Diabetic foot ulcer outcomes from a podiatry led tertiary service in Kuwait.
Grace MessengerRichard MasoetsaImtiaz HussainDevarajan SriramanMohamed JahromiPublished in: Diabetic foot & ankle (2018)
Objective: This single-centred study aims to evaluate the incidence, risk factors and treatment outcomes of a podiatry led, evidence-based diabetic foot ulcer (DFU) clinic. Research design and methods: Data from the DFU database and patient electronic health records were retrospectively collected from patients with new DFUs who were referred for treatment to the Department of Podiatry, Dasman Diabetes Institute, Kuwait, from 1 October 2014, to 31 December 2016. Patients were followed-up until healing occurred or until 6 months after the study end date, whichever came first. Results: All data were analysed using IBM SPSS version 24 software. Data were collected from 230 patients with 335 DFUs. Most DFUs (67%) were present for <3 months from the time of the first podiatry appointment. A total of 56% of DFUs were classified as neuropathic. Most (72%) DFUs healed, with a median healing time of 52.0 days. Chronic kidney disease (p = 0.001), retinopathy (p = 0.03), smoking (p = 0.02), ulcer location (p = 0.03), peripheral arterial disease (PAD) (p = 0.004) and osteomyelitis (p = 0.05) were found to have a meaningful association with DFU outcome. The number of days to heal was associated with ulcer classification (p = 0.005), bacterial infection (p = 0.002), osteomyelitis (p = < 0.001) and PAD (p = < 0.001). Conclusions: The incidence of new DFUs in our tertiary clinic is 3.4%. The incidence of diabetic foot ulceration, days to heal, healing rate and the risk factors influencing healing are in accordance with other multidisciplinary facilities with podiatry input.
Keyphrases
- electronic health record
- risk factors
- chronic kidney disease
- end stage renal disease
- clinical decision support
- cardiovascular disease
- adverse drug
- big data
- primary care
- type diabetes
- ejection fraction
- newly diagnosed
- machine learning
- mental health
- deep learning
- emergency department
- data analysis
- case report
- quality improvement
- adipose tissue
- artificial intelligence
- metabolic syndrome
- patient reported outcomes
- weight loss
- skeletal muscle
- patient reported