Development and Validation of a Risk Prediction Model for Foot Ulcers in Diabetic Patients

Performance of risk prediction models for diabetic foot ulcer: a meta-analysis

Background

The number of prediction models for diabetic foot ulcer (DFU) risk is increasing, but their methodological quality and clinical applicability are uncertain. We conducted a systematic review to assess their performance.

Methods

We searched PubMed, Cochrane Library, and Embase databases up to 10 February 2024 and extracted relevant information from selected prediction models. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) checklist was used to assess bias risk and applicability. All statistical analyses were conducted in Stata 14.0.

Results

Initially, 13,562 studies were retrieved, leading to the inclusion of five development and five validation models from eight studies. DFU incidence ranged from 6% to 16.8%, with age and hemoglobin A1C (HbA1c) commonly used as predictive factors. All included studies had a high risk of bias, mainly due to disparities in population characteristics and methodology. In the meta-analysis, we observed area under the curve (AUC) values of 0.78 (95% CI [0.69-0.89]) for development models and 0.84 (95% CI [0.79-0.90]) for validation models.

Conclusion

DFU risk prediction models show good overall accuracy, but there is a risk of bias. Adherence to the PROBAST checklist is crucial for improving their clinical applicability.

Diabetic foot ulcers risk prediction in patients with type 2 diabetes using classifier based on associations rule mining

Identifying diabetic patients at risk of developing foot ulcers, as one of the most significant complications of diabetes, is a crucial healthcare concern. This study aimed to develop an associative classification model (CBA) using the Apriori algorithm to predict diabetic foot ulcers (DFU). This retrospective cohort study included 666 patients with type 2 diabetes referred to Shahid Beheshti Hospital in Iran between April 2020 and August 2022, of which 279 (42%) had DFU. Data on 29 specific baseline features were collected, which were preprocessed by discretizing numerical variables based on medical cutoffs. The target variable was the occurrence of DFU, and the minimum support, confidence, and lift thresholds were set to 0.01, 0.7, and 1, respectively. After data preparation and cleaning, a CBA model was created using the Apriori algorithm, with 80% of the data used as a training set and 20% as a testing set. The accuracy and AUC (area under the roc curve) measure were used to evaluate the performance of the model. The CBA model discovered a total of 146 rules for two patient groups. Several factors, such as longer duration of diabetes over 10 years, insulin therapy, male sex, older age, smoking, addiction to other drugs, family history of diabetes, higher body mass index, physical inactivity, and diabetes complications such as proliferative and non-proliferative retinopathy and nephropathy, were identified as major risk factors contributing to the development of DFU. The CBA model achieved an overall accuracy of 96%. Also, the AUC value was 0.962 (95%CI 0.924, 1.000). The developed model has a high accuracy in predicting the risk of DFU in patients with type 2 diabetes. The creation of accurate predictive models for DFU has the potential to significantly reduce the burden of managing recurring ulcers and the need for amputation, which are significant health concerns associated with diabetes.

The role of neutrophils in diabetic ulcers and targeting therapeutic strategies

Diabetic ulcers (DUs) are a common complication of diabetes with high morbidity, poor prognosis, and a high socio-economic burden. The main pathological manifestations of DUs are chronic inflammation, impaired re-epithelialization, and impaired angiogenesis. During the inflammatory phase, neutrophils are one of the main DU cell types and act by releasing neutrophil extracellular traps (NETs), leading to poor healing in DUs. This review summarizes the role of neutrophils in the pathology and treatment of DUs, with a view to potential novel therapies and therapeutic targets.