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

Lv, Jing; Li, Rao; Li, Yuan; Huang, Feng-Mei; Wang, Yi; He, Ting; Ye, Zi-Wei

doi:10.1155/2023/1199885

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…In strongly correlated data to build robust models and identify complex patterns. [41][42] Compared with the statistical methods in previous relevant studies, the prediction model we constructed includes more relevant factors, has higher accuracy and more intuitive prediction results.We believe that our model is convincing for the guidance of clinical work.…”

Section: Discussionmentioning

confidence: 99%

A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer

Liu,

Han,

Gao

et al. 2024

Preprint

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Background. Diabetic foot (DF) disease, which includes ulcers, infections and gangrene of the feet, is one of the leading causes of disability worldwide. Due to the high disability rate and expensive treatment cost of diabetic foot, doctors and patients all hope to forecast the prognosis in time and give early intervention. With the development of artificial intelligence technology, more and more methods are used in the diagnosis and prognosis prediction of chronic diseases. Machine learning, a type of artificial intelligence, has excellent predictive effects with a certain accuracy.1 The results of diabetic foot are affected by many factors, so it is necessary for the machine learning to reasonably predict the relationship between input variables and output variables, and to correct and tolerate faults.2 Objective. To develop an accurate and applicable predictive model for diabetic foot amputation and use it to guide clinical diagnosis and treatment, indicating the direction for the prevention of diabetic foot amputation. Methods and Materials. This retrospective study collected the basic data of 150 patients with DFU who met the study criteria in Beijing Shijitan Hospital from January 2019 to December 2022. Above all, We divided them into amputation group and non-amputation group based on prognostic outcome. Then we used Lasso algorithm to screen relevant risk factors, and predictive models were built with support vector mechanism(SVM) to input risk factors and predict amputation. Besides, we divided the test set and training set by 5-fold cross-validation. The area under the receiver operating characteristic (ROC) curves of the model were 0.89. This model’s calibration capability was 19.614 through Hosmer-Lemeshow test (p=0.012). Conclusion. In summary, our survey data suggested that C-reactive protein (CRP) in the infection index and the Wagner scale of the affected foot might play a vital role in predicting diabetic foot amputation. The predictive model we constructed can accurately estimate the rate of amputation during hospitalization in DFU patients. In addition, the model allows for personalized analysis of patients' risk factors.

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Section: Discussionmentioning

confidence: 99%

A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer

Liu,

Han,

Gao

et al. 2024

Preprint

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The role of neutrophils in diabetic ulcers and targeting therapeutic strategies

Ma,

Liu,

Kuai

et al. 2023

International Immunopharmacology

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

Piran,

Farhadian,

Soltanian

et al. 2024

Sci Rep

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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.

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

Cited by 5 publications

References 25 publications

A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer

A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer

The role of neutrophils in diabetic ulcers and targeting therapeutic strategies

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

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