Validation of predictive factors for infection in adults with chronic leg ulcers: A prospective longitudinal study

Abstract
Aims and objectives To validate the ability of factors to predict infection in adults with chronic leg ulcers over a 12‐week period. Background Leg ulcers affect approximately 3% of older adults and are often hard to heal. Infection is a leading contributor for delayed healing, causing delayed wound healing, increased hospitalisation, increased healthcare costs and reduced patient quality of life. The importance of early identification of infection has been highlighted for decades, yet there is little known about factors that are associated with increased risk for infection in this specific population. Design A longitudinal, prospective observational study in a single centre. Methods Between August 2017 and May 2018, a total of 65 adults with chronic leg ulcers were prospectively observed for a 12‐week period. Patients were recruited from an outpatient wound clinic at a tertiary hospital in Australia. Data were collected from recruitment (baseline), and each visit (weekly or fortnightly) up until 12 weeks. Descriptive statistics were calculated for all variables. A Cox proportional hazards regression model was used to identify predictive factors for infection. The TRIPOD guidelines for reporting were followed (See Supplementary file 1). Results The sample consisted of 65 adults with chronic leg ulcers and 9.2% of these had their ulcer infected at baseline. Two predictive factors, using walking aids and gout, were found to be significantly related to increased likelihood of developing infection within 12 weeks. Conclusion The present study showed that patients, who either used walking aids and/or were diagnosed with gout were at greater risk for infection compared to those without these factors. Relevance to clinical practice These findings provide new information for clinicians in early identification of patients at risk of infection, and for patients in enhancing their awareness of their own risk.
Funding Information
  • Queensland University of Technology (Commonwealth‐funded postgraduate research scholars)