Related Experiment Video
Updated: Jan 19, 2026

Technical Refinement of a Bilateral Renal Ischemia-Reperfusion Mouse Model for Acute Kidney Injury Research
Published on: November 3, 2023
Refining Heel Pressure Injury Risk Factors in the Hospitalized Patient
Barbara Delmore1, Elizabeth A Ayello, Daniel Smith
1In New York, New York, Barbara Delmore, PhD, RN, CWCN, MAPWCA, IIWCC-NYU, is Senior Nurse Scientist, Center for Innovations in the Advancement of Care, and Clinical Assistant Professor, Hansjörg Wyss, Department of Plastic Surgery, NYU Langone Health; Elizabeth A. Ayello, PhD, MS, BSN, RN, CWON, ETN, MAPWCA, FAAN, is Faculty, Excelsior College, School of Nursing, and President, Ayello, Harris & Associates, Inc; Daniel Smith, MA, is Data Analyst and Research Coordinator, Center for Innovations in the Advancement of Care, NYU Langone Health; Linda Rolnitzky, MS, is a biostatistician; and Andy S. Chu, MS, RD, CDN, CNSC, FAND, is Registered Dietitian, Food and Nutrition Services, NYU Langone Health. Acknowledgments: The study was funded by Sage Products, LLC. The funding source was not involved in study design, monitoring, data collection, statistical analyses, interpretation of results, or manuscript writing. The authors have disclosed no other financial relationships related to this article. Submitted January 29, 2019; accepted in revised form April 3, 2019; published online ahead of print September 5, 2019.
Objective:
To replicate previous research that found four independent and significant predictors of heel pressure injuries (HPIs) in hospitalized patients using a larger and more diverse patient population.
Methods:
Researchers conducted a retrospective, case-control study with a main and a validation analysis (N = 1,937). The main analysis had 1,697 patients: 323 patients who had HPIs and 1,374 who did not. The validation analysis had 240 patients: 80 patients who developed HPIs and 160 who did not. Researchers used a series of diagnosis codes to define variables associated with an HPI. Data were extracted from the New York Statewide Planning and Research Cooperative System for January 2014 to June 2015. Study authors conducted a series of forward stepwise logistic regression analyses for both samples to select the variables that were significantly and independently associated with the development of an HPI in a multivariable setting. Researchers generated a receiver operating characteristic curve using the final model to assess the regression model's ability to predict HPI development.
Results:
Seven variables were significant and independent predictors associated with HPIs: diabetes mellitus, vascular disease, perfusion issues, impaired nutrition, age, mechanical ventilation, and surgery. The receiver operating characteristic curve demonstrated predictive accuracy of the model.
Conclusions:
Beyond a risk assessment scale, providers should consider other factors, such as comorbidities, which can predispose patients to HPI development.
Related Concept Videos
Factors Affecting the Risk of Infection
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
Hospitals-II
Nurses that work in...
03:13Technical Refinement of a Bilateral Renal Ischemia-Reperfusion Mouse Model for Acute Kidney Injury Research
06:51Mouse Model of Pressure Ulcers After Spinal Cord Injury
19:15Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Hospitals-I

