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Objective <p>To develop and validate a nomogram for the individualized prediction of superficial fungal infections (SFI) risk in patients with type 2 diabetes mellitus (T2DM).</p> Methods <p>This cross-sectional study enrolled patients with T2DM from the Affiliated Anning First People’s Hospital of Kunming University of Science and Technology between December 2023 and December 2024. Risk factors were identified using multivariable logistic regression, and a nomogram was developed subsequently for predicting SFI in T2DM patients. The model’s performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA) and clinical impact curve (CIC) in the training and validation sets.</p> Results <p>Among 308 hospitalized T2DM patients screened by multiplex quantitative polymerase chain reaction (qPCR), 220 (71.40%) were diagnosed with SFI. <i>Trichophyton rubrum</i> (107 cases) was the most common pathogen, and monoinfection was the most frequent presentation (90 patients). Patients were randomly divided into a training (<i>n</i> = 216) and a validation cohort (<i>n</i> = 92) in a 7:3 ratio. Multivariable logistic regression analysis identified eight key variables: body mass index (BMI), blood glucose (Glu), hemoglobin A1c (HbA1c), urinary albumin-to-creatinine ratio (UACR), serum potassium (K<sup>+</sup>), sodium (Na<sup>+</sup>), hyperlipidemia (HLP) and hypertension (HTN). The nomogram demonstrated excellent predictive ability. The ROC analysis indicated good discrimination in the training cohort (area under the curve (AUC) = 0.966; 95% CI, 0.945–0.987) and the validation cohort (AUC = 0.931; 95% CI, 0.877–0.985). The optimal cut-point of the nomogram was 0.624 with a sensitivity of 92.3% and specificity of 86.7% (Youden’s index: 0.79). Calibration curves showed good agreement. DCA confirmed the clinical utility of the nomogram.</p> Conclusion <p>This nomogram effectively predicts the risk of SFI in T2DM patients and provides an objective tool to facilitate early identification and intervention by clinicians.</p>

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Development and validation of a risk nomogram model for predicting superficial fungal infections in patients with type 2 diabetes mellitus : a cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Yu Li,
  • Guozhong Zhou,
  • Feifei Yang,
  • Rong long,
  • Wei Shi,
  • Yan Dong,
  • Yuanyuan Zhou,
  • Nan Chen,
  • Ying Yang

摘要

Objective

To develop and validate a nomogram for the individualized prediction of superficial fungal infections (SFI) risk in patients with type 2 diabetes mellitus (T2DM).

Methods

This cross-sectional study enrolled patients with T2DM from the Affiliated Anning First People’s Hospital of Kunming University of Science and Technology between December 2023 and December 2024. Risk factors were identified using multivariable logistic regression, and a nomogram was developed subsequently for predicting SFI in T2DM patients. The model’s performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA) and clinical impact curve (CIC) in the training and validation sets.

Results

Among 308 hospitalized T2DM patients screened by multiplex quantitative polymerase chain reaction (qPCR), 220 (71.40%) were diagnosed with SFI. Trichophyton rubrum (107 cases) was the most common pathogen, and monoinfection was the most frequent presentation (90 patients). Patients were randomly divided into a training (n = 216) and a validation cohort (n = 92) in a 7:3 ratio. Multivariable logistic regression analysis identified eight key variables: body mass index (BMI), blood glucose (Glu), hemoglobin A1c (HbA1c), urinary albumin-to-creatinine ratio (UACR), serum potassium (K+), sodium (Na+), hyperlipidemia (HLP) and hypertension (HTN). The nomogram demonstrated excellent predictive ability. The ROC analysis indicated good discrimination in the training cohort (area under the curve (AUC) = 0.966; 95% CI, 0.945–0.987) and the validation cohort (AUC = 0.931; 95% CI, 0.877–0.985). The optimal cut-point of the nomogram was 0.624 with a sensitivity of 92.3% and specificity of 86.7% (Youden’s index: 0.79). Calibration curves showed good agreement. DCA confirmed the clinical utility of the nomogram.

Conclusion

This nomogram effectively predicts the risk of SFI in T2DM patients and provides an objective tool to facilitate early identification and intervention by clinicians.