Abstract
Objective: Chronic wounds represent a highly prevalent but little recognized condition with substantial implications for patients and payers. While better wound care products and treatment modalities are known to improve healing rates, they are inconsistently used in real-world practice. Predicting healing rates of chronic wounds and comparing to actual rates could be used to detect and reward better quality of care. We developed a prediction model for chronic wound healing.Approach: We analyzed electronic medical records (EMRs) for 620,356 chronic wounds of various etiologies in 261,398 patients from 532 wound care clinics in the United States. Patient-level and wound-level parameters influencing wound healing were identified from prior research and clinician input. Logistic regression and classification tree models to predict the probability of wound healing within 12 weeks were developed using a random sample of 70% of the wounds and validated in the remaining data.Results: A total of 365,659 (58.9%) wounds were healed by week 12. The logistic and classification tree models predicted healing with an area under the curve of 0.712 and 0.717, respectively. Wound-level characteristics, such as location, area, depth, and etiology, were more powerful predictors than patient demographics and comorbidities.Innovation: The probability of wound healing can be predicted with reasonable accuracy in real-world data from EMRs.Conclusion: The resulting severity adjustment model can become the basis for applications like quality measure development, research into clinical practice and performance-based payment.
Highlights
Chronic wounds, usually defined as wounds that do not heal within an expected time frame, typically 4–12 weeks, are a growing but little recognized public health challenge.[1,2] Their prevalence is similar to that of heart failure, affecting 6.5a Sang Kyu Cho, et al, 2020; Published by Mary Ann Liebert, Inc
The objective of our study is to develop and validate a unified severity adjustment model for a broad range of chronic wounds that can form the basis for a quality measure as it is entirely based on routinely collected data from intake visits at wound care clinics
Our study used data from a large electronic medical records (EMRs) to predict the probability of wound healing as a function of patient and wound characteristics with both clinically informed and machine learning approaches
Summary
Usually defined as wounds that do not heal within an expected time frame, typically 4–12 weeks, are a growing but little recognized public health challenge.[1,2] Their prevalence is similar to that of heart failure, affecting 6.5. Patients with chronic wounds often suffer loss of productivity, psychological distress, decreased quality of life, and reduced life expectancy.[5,6]
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