Development and Evaluation of a Bayesian Risk Stratification Method for Major Amputations in Patients with Diabetic Foot Ulcers

Prediction models for amputation after diabetic foot: systematic review and critical appraisal

BACKGROUND

Numerous studies have developed or validated prediction models aimed at estimating the likelihood of amputation in diabetic foot (DF) patients. However, the quality and applicability of these models in clinical practice and future research remain uncertain. This study conducts a systematic review and assessment of the risk of bias and applicability of amputation prediction models among individuals with DF.

METHODS

A comprehensive search was conducted across multiple databases, including PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedical Literature Database (CBM), and Weipu (VIP) from their inception to December 24, 2023. Two investigators independently screened the literature and extracted data using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability.

RESULTS

A total of 20 studies were included in this analysis, comprising 17 development studies and three validation studies, encompassing 20 prediction models and 11 classification systems. The incidence of amputation in patients with DF ranged from 5.9 to 58.5%. Machine learning-based methods were employed in more than half of the studies. The reported area under the curve (AUC) varied from 0.560 to 0.939. Independent predictors consistently identified by multivariate models included age, gender, HbA1c, hemoglobin, white blood cell count, low-density lipoprotein cholesterol, diabetes duration, and Wagner's Classification. All studies were found to exhibit a high risk of bias, primarily attributed to inadequate handling of outcome events and missing data, lack of model performance assessment, and overfitting.

CONCLUSIONS

The assessment using PROBAST revealed a notable risk of bias in the existing prediction models for amputation in patients with DF. It is imperative for future studies to concentrate on enhancing the robustness of current prediction models or constructing new models with stringent methodologies.

Nomogram Prediction for the Risk of Diabetic Foot in Patients With Type 2 Diabetes Mellitus

AIMS

To develop and validate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes mellitus (T2DM) and evaluate its clinical application value.

METHODS

We retrospectively collected clinical data from 1,950 patients with T2DM from the Second Affiliated Hospital of Xi'an Jiaotong University between January 2012 and June 2021. The patients were divided into training cohort and validation cohort according to the random number table method at a ratio of 7:3. The independent risk factors for diabetic foot among patients with T2DM were identified by multivariate logistic regression analysis. Then, a nomogram prediction model was developed using the independent risk factors. The model performances were evaluated by the area under the receiver operating characteristic curve (AUC), calibration plot, Hosmer-Lemeshow test, and the decision curve analysis (DCA).

RESULTS

Multivariate logistic regression analysis indicated that age, hemoglobin A1c (HbA1c), low-density lipoprotein (LDL), total cholesterol (TC), smoke, and drink were independent risk factors for diabetic foot among patients with T2DM (P < 0.05). The AUCs of training cohort and validation cohort were 0.806 (95% CI: 0.775∼0.837) and 0.857 (95% CI: 0.814∼0.899), respectively, suggesting good discrimination of the model. Calibration curves of training cohort and validation cohort showed a favorable consistency between the predicted probability and the actual probability. In addition, the P values of Hosmer-Lemeshow test for training cohort and validation cohort were 0.826 and 0.480, respectively, suggesting a high calibration of the model. When the threshold probability was set as 11.6% in the DCA curve, the clinical net benefits of training cohort and validation cohort were 58% and 65%, respectively, indicating good clinical usefulness of the model.

CONCLUSION

We developed and validated a user-friendly nomogram prediction model for the risk of diabetic foot in patients with T2DM. Nomograms may help clinicians early screen and identify patients at high risk of diabetic foot.