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. 2022 Jul 13:13:890057.
doi: 10.3389/fendo.2022.890057. eCollection 2022.

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

Jie Wang  1 , Tong Xue  2 , Haopeng Li  1 , Shuai Guo  1
Affiliations

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

Jie Wang et al. Front Endocrinol (Lausanne). .

Abstract

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.

Keywords: diabetic foot; individual risk prediction model; nomogram; orthopedics; type 2 diabetes mellitus (T2DM).

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of patients included in this study.
Figure 2
Figure 2
Nomogram prediction for the risk of diabetic foot in patients with T2DM.
Figure 3
Figure 3
ROC curves of the nomogram prediction for the risk of diabetic foot in patients with T2DM in the training cohort (A) and validation cohort (B).
Figure 4
Figure 4
Calibration plots of the nomogram prediction for the risk of diabetic foot in patients with T2DM in the training cohort (A) and validation cohort (B).
Figure 5
Figure 5
DCA curves of the nomogram prediction for the risk of diabetic foot in patients with T2DM in the training cohort (A) and validation cohort (B).
Figure 6
Figure 6
Visualization application of the nomogram prediction for the risk of diabetic foot in patients with T2DM.
See this image and copyright information in PMC

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