicator. Individuals with spinal cord injury (SCI) have limited mobility and therefore an expected increased risk for CVD. The purpose of this study was to determine which CVD risk factors predict CIMT among women with SCI, with the ultimate goal of targeting therapy to improve CVD in this population. Methods: One hundred twenty-two women with SCI who attended an outpatient SCI clinic and met inclusion and exclusion criteria participated in this study. SCI was categorized into 1 of 4 categories: complete tetraplegia, incomplete tetraplegia, complete paraplegia, and incomplete paraplegia. Maximum heart rate and VO2 max were obtained using bicycle ergometry with ventilatory gas exchange and continuous electrocardiogram. Hierarchical regression was used to predict CIMT, with the first block including demographic variables (age, race, smoking status) and the second block including physiologic variables (total cholesterol, heart rate, VO2 max, BMI, fasting serum glucose, hemoglobin A1c, and blood pressure). Results: Similar findings were observed for left and right CIMT, therefore only results from right CIMT are reported. The overall model was significant, F(16,46)=8.53, p=.000. Adjusted R square was .54 for the first block of variables and increased significantly (p=.006) to .66 when the second block of variables was added. Significant predictors at alpha=.05 included age (beta=.51, t=4.79, p=.000) and max/peak heart rate (beta=−.336, t=−2.39, p=.02). At alpha=.10, A1c was significant (beta=.187, t=1.99, p=.053). Conclusions: Although low aerobic conditioning is a purported CVD risk factor, quantitative measurements of such lack a demonstrable relationship with subclinical atherosclerosis (CIMT), perhaps because of its reduced importance relative to other CVD risk factors in a mobile population. We found expected relationships with CIMT in our SCI population (i.e., age), however we also found a quantitative measure of aerobic conditioning (max/peak heart rate) to be associated with CIMT. Our data indicate that SCI individuals may bear a greater CVD burden from cardiac de-conditioning than the general population and that investigation of a cohort with mobility limitation may provide a unique opportunity to study the impact of physical conditioning on CVD risk.


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