Prediction Model for the Risk of Developing Foot Ulcers in Diabetes

Introduction Foot ulcers in diabetes mellitus (DM) are a common and serious complication that can lead to infection, amputation, and increased mortality. Early identification of patients at high risk is crucial in order to implement preventive measures at an early stage. The number of people with DM is increasing globally, from 540 million in 2021 to an estimated 780 million by 2045. Foot ulcers cause considerable suffering for the individual and entail substantial costs for the healthcare system. Despite national guidelines recommending regular, structured foot examinations and risk classification to assess the risk of developing foot ulcers, current risk models do not take into account the complex interactions between risk factors and socioeconomic factors such as marital status, level of education, and place of residence. Data-driven advances and artificial intelligence (AI) offer new opportunities to refine risk identification, but their use in predicting the risk of diabetic foot ulcers remains limited. The need for foot screening is considerable. In Sweden, there are approximately 600,000 patients with DM, and half of them live with an increased risk due to nerve damage in the feet. This means that, based on risk level, around 300,000 patients in Sweden may require preventive interventions, including medical foot care, customised footwear, and access to specialist care for those with foot ulcers. Improved preventive efforts are emphasised in the person-centred and integrated care pathway for people with diabetes at high risk of foot ulcers. However, accurate identification of foot ulcer risk is currently lacking. Prevention leads not only to good quality of life for the individual but also to reduced healthcare costs. Estimates by Ragnarsson Tennvall show that a hard-to-heal ulcer costs approximately SEK 100,000 per year, while an amputation costs around SEK 300,000-500,000. Given a prevalence of foot ulcers of 5% among patients with diabetes, the annual cost of ulcer care amounts to SEK 3 billion. In addition, there are costs of approximately SEK 750 million for amputations, according to data from the quality register SwedAmp. The aim of the study is to develop, test, and validate prediction models (statistical and AI-based) to identify patients with DM who are at risk of developing foot ulcers. The models will be based on retrospective electronic health record data from primary care in the Västra Götaland Region (VGR), as well as data from Statistics Sweden (SCB) concerning demographic factors such as marital status, level of education, occupation, and place of residence. Methods The study has two methodological approaches: AI-based modelling and statistical modelling. AI-based approach Machine learning models will be developed to predict patients at risk of developing diabetic foot ulcers. The models will be trained using cross-validation on a large dataset in which variables will be iteratively excluded. Conformal prediction will be used to quantify uncertainty in patient-level predictions. The resulting models will be analysed to identify the strongest predictors and will be compared with classical statistical modelling and findings from the literature. Steps in AI modelling: Data extraction: Electronic health record data from primary care in VGR, supplemented with sociodemographic data from SCB. Data processing: Use of, among other variables, diagnostic codes (ICD-10), healthcare interventions (KVÅ codes), visit types, visit frequency, ECG parameters, and free-text data to construct predictors. Model development: Prediction models will be developed and trained using cross-validation. Measures of uncertainty will be generated using conformal prediction. Validation: A separate cohort will be used to test model performance (sensitivity, specificity, positive predictive value \[PPV\]). Interpretation: The models will be reviewed for transparency and clinical interpretability in collaboration with patient representatives, clinicians, and researchers. The results of the statistical and AI-based models will be compared with regard to their respective strengths and weaknesses. Statistical modelling Two populations will be analysed: patients with diabetes without foot ulcers and patients with diabetes with foot ulcers. Co-variation and causal relationships between risk factors and foot ulcers will be identified. A model describing causal pathways leading to ulcer development will be developed, and its certainty and uncertainty will be analysed.