Design and clinical application of a risk prediction model for diabetic foot
AJTR Copyright © 2024..
OBJECTIVE: To construct and evaluate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes based on their clinical data, and to assist clinical healthcare professionals in identifying high-risk factors and developing targeted intervention measures.
METHODS: We retrospectively collected clinical data from 478 hospitalized patients with type 2 diabetes at the First Affiliated Hospital of Shantou University Medical College from January 2019 to December 2021. The patients were divided into a diabetic foot group (n=312) and a non-diabetic foot group (n=166) based on whether they had diabetic foot. The baseline data of both groups were collected. Univariate and multivariate analyses as well as logistic regression analysis were conducted to explore the risk factors for diabetic foot. A nomogram prediction model was established using the package "rms" version 4.3. The model was internally validated using the area under the receiver operating characteristic curve (AUC). Additionally, the decision curve analysis (DCA) was performed to evaluate the performance of the nomogram model.
RESULTS: The results from the logistic regression analysis revealed that being male, smoking, duration of diabetes, glycated hemoglobin, hyperlipidemia, and atherosclerosis were influencing factors for diabetic foot (all P<0.05). The AUC of the model in predicting diabetic foot was 0.804, with a sensitivity of 75.3% and specificity of 74.4%. Harrell's C-index of the nomogram prediction model for diabetic foot was 0.804 (95% CI: 0.762-0.844), with a threshold value of >0.675. The DCA findings demonstrated that the nomogram model provided a net clinical benefit.
CONCLUSION: The nomogram prediction model constructed in this study showed good predictive performance and can provide a basis for clinical workers to prevent and intervene in diabetic foot, thereby improving the overall diagnosis and treatment.
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
American journal of translational research - 16(2024), 2 vom: 28., Seite 458-465 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yang, Xiaoping [VerfasserIn] |
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| Themen: |
Clinical application |
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| Anmerkungen: |
Date Revised 12.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369533046 |
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| 100 | 1 | |a Yang, Xiaoping |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Design and clinical application of a risk prediction model for diabetic foot |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 12.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a AJTR Copyright © 2024. | ||
| 520 | |a OBJECTIVE: To construct and evaluate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes based on their clinical data, and to assist clinical healthcare professionals in identifying high-risk factors and developing targeted intervention measures | ||
| 520 | |a METHODS: We retrospectively collected clinical data from 478 hospitalized patients with type 2 diabetes at the First Affiliated Hospital of Shantou University Medical College from January 2019 to December 2021. The patients were divided into a diabetic foot group (n=312) and a non-diabetic foot group (n=166) based on whether they had diabetic foot. The baseline data of both groups were collected. Univariate and multivariate analyses as well as logistic regression analysis were conducted to explore the risk factors for diabetic foot. A nomogram prediction model was established using the package "rms" version 4.3. The model was internally validated using the area under the receiver operating characteristic curve (AUC). Additionally, the decision curve analysis (DCA) was performed to evaluate the performance of the nomogram model | ||
| 520 | |a RESULTS: The results from the logistic regression analysis revealed that being male, smoking, duration of diabetes, glycated hemoglobin, hyperlipidemia, and atherosclerosis were influencing factors for diabetic foot (all P<0.05). The AUC of the model in predicting diabetic foot was 0.804, with a sensitivity of 75.3% and specificity of 74.4%. Harrell's C-index of the nomogram prediction model for diabetic foot was 0.804 (95% CI: 0.762-0.844), with a threshold value of >0.675. The DCA findings demonstrated that the nomogram model provided a net clinical benefit | ||
| 520 | |a CONCLUSION: The nomogram prediction model constructed in this study showed good predictive performance and can provide a basis for clinical workers to prevent and intervene in diabetic foot, thereby improving the overall diagnosis and treatment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Diabetes | |
| 650 | 4 | |a clinical application | |
| 650 | 4 | |a diabetic foot | |
| 650 | 4 | |a nomogram prediction model | |
| 700 | 1 | |a Chen, Shaohong |e verfasserin |4 aut | |
| 700 | 1 | |a Ji, Leiquan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qiaohui |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Chujia |e verfasserin |4 aut | |
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