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

Hüsers, Jens; Hafer, Guido; Heggemann, Jan; Wiemeyer, Stefan; John, Swen Malte; Hübner, Ursula

doi:10.3233/shti210897

Cited by 6 publications

(15 citation statements)

References 8 publications

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“…have evaluated other classifications, a direct comparison was not conducted. Eight studies, 51–58 five at high risk of bias, provided results in the same line for amputation prediction.…”

Section: Resultsmentioning

confidence: 90%

“…Although two of them (from Abbas et al and Ndosi et al 49,50 ) have evaluated other classifications, a direct comparison was not conducted. Eight studies, [51][52][53][54][55][56][57][58] five at high risk of bias, provided results in the same line for amputation prediction.…”

Section: Diabetic Foot Ulcer Assessment Scale (Dfuas)mentioning

confidence: 96%

“…Chuan and colleagues 54 transformed this classification into a score, a value superior to 7 providing an AUC of 0.95 for amputation. Husers et al, both in 2020 and in 2022 55,56 and using the same cohort of patients in both articles, have included this classification in Bayesian-derived models and reported AUC values of 0.79-0.80 for both any amputation or major amputation (for the informed models).…”

Section: Diabetic Foot Ulcer Assessment Scale (Dfuas)mentioning

confidence: 99%

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Classification of foot ulcers in people with diabetes: A systematic review

Monteiro‐Soares

Hamilton

Russell

et al. 2023

Diabetes Metabolism Res

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BackgroundClassification and scoring systems can help both clinical management and audit the outcomes of routine care.AimThis study aimed to assess published systems used to characterise ulcers in people with diabetes to determine which should be recommended to (a) aid communication between health professionals, (b) predict clinical outcome of individual ulcers, (c) characterise people with infection and/or peripheral arterial disease, and (d) audit to compare outcomes in different populations. This systematic review is part of the process of developing the 2023 guidelines to classify foot ulcers from the International Working Group on Diabetic Foot.MethodsWe searched PubMed, Scopus and Web of Science for articles published up to December 2021 which evaluated the association, accuracy or reliability of systems used to classify ulcers in people with diabetes. Published classifications had to have been validated in populations of >80% of people with diabetes and a foot ulcer.ResultsWe found 28 systems addressed in 149 studies. Overall, the certainty of the evidence for each classification was low or very low, with 19 (68%) of the classifications being assessed by ≤ 3 studies. The most frequently validated system was the one from Meggitt‐Wagner, but the articles validating this system focused mainly on the association between the different grades and amputation. Clinical outcomes were not standardized but included ulcer‐free survival, ulcer healing, hospitalisation, limb amputation, mortality, and cost.ConclusionDespite the limitations, this systematic review provided sufficient evidence to support recommendations on the use of six particular systems in specific clinical scenarios.

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“…have evaluated other classifications, a direct comparison was not conducted. Eight studies, 51–58 five at high risk of bias, provided results in the same line for amputation prediction.…”

Section: Resultsmentioning

confidence: 90%

Section: Diabetic Foot Ulcer Assessment Scale (Dfuas)mentioning

confidence: 96%

Section: Diabetic Foot Ulcer Assessment Scale (Dfuas)mentioning

confidence: 99%

See 1 more Smart Citation

Classification of foot ulcers in people with diabetes: A systematic review

Monteiro‐Soares

Hamilton

Russell

et al. 2023

Diabetes Metabolism Res

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“…Most of the current research on the relevant risk factors of diabetic foot has focused on intervening in the progression of diabetic foot to prevent severe ulcers and amputations (21)(22)(23)(24). Although these studies are important, we believe that how to prevent diabetic patients from developing diabetic foot is more important and critical.…”

Section: Discussionmentioning

confidence: 99%

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

Wang

Xue

et al. 2022

Front. Endocrinol.

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AimsTo 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.MethodsWe 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).ResultsMultivariate 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.ConclusionWe 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.

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“…This study presents a deep convolutional neural network trained to classify two types of wound images and extends the toolbox of automatic wound classification, e.g. tools are available that detect wound characteristics like macerations in wound images [7]. The performance metrics show promising results.…”

Section: Discussionmentioning

confidence: 99%

Automatic Wound Type Classification with Convolutional Neural Networks

Malihi

Hüsers

Richter

et al. 2022

Studies in Health Technology and Informatics

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Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The timely identification of this condition is crucial for healing. However, this identification requires expert knowledge unavailable in some care situations. Here, artificial intelligence technology may support clinicians. In this study, we explore the performance of a deep convolutional neural network to classify diabetic foot and venous leg ulcers using wound images. We trained a convolutional neural network on 863 cropped wound images. Using a hold-out test set with 80 images, the model yielded an F1-score of 0.85 on the cropped and 0.70 on the full images. This study shows promising results. However, the model must be extended in terms of wound images and wound types for application in clinical practice.

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

Cited by 6 publications

References 8 publications

Classification of foot ulcers in people with diabetes: A systematic review

Classification of foot ulcers in people with diabetes: A systematic review

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

Automatic Wound Type Classification with Convolutional Neural Networks

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