Evaluation of the Effectiveness of Diabetic Foot Ulcer Recurrence Risk Prediction Models: A Systematic Review
-
Ziqiang Li
,
Yan-Ping Zhang
,
Guifen Fu
,
Jing-Feng Chen
,
Qiu-Ping Zheng
,
Xiaomin Xian
,
Miao Wang
Abstract
Background: We used the Predictive Model Bias Risk Assessment tool (PROBAST) tool to systematically evaluate the existing models worldwide, in order to provide a reference for clinical staff to select and optimize DFU recurrence risk prediction models.
Methods: Literature on DFU recurrence risk prediction model construction published in CNKI, China Biomedical Literature Database, Vipu China Knowledge, China Biomedical Literature Database, Vipu Chinese Journal Service Platform, Wanfang Data Knowledge Service Platform, Embase, PubMed, Web of Science, Cochrane Library and other databases were systematically searched. The search period was until January 29, 2024, encompassing all relevant studies published up to that date. Literature screening and data extraction were conducted by two researchers, and the PROBAST was used to evaluate the bias risk and applicability of the included literature.
Results: Finally, 9 literatures were included, 13 prediction models were established, and the area under the AUC or C-index ranged from 0.660 to 0.943. Nine models were validated internally and one model was validated externally. All the models constructed in the included literature are of high-risk bias, and the applicability of the models is reasonable. Common predictors in the prediction model were Wagner scale, glycosylated hemoglobin, and diabetic peripheral neuropathy.
Conclusion: Although most of the existing DFU risk prediction models have good prediction performance, they all have high risk of bias. It is suggested that researchers should update the existing models in the future, and future modeling studies should follow the reporting norms, so as to develop a scientific, effective and convenient risk prediction model that is more conducive to clinical practice.
1. An N, Liu CQ, Yang X, et al (2023). Analy-sis of risk factors for recurrence of dia-betic foot ulcers after healing. Journal of Vascular and Endovascular Surgery,9 (11): 1219-1223.
2. Armstrong D G, Tan T W, Boulton A, et al (2023). Diabetic Foot Ulcers: a Review. JAMA, 330 (1): 62-75.
3. Zhang X, LI Q, Zhou X, et al (2024). Risk factors for amputation in diabetic foot ulcers: a retrospective analysis. Int Wound J, 21 (4): e14832.
4. Chen L, Sun S, Gao Y, et al (2023). Global mortality of diabetic foot ulcer: a sys-tematic review and meta-analysis of ob-servational studies. Diabetes Obes Metab, 25 (1): 36-45.
5. Guo Q, Ying G, Jing O, et al (2023). Influ-encing factors for the recurrence of di-abetic foot ulcers: a meta-analysis. Int Wound J, 20 (5): 1762-1775.
6. Hsu CR (2019). The key to diabetic foot disease prevention and treatment - In-terpretation of 2019 domestic and inter-national guidelines for diabetic foot prevention and treatment. China Clinical New Medicine,12 (12): 1259-1262.
7. Palazón-bru A, Martín-pérez F, Mares-garcía E, et al (2020). A general presenta-tion on how to carry out a CHARMS analysis for prognostic multivariate models. Stat Med, 39 (23): 3207-3225.
8. Moons K, Wolff R F, Riley R D, et al (2019). PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elabo-ration. Ann Intern Med, 170 (1): W1-W33.
9. Chen XP, Zhang Y, Zhuang YY, et al (2020). PROBAST: an assessment tool for risk of bias in diagnostic or prognostic mul-tifactorial prediction modeling studies. Chinese Journal of Evidence-Based Medicine, 20 (06): 737-744.
10. Wei L, Wang YT, Sun Y (2023). Predictive value of wound, ischemia and foot in-fection grading for recurrence of dia-betic foot ulcers within 1 year and con-struction of prediction model. Journal of Vascular and Endoluminal Vascular Surgery, 9 (12): 1454-1459.
11. Wang H, Li L (2023). Influencing factors and risk modeling of recurrence after ulcer healing in diabetic foot patients. China Journal of Practical Medicine, 23 (01): 54-58.
12. Zhang J, Li HF, Li XM, et al (2023). Con-struction of a diabetic foot ulcer recur-rence risk prediction model: based on logistic regression and support vector machine and BP neural network model. Chinese Family Medicine,26 (32): 4013-4019.
13. Aan D S W, Schut M C, Abu-hanna A, et al (2021). Development of a prediction model for foot ulcer recurrence in peo-ple with diabetes using easy-to-obtain clinical variables. BMJ Open Diabetes Res Care, 9 (1): e002257.
14. Lai JJ, Sun Y (2024). The value of the DMIST scale in predicting recurrence of diabetic foot ulcers within 1 year af-ter healing and the construction of a prediction model. Journal of Vascular and Endovascular Surgery, 10 (01): 6-11.
15. Wang YR Qiu XT, Pan L (2023). Analysis of factors influencing the recurrence of di-abetic foot ulcers after healing and con-struction of risk prediction model. Mili-tary Nursing, 40 (5): 62-65.
16. Aan D S W, Abu-hanna A, Bus S A (2020). Development of a multivariable predic-tion model for plantar foot ulcer recur-rence in high-risk people with diabetes. BMJ Open Diabetes Res Care, 8 (1): e001207.
17. Lv J, Yuan L, Li R, et al (2022). Construction of a predictive model for diabetic foot ulcer recurrence risk. Nursing Research,36 (06): 993-998.
18. Xia L (2023). Construction and validation of a 1-year recurrence risk prediction column chart model for diabetic foot ulcer patients. Jiangsu University, 2023.
19. Ogundimu E O, Altman D G, Collins G S (2016). Adequate sample size for devel-oping prediction models is not simply related to events per variable. J Clin Ep-idemiol, 76: 175-182.
20. Wynants L, Collins G S, Van Calster B (2017). Key steps and common pitfalls in developing and validating risk mod-els. BJOG, 124 (3): 423-432.
21. Mühlenbruch K, Kuxhaus O, di Giuseppe R, et al (2017). Multiple imputation was a valid approach to estimate absolute risk from a prediction model based on case-cohort data. J Clin Epidemiol, 84: 130-141.
22. Mok HF, Chen YP, Han H, et al (2024). Methods and steps of clinical prediction modeling research. Chinese Journal of Evi-dence-Based Medicine, 24 (02): 228-236.
23. Xi LJ, Guo ZY, Yang XK, et al (2023). Ap-plication of LASSO and its extension methods in variable screening for re-gression analysis. Chinese Journal of Preven-tive Medicine, 2023 (01): 107-111.
24. Oley M H, Oley M C, Kepel B J, et al (2024). Hyperbaric Oxygen Therapy for Diabetic Foot Ulcers Based on Wagner Grading: a Systematic Review and Meta-analysis. Plast Reconstr Surg Glob Open, 12 (3): e5692.
25. Zhou Shujie, Zhao Ling, Ke Tingyu (2024). Correlation analysis between common clinical indicators and diabetic foot ul-cers. Journal of Kunming Medical University, 45 (01): 61-66.
26. Wang Y, Xue F, Lin SN, et al (2023). Corre-lation between serum reticulin-1 and vascular endothelial growth factor levels and Wagner's grading in patients with diabetic foot. Chinese Journal of Practical Diagnosis and Therapy, 37 (06): 565-569.
27. Wu J, Bista Raju, CHA PP, et al (2023). Characterization of inflammatory markers in diabetic foot patients and their relationship with foot ulcer prog-nosis. Journal of Sichuan University (Medical Edition), 54 (06): 1233-1238.
28. Huang Z H, Li S Q, Kou Y, et al (2019). Risk factors for the recurrence of diabetic foot ulcers among diabetic patients: a meta-analysis. Int Wound J, 16 (6): 1373-1382.
29. Zhu XL, Liao WQ, Zhang C, et al (2023). Pathogenesis and treatment of diabetic foot ulcers. Chinese Journal of Dermatology and Venereology, 37 (04): 367-372.
30. Xie JX, Dai X, Huang SH, et al (2023). A prospective study of risk factors for di-abetic foot ulcers in the Zhuang popula-tion. Chinese Journal of Gerontology, 43 (01): 55-59.
31. Xia L, Zhuang R, Wu L, et al (2022). Meta-analysis of risk factors associated with recurrent diabetic foot ulcers. Chinese Journal of Modern Nursing, (09): 1143-1148.
32. Sen C K, Roy S, Khanna S (2023). Diabetic Peripheral Neuropathy Associated with Foot Ulcer: One of a Kind. Antioxidant Redox Signal, 10.1089/ars.2022.0093.
33. Yang J, Xing Y, Shi Q (2023). The Michigan Neurologic Screening Scale. The Value of Michigan Nerve Screening Scale, Nociceptive Tactile Warming Test and Vibratory Sensory Threshold in Diag-nosing Diabetic Peripheral Neuropathy. Chinese Journal of Continuing Medical Educa-tion, (08): 727-730.
34. Moons K G, Altman D G, Reitsma J B, et al (2015). Transparent Reporting of a multivariable prediction model for In-dividual Prognosis or Diagnosis (TRI-POD). Explanation and elaboration. Ann Intern Med, 162 (1): W1-73.
35. Liu YA, Yang SW, Li LZ (2021). Research progress on the application of machine learning in disease prediction. Journal of Nursing, 28 (07): 30-34.
2. Armstrong D G, Tan T W, Boulton A, et al (2023). Diabetic Foot Ulcers: a Review. JAMA, 330 (1): 62-75.
3. Zhang X, LI Q, Zhou X, et al (2024). Risk factors for amputation in diabetic foot ulcers: a retrospective analysis. Int Wound J, 21 (4): e14832.
4. Chen L, Sun S, Gao Y, et al (2023). Global mortality of diabetic foot ulcer: a sys-tematic review and meta-analysis of ob-servational studies. Diabetes Obes Metab, 25 (1): 36-45.
5. Guo Q, Ying G, Jing O, et al (2023). Influ-encing factors for the recurrence of di-abetic foot ulcers: a meta-analysis. Int Wound J, 20 (5): 1762-1775.
6. Hsu CR (2019). The key to diabetic foot disease prevention and treatment - In-terpretation of 2019 domestic and inter-national guidelines for diabetic foot prevention and treatment. China Clinical New Medicine,12 (12): 1259-1262.
7. Palazón-bru A, Martín-pérez F, Mares-garcía E, et al (2020). A general presenta-tion on how to carry out a CHARMS analysis for prognostic multivariate models. Stat Med, 39 (23): 3207-3225.
8. Moons K, Wolff R F, Riley R D, et al (2019). PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elabo-ration. Ann Intern Med, 170 (1): W1-W33.
9. Chen XP, Zhang Y, Zhuang YY, et al (2020). PROBAST: an assessment tool for risk of bias in diagnostic or prognostic mul-tifactorial prediction modeling studies. Chinese Journal of Evidence-Based Medicine, 20 (06): 737-744.
10. Wei L, Wang YT, Sun Y (2023). Predictive value of wound, ischemia and foot in-fection grading for recurrence of dia-betic foot ulcers within 1 year and con-struction of prediction model. Journal of Vascular and Endoluminal Vascular Surgery, 9 (12): 1454-1459.
11. Wang H, Li L (2023). Influencing factors and risk modeling of recurrence after ulcer healing in diabetic foot patients. China Journal of Practical Medicine, 23 (01): 54-58.
12. Zhang J, Li HF, Li XM, et al (2023). Con-struction of a diabetic foot ulcer recur-rence risk prediction model: based on logistic regression and support vector machine and BP neural network model. Chinese Family Medicine,26 (32): 4013-4019.
13. Aan D S W, Schut M C, Abu-hanna A, et al (2021). Development of a prediction model for foot ulcer recurrence in peo-ple with diabetes using easy-to-obtain clinical variables. BMJ Open Diabetes Res Care, 9 (1): e002257.
14. Lai JJ, Sun Y (2024). The value of the DMIST scale in predicting recurrence of diabetic foot ulcers within 1 year af-ter healing and the construction of a prediction model. Journal of Vascular and Endovascular Surgery, 10 (01): 6-11.
15. Wang YR Qiu XT, Pan L (2023). Analysis of factors influencing the recurrence of di-abetic foot ulcers after healing and con-struction of risk prediction model. Mili-tary Nursing, 40 (5): 62-65.
16. Aan D S W, Abu-hanna A, Bus S A (2020). Development of a multivariable predic-tion model for plantar foot ulcer recur-rence in high-risk people with diabetes. BMJ Open Diabetes Res Care, 8 (1): e001207.
17. Lv J, Yuan L, Li R, et al (2022). Construction of a predictive model for diabetic foot ulcer recurrence risk. Nursing Research,36 (06): 993-998.
18. Xia L (2023). Construction and validation of a 1-year recurrence risk prediction column chart model for diabetic foot ulcer patients. Jiangsu University, 2023.
19. Ogundimu E O, Altman D G, Collins G S (2016). Adequate sample size for devel-oping prediction models is not simply related to events per variable. J Clin Ep-idemiol, 76: 175-182.
20. Wynants L, Collins G S, Van Calster B (2017). Key steps and common pitfalls in developing and validating risk mod-els. BJOG, 124 (3): 423-432.
21. Mühlenbruch K, Kuxhaus O, di Giuseppe R, et al (2017). Multiple imputation was a valid approach to estimate absolute risk from a prediction model based on case-cohort data. J Clin Epidemiol, 84: 130-141.
22. Mok HF, Chen YP, Han H, et al (2024). Methods and steps of clinical prediction modeling research. Chinese Journal of Evi-dence-Based Medicine, 24 (02): 228-236.
23. Xi LJ, Guo ZY, Yang XK, et al (2023). Ap-plication of LASSO and its extension methods in variable screening for re-gression analysis. Chinese Journal of Preven-tive Medicine, 2023 (01): 107-111.
24. Oley M H, Oley M C, Kepel B J, et al (2024). Hyperbaric Oxygen Therapy for Diabetic Foot Ulcers Based on Wagner Grading: a Systematic Review and Meta-analysis. Plast Reconstr Surg Glob Open, 12 (3): e5692.
25. Zhou Shujie, Zhao Ling, Ke Tingyu (2024). Correlation analysis between common clinical indicators and diabetic foot ul-cers. Journal of Kunming Medical University, 45 (01): 61-66.
26. Wang Y, Xue F, Lin SN, et al (2023). Corre-lation between serum reticulin-1 and vascular endothelial growth factor levels and Wagner's grading in patients with diabetic foot. Chinese Journal of Practical Diagnosis and Therapy, 37 (06): 565-569.
27. Wu J, Bista Raju, CHA PP, et al (2023). Characterization of inflammatory markers in diabetic foot patients and their relationship with foot ulcer prog-nosis. Journal of Sichuan University (Medical Edition), 54 (06): 1233-1238.
28. Huang Z H, Li S Q, Kou Y, et al (2019). Risk factors for the recurrence of diabetic foot ulcers among diabetic patients: a meta-analysis. Int Wound J, 16 (6): 1373-1382.
29. Zhu XL, Liao WQ, Zhang C, et al (2023). Pathogenesis and treatment of diabetic foot ulcers. Chinese Journal of Dermatology and Venereology, 37 (04): 367-372.
30. Xie JX, Dai X, Huang SH, et al (2023). A prospective study of risk factors for di-abetic foot ulcers in the Zhuang popula-tion. Chinese Journal of Gerontology, 43 (01): 55-59.
31. Xia L, Zhuang R, Wu L, et al (2022). Meta-analysis of risk factors associated with recurrent diabetic foot ulcers. Chinese Journal of Modern Nursing, (09): 1143-1148.
32. Sen C K, Roy S, Khanna S (2023). Diabetic Peripheral Neuropathy Associated with Foot Ulcer: One of a Kind. Antioxidant Redox Signal, 10.1089/ars.2022.0093.
33. Yang J, Xing Y, Shi Q (2023). The Michigan Neurologic Screening Scale. The Value of Michigan Nerve Screening Scale, Nociceptive Tactile Warming Test and Vibratory Sensory Threshold in Diag-nosing Diabetic Peripheral Neuropathy. Chinese Journal of Continuing Medical Educa-tion, (08): 727-730.
34. Moons K G, Altman D G, Reitsma J B, et al (2015). Transparent Reporting of a multivariable prediction model for In-dividual Prognosis or Diagnosis (TRI-POD). Explanation and elaboration. Ann Intern Med, 162 (1): W1-73.
35. Liu YA, Yang SW, Li LZ (2021). Research progress on the application of machine learning in disease prediction. Journal of Nursing, 28 (07): 30-34.
| Files | ||
| Issue | Vol 54 No 1 (2025) | |
| Section | Review Article(s) | |
| Keywords | ||
| Predictive model bias risk assessment tool Diabetic foot ulcer Recurrence Risk prediction model Systematic review | ||
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How to Cite
1.
Li Z, Zhang Y-P, Fu G, Chen J-F, Zheng Q-P, Xian X, Wang M. Evaluation of the Effectiveness of Diabetic Foot Ulcer Recurrence Risk Prediction Models: A Systematic Review. Iran J Public Health. 2025;54(1):24-35.
