Development of a Model to Predict Healing of Chronic Wounds Within 12 Weeks

Abstract

Pré cis: Using both clinically informed and machine learning approaches, we developed a prediction model for chronic wound healing that can form the basis for quality measurement. 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.

Diagnostic Accuracy of Point-of-Care Fluorescence Imaging for the Detection of Bacterial Burden in Wounds: Results from the 350-Patient Fluorescence Imaging Assessment and Guidance Trial

“…Quantitative tissue cultures of wound biopsies are the reference standard to quantify bacterial load, but prolonged turnaround time between biopsy and microbiological results limits the rapid decision making needed to effectively manage bacterial burden in wounds. The relative inconsistency of CSS and delays in results from microbiological culture and PCR analysis may explain why 12-week wound healing rates are below 60% 7 and have remained stagnant over the past 40 years, 20 despite tremendous advances in wound treatments.…”

“…It is known that increased microbial load is a key predictor of nonhealing wounds. 7,8 Proliferation of bacteria resulting in moderate-to-heavy loads (>10 4 colony-forming units [CFU]/g) delays healing [9][10][11] and increases the risk of wound complications, including infection, sepsis, and amputation. [12][13][14] Guidelines advise that early diagnosis of high bacterial burden is essential to prevent the wound from progression to local or systemic infection.…”

Evaluation of the combination of a biofilm-disrupting agent and negative pressure wound therapy: a case series

“…A recent article evaluating the medical records of over 100,000 patients reported that only 14.9% of pressure ulcers healed by 12 weeks. 2 A variety of local and systemic factors contribute to delayed wound healing in PUs, including age, wound size and depth, bacterial burden, elevated matrix metalloproteases, unrelieved pressure, and comorbidities such as diabetes, renal disease, malnutrition and obesity. [2][3][4][5] The most commonly used staging system for PUs (also known as pressure injuries), published by the National Pressure Injury Advisory Panel (NPIAP), designates full-thickness ulcers as Stage 3 or 4.…”

A real‐world experience with the bioactive human split thickness skin allograft for venous leg ulcers

“…6 A recent publication looking at predictive models of wound healing confirmed that wound surface area was a significant factor in determining likelihood of healing. 10 In 2004, Margolis found that VLU duration (less than or greater than 1 year) and size (less than or greater than 10cm 2 ) were prognostic indicators of healing. 11 For example, the Margolis study predicted that the likelihood of healing a VLU >1 year duration and > 10 cm 2 in size was only 26%.…”

“…Their study considered VLU's that had been present on average for 17.9 weeks (SD 30.3 weeks) prior to treatment and also found smaller wounds healed faster 6 . A recent publication looking at predictive models of wound healing confirmed that wound surface area was a significant factor in determining likelihood of healing 10 …”