Diabetic foot disease affects 6% of people with diabetes and includes infection, ulceration, or destruction of tissues of the foot. The various forms of diabetic foot disease impair patients quality of life and affect social participation. Between 0.03% and 1.5% of the population with diabetes may require some type of amputation, whereas among patients with diabetic foot ulcerations (DFU) this may run as high as 25%. Most ulcers can be prevented by screening for risk factors for a foot at risk of complications and good foot care.
Scoring systems for prediction of DFU in patients with diabetes perform relatively well in a hospital setting, but poor in a primary care setting. Furthermore, clinical risk scores that predict development of DFU by type do not exist. Additionally, current scores are difficult to interpret. Therefore, the aim of this study is to develop and validate a score for DFU, broken down by type using traditional, and sophisticated automated techniques.
The study objective is to develop prognostic scores for risk of diabetic foot ulceration (DFU), broken down by aetiology (composite, ischaemic, neuropathic, infected), in patients with a diagnosis of type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM). Patients aged 18 years or older with a registration of T1DM or T2DM between April 2004 and time of data extraction (approximately March 2019) will be included. The date of first diagnosis will define start of follow-up. All patients will be followed up for the occurrence of DFU (composite and aetiology specific), date of transfer out of practice area, death, end of data collection, or end of risk period (1, 5, 10, or 15 years), whichever will come first.
Both traditional epidemiological methods and machine learning techniques will be applied to develop and validate the prognostic scores.
Traditional: Regression models will be fitted with a predefined set of determinants for DFU, using forward selection with a significance level of 0.05. Various measures for prognostic accuracy including sensitivity, specificity, positive predictive values, negative predictive values, and beta-coefficients of the included factors will be determined. The beta-coefficients in the final Cox models will be converted into 1-, 5-, 10-, and 15-year absolute risk scores. The models performance will be determined by assessing goodness-of-fit and by assessment of the discriminative ability. To asses internal validity a k-fold cross-validation will be conducted.
Machine learning: The study population will be split into a `training' cohort and a test cohort. The training cohort will be used to develop the prediction models using machine learning algorithms such as random forest (RF) and artificial neural networks (ANN). The models will be trained through 10-fold cross-validation. The test dataset will be used for model evaluation. Accuracy, sensitivity, specificity, receiver operating characteristics (ROC) curves and areas under the curve (AUC) will be determined.
Diabetic foot ulceration (composite, and stratified by aetiology) Appendix 2-5
Frank de Vries - Chief Investigator - Utrecht University
Frank de Vries - Corresponding Applicant - Utrecht University
Johan Roikjer - Collaborator - Aalborg University Hospital
Johanna Driessen - Collaborator - Utrecht University
Johannes T.H. Nielen - Collaborator - Utrecht University
Joop van den Bergh - Collaborator - Maastricht University
Nicolaas Schaper - Collaborator - Maastricht University
Niels Ejskjaer - Collaborator - Aalborg University Hospital
Romin Pajouheshnia - Collaborator - Utrecht University