Related Experiment Video
Updated: Mar 30, 2026
Development of a Benchtop Model for Evaluating the Compatibility of Wound Dressing Materials with Negative Pressure Wound Therapy Systems
Published on: May 2, 2025
A predictive model for pressure ulcer outcome: the Wound Healing Index
Susan D Horn1, Ryan S Barrett, Caroline E Fife
1Susan D. Horn, PhD • Adjunct Professor • Department of Population Health Sciences Health System Innovation and Research Program • University of Utah School of Medicine • Salt Lake City • Senior Scientist • Institute for Clinical Outcomes Research • Salt Lake City, Utah Ryan S. Barrett, MStat • Quantitative Modeling Analyst • Zions Bancorporation • Salt Lake City, Utah • Senior Analyst • Institute for Clinical Outcomes Research • Salt Lake City, Utah Caroline E. Fife, MD • Chief Medical Officer • Intellicure, Inc • The Woodlands, Texas • Director • US Wound Registry • The Woodlands, Texas Brett Thomson, BS • Chief Information Officer • Intellicure, Inc • The Woodlands, Texas • Senior Analyst • US Wound Registry • The Woodlands, Texas.
Purpose:
The purpose of this learning activity is to provide information regarding the creation of a risk-stratification system to predict the likelihood of the healing of body and heel pressure ulcers (PrUs).
Target Audience:
This continuing education activity is intended for physicians and nurses with an interest in skin and wound care.
Objectives:
After participating in this educational activity, the participant should be better able to:1. Explain the need for a PrU risk stratification tool.2. Describe the purpose and methodology of the study.3. Delineate the results of the study and development of the Wound Healing Index.
Objective:
: To create a validated system to predict the healing likelihood of patients with body and heel pressure ulcers (PrUs), incorporating only patient- and wound-specific variables.
Design:
The US Wound Registry data were examined retrospectively and assigned a clear outcome (healed, amputated, and so on). Significant variables were identified with bivariate analyses. Multivariable logistic regression models were created based on significant factors (P < .05) and tested on a 10% randomly selected hold-out sample.
Setting:
Fifty-six wound clinics in 24 states
Patients:
: A total of 7973 body PrUs and 2350 heel PrUs were eligible for analysis.
Intervention:
Not applicable
Main Outcome Measure:
: Healed PrU MAIN RESULTS:: Because of missing data elements, the logistic regression development model included 6640 body PrUs, of which 4300 healed (64.8%), and the 10% validation sample included 709 PrUs, of which 477 healed (67.3%). For heel PrUs, the logistic regression development model included 1909 heel PrUs, of which 1240 healed (65.0%), and the 10% validation sample included 203 PrUs, of which 133 healed (65.5%). Variables significantly predicting healing were PrU size, PrU age, number of concurrent wounds of any etiology, PrU Stage III or IV, evidence of bioburden/infection, patient age, being nonambulatory, having renal transplant, paralysis, malnutrition, and/or patient hospitalization for any reason.
Conclusions:
Body and heel PrU Wound Healing Indices are comprehensive, user-friendly, and validated predictive models for likelihood of body and heel PrU healing. They can risk-stratify patients in clinical research trials, stratify patient data for quality reporting and benchmarking activities, and identify patients most likely to require advanced therapeutics to achieve healing.
