About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Adult patients aged 18 years or older at the time of inclusion

Patients with a diagnosis of diabetes mellitus according to ICD-10 codes E10-E14, and/or

Patients who have been prescribed at least one diabetes-related medication after the age of 18

Patients with relevant diagnoses and/or prescriptions recorded in the study data sources between 1 January 2014 and 30 June 2025

Disqualifiers

Patients younger than 18 years of age at the time of diabetes diagnosis or prescription

Patients with no recorded diagnosis of diabetes (ICD-10 E10-E14) and no prescription of diabetes medication after the age of 18

Patients with incomplete or missing key data required for model development or validation (e.g. missing outcome or essential covariates)

Trial population

The study population consists of all eligible adult patients receiving primary care within Region Västra Götaland (VGR). Data will be extracted from Assynja Whisp, the electronic health record system used by Närhälsan, the public primary care provider in VGR. The dataset includes information on all patients meeting the inclusion criteria who have been registered in the system during the study period. Extracted data comprise routinely collected clinical, diagnostic, and prescription information, enabling comprehensive population-based analyses

Trial design

Design model

Cohort

Time perspective

Retrospective

Treatments tested in this trial

Not listed

Trial groups

100,000 Participants

are grouped into 2 trial groups

Group A: Patients with diabetes with foot ulcers

Group B: Patients with diabetes without foot ulcers

Trial outcomes

Primary outcomes

1

Performance of machine learning-based prediction models for diabetic foot ulcer risk

The primary outcome is the predictive performance of machine learning-based models developed to estimate the risk of diabetic foot ulceration in patients with diabetes. Models will be trained using supervised machine learning techniques, with optimal hyperparameters identified through cross-validation. In the initial evaluation phase, model performance will be assessed for the ability to discriminate between patients with and without existing diabetic foot ulcers. In a subsequent phase, the models' ability to prospectively predict the development of diabetic foot ulcers during follow-up will be evaluated. Model robustness will be improved through an iterative process in which redundant variables are excluded and models are retrained. Predictive performance will be quantified using established metrics such as discrimination, calibration, and classification accuracy. To account for uncertainty in individual predictions, the final models will be combined with Conformal Prediction meth

Time frame

From study start to 2027-12-31

Secondary outcomes

1

Identification and interpretability of risk factors for diabetic foot ulcer development

The secondary outcome is the identification and validation of clinical, demographic, and socioeconomic variables that are potential risk factors for diabetic foot ulcer development in patients with diabetes. Variables and risk factor categories will be identified using electronic health record data from the primary care information system Assynja Whisp in Region Västra Götaland, linked with national registry data from Statistics Sweden (SCB), together with established scientific and empirical evidence. A case-control study design will be applied, in which patients with diabetes who develop foot ulcers are compared with a control group of patients with diabetes who do not develop foot ulcers. Population-level analyses will be conducted to examine associations and co-variation between the occurrence of diabetic foot ulcers and other relevant factors.