The accuracy of venous leg ulcer prognostic models in a wound care system

TLDR

Venous leg ulcers are common chronic wounds typically treated with compression bandages, and prior small studies have suggested that predictive models may identify which wounds respond to therapy. This study aimed to evaluate the accuracy of several prognostic models in a large cohort of over 20,000 patients with venous leg ulcers. The researchers constructed logistic‑regression models and simple prognostic‑factor counts using baseline wound size and duration data from the cohort. They found that initial wound size and duration accurately predict healing by 24 weeks, with a 29 % chance of non‑healing for wounds <10 cm² and <12 months old versus a 78 % chance for larger, older wounds, and the models can guide clinical care and trial design.

Abstract

Venous leg ulcers are among the most common chronic wounds. Treatment is commonly with a limb compression bandage. Previous small, often single‐center, studies have shown that it is possible to predict which wounds are likely to respond to compression therapy. We designed this cohort study using a dataset of over 20,000 individuals with a venous leg ulcer to investigate the accuracy of several prognostic models. Creating complex models using logistic regression, as well as simply counting prognostic factors, we show that initial measures of wound size and duration accurately predict, as measured by area under the receiver operator curve and Brier score, who will heal by the 24th week of care. For example, a wound that is less than 10 cm 2 and less than 12 months old at the first visit has a 29 percent chance of not healing by the 24th week of care, while a wound greater than 10 cm 2 and greater than 12 months old has a 78 percent chance of not healing. Ultimately, these models can be applied by a clinician to help determine whom to continue to treat with standard care and perhaps whom to treat with adjuvant therapies. They may also aid in the design of clinical trials.