Outcome predictors for wound healing in patients with a diabetic foot ulcer

Lessons learnt from an aggressive tumour masquerading as a neuropathic heel ulcer: a case report

Cutaneous malignant melanoma (cMM) can develop at any site, but one-third of cases primarily affect the lower extremities, with ankle and foot lesions representing 3-15% of all cases. However, cMM may become a clinical conundrum when it presents as chronic ulceration that is clinically indiscernible from other lower extremity ulcers in patients with diabetes. We present the case of a 71-year-old female patient with a longstanding history of diabetes, hypertension, obesity, chronic kidney disease and heart failure who presented to our hospital with a fungating heel ulcer. The lesion was initially managed in another hospital as a neuropathic diabetic foot ulcer (DFU), treated by multiple local wound debridement. However, the ulcer progressed into a fungating heel lesion that interfered with the patient's mobility and quality of life. Consequently, the patient was referred to our specialist diabetic foot service for further management. Excisional biopsy of the lesion disclosed a cMM. Positron emission tomography/computed-tomography scanning revealed hypermetabolic ipsilateral inguinal lymphadenopathy, and a right cerebral metastasis for which palliative chemotherapy was initiated. Immunotherapy was considered, but the patient died before it was started. Atypical foot ulcers in patients with diabetes warrant a careful diagnostic approach, especially for recalcitrant cutaneous lesions not responding to standard therapies. Conscientious management, without undue delay in obtaining a histopathological diagnosis, might lead to early diagnosis of melanoma and potentially more favourable outcomes. This case highlights the importance of consideration of atypical foot lesions, in general practice in addition to referral centres, to try to identify alarming features and act accordingly.

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

BACKGROUND

Classification and scoring systems can help both clinical management and audit the outcomes of routine care.

AIM

This 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.

METHODS

We 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.

RESULTS

We 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.

CONCLUSION

Despite the limitations, this systematic review provided sufficient evidence to support recommendations on the use of six particular systems in specific clinical scenarios.

Prognostic factors for delayed healing of complex wounds in adults: A scoping review

Complex or hard-to-heal wounds continue to be a challenge because of the negative impact they have on patients, caregivers, and all the associated costs. This study aimed to identify prognostic factors for the delayed healing of complex wounds. Five databases and grey literature were the sources used to research adults with pressure ulcers/injuries, venous leg ulcers, critical limb-threatening ischaemia, or diabetic foot ulcers and report the prognostic factors for delayed healing in all care settings. In the last 5 years, a total of 42 original peer-reviewed articles were deemed eligible for this scoping review that followed the JBI recommendations and checklist PRISMA-ScR. The most frequent prognostic factors found with statistical significance coinciding with various wound aetiologies were: gender (male), renal disease, diabetes, peripheral arterial disease, the decline in activities of daily life, wound duration, wound area, wound location, high-stage WIfI classification, gangrene, infection, previous ulcers, and low ankle brachial index. It will be essential to apply critical appraisal tools and assessment risk of bias to the included studies, making it possible to make recommendations for clinical practice and build prognostic models. Future studies are recommended because the potential for healing through identification of prognostic factors can be determined, thus allowing an appropriate therapeutic plan to be developed.

Effects of Intensive Glycemic Control on Serum Exosome miR-126-3p and miR-125b-1-3p Levels and Wound Healing in Patients with Diabetic Ulcers

Objective

Intensive glycemic control and exosomal miRNAs have both been reported to improve wound repair in diabetic ulcers. In this study, we aimed to investigate the effects of intensive glycemic control on serum exosome microRNA-126-3p (miR-126-3p), microRNA-125b-1-3p (miR-125b-1-3p), and wound healing in patients with diabetic ulcers.

Methods

Herein, 45 diabetic patients with an ulcer, aged 35-75 years old, were randomly assigned to the intensive glycemic control group (n = 21) and the conventional glycemic control group (n = 24). Serum exosomes were extracted in the laboratory and assessed by Western blotting, transmission electron microscopy, and nanoparticle tracking analysis. The expression of miR-126-3p and miR-125b-1-3p was validated using quantitative real-time polymerase chain reaction. The wound healing of each diabetic ulcer patient was measured and imaged; additionally, clinical and follow-up data were collected. Finally, the clinical and laboratory data were combined for statistical analysis.

Results

Intensive glycemic control was significantly more conducive to wound healing and infection control than conventional glycemic control (P < 0.05). Serum exosomal miR-126-3p was negatively correlated with fasting plasma glucose levels (r = 0.34, P < 0.05) and positively associated with the wound healing rate (r = 0.45, P < 0.01). The level of miR-126-3p in the intensive glycemic control group was significantly higher than that in the conventional glycemic control group (P < 0.01). Serum exosomal miR-125b-1-3p was not correlated with blood glucose levels (r = 0.03, P > 0.05) and was positively associated with the wound healing rate (r = 0.33, P < 0.05). No significant difference was observed in the level of miR-125b-1-3p between the intensive and conventional glycemic control groups. Regarding the prognosis of diabetic ulcers, the intensive glycemic control group was better than the conventional group (Z = -2.02, P < 0.05).

Conclusion

Serum exosome (miR-125b-1-3p and miR-126-3p) levels are correlated with wound healing in diabetic ulcers. Intensive glycemic control increases the serum exosomal miR-126-3p level, which might be one of the mechanisms that promotes wound healing in diabetic ulcers.

Machine learning for the prediction of minor amputation in University of Texas grade 3 diabetic foot ulcers

Minor amputations are performed in a large proportion of patients with diabetic foot ulcers (DFU) and early identification of the outcome of minor amputations facilitates medical decision-making and ultimately reduces major amputations and deaths. However, there are currently no clinical predictive tools for minor amputations in patients with DFU. We aim to establish a predictive model based on machine learning to quickly identify patients requiring minor amputation among newly admitted patients with DFU. Overall, 362 cases with University of Texas grade (UT) 3 DFU were screened from tertiary care hospitals in East China. We utilized the synthetic minority oversampling strategy to compensate for the disparity in the initial dataset. A univariable analysis revealed nine variables to be included in the model: random blood glucose, years with diabetes, cardiovascular diseases, peripheral arterial diseases, DFU history, smoking history, albumin, creatinine, and C-reactive protein. Then, risk prediction models based on five machine learning algorithms: decision tree, random forest, logistic regression, support vector machine, and extreme gradient boosting (XGBoost) were independently developed with these variables. After evaluation, XGBoost earned the highest score (accuracy 0.814, precision 0.846, recall 0.767, F1-score 0.805, and AUC 0.881). For convenience, a web-based calculator based on our data and the XGBoost algorithm was established (https://dfuprediction.azurewebsites.net/). These findings imply that XGBoost can be used to develop a reliable prediction model for minor amputations in patients with UT3 DFU, and that our online calculator will make it easier for clinicians to assess the risk of minor amputations and make proactive decisions.

Bottom-up approach to build a 'precision' risk factor classification for diabetic foot ulcer healing. Proof-of-concept

AIMS

Diabetic foot ulcers (DFU) have a complex multifactorial pathophysiology. It is crucial to identify essential prognostic variables to streamline therapeutic actions and quality-of-care audits. Although SINBAD and University of Texas (UT), the most frequently used prognostic classification systems, were prospectively validated, not all individual parameters were shown to have consistent associations with healing. In this study, we used a bottom-up approach relying on robust methods to identify independent predictors of DFU healing.

METHODS

1,664 DFU patients were included by 34 Belgian diabetic foot clinics (DFCs). Twenty-one patient- and foot-related characteristics were recorded at presentation. Predictors of healing were identified using multivariable Cox proportional hazard regression. Multivariable models were built using backward regression with multiple imputation of missing values and bootstrapping.

RESULTS

Five essential independent variables were identified: presentation delay, history of minor amputation, ulcer location, surface area and ischemia. This 5 variable-model showed a better performance compared to models based on existing classification systems.

CONCLUSIONS

A bottom-up approach was used to build a prognostic classification for DFU healing based on large databases. It offers new insights and allows to tailor the classification to certain clinical settings. These 5 parameters could be used as a 'precision classification' for specialized DFCs.

Machine Learning Models for Predicting the Risk of Hard-to-Heal Diabetic Foot Ulcers in a Chinese Population

BACKGROUND

Early detection of hard-to-heal diabetic foot ulcers (DFUs) is vital to prevent a poor prognosis. The purpose of this work was to employ clinical characteristics to create an optimal predictive model of hard-to-heal DFUs (failing to decrease by >50% at 4 weeks) based on machine learning algorithms.

METHODS

A total of 362 DFU patients hospitalized in two tertiary hospitals in eastern China were enrolled in this study. The training dataset and validation dataset were split at a ratio of 7:3. Univariate logistic analysis and clinical experience were utilized to screen clinical characteristics as predictive features. The following six machine learning algorithms were used to build prediction models for differentiating hard-to-heal DFUs: support vector machine, the naïve Bayesian (NB) model, k-nearest neighbor, general linear regression, adaptive boosting, and random forest. Five cross-validations were employed to realize the model's parameters. Accuracy, precision, recall, F1-scores, and AUCs were utilized to compare and evaluate the models' efficacy. On the basis of the best model identified, the significance of each characteristic was evaluated, and then an online calculator was developed.

RESULTS

Independent predictors for model establishment included sex, insulin use, random blood glucose, wound area, diabetic retinopathy, peripheral arterial disease, smoking history, serum albumin, serum creatinine, and C-reactive protein. After evaluation, the NB model was identified as the most generalizable model, with an AUC of 0.864, a recall of 0.907, and an F1-score of 0.744. Random blood glucose, C-reactive protein, and wound area were determined to be the three most important influencing factors. A corresponding online calculator was created (https://predicthardtoheal.azurewebsites.net/).

CONCLUSION

Based on clinical characteristics, machine learning algorithms can achieve acceptable predictions of hard-to-heal DFUs, with the NB model performing the best. Our online calculator can assist doctors in identifying the possibility of hard-to-heal DFUs at the time of admission to reduce the likelihood of a dismal prognosis.

Diabetic foot ulcers: Retrospective comparative analysis from Sicily between two eras

Aim

The aim of this study was to analyze changes in the incidence, management and mortality of DFU in Sicilian Type 2 diabetic patients hospitalized between two eras, i.e. 2008–2013 and 2014–2019.

Methods

We compared the two eras, era1: 2008–13, era2: 2014–19. In era 1, n = 149, and in era 2, n = 181 patients were retrospectively enrolled.

Results

In the population hospitalized for DFU in 2008–2013, 59.1% of males and 40.9% of females died, whilst in 2014–2019 65.9% of males and 34.1% of females died. Moderate chronic kidney disease (CKD) was significantly higher in patients that had died than in ones that were alive (33% vs. 43%, p < 0.001), just as CKD was severe (14.5% vs. 4%, p < 0.001). Considering all together the risk factors associated with mortality, at Cox regression multivariate analysis only moderate-severe CKD (OR 1.61, 95% CI 1.07–2.42, p 0.021), age of onset greater than 69 years (OR 2.01, 95% CI 1.37–2.95, p <0.001) and eGFR less than 92 ml/min (OR 2.84, 95% CI 1.51–5.34, p 0.001) were independently associated with risk of death.

Conclusions

Patients with DFU have high mortality and reduced life expectancy. Age at onset of diabetic foot ulcer, eGFR values and CKD are the principal risk factors for mortality.

Ginsenoside Rg1 promoted the wound healing in diabetic foot ulcers via miR-489-3p/Sirt1 axis

PURPOSE

Diabetic foot ulcers (DFUs) are common complications of high severity for diabetes. Ginsenoside Rg1 (Rg1) has the potential for diabetes and cardiovascular diseases therapy. This research aimed at exploring the regulation of Rg1 on DFUs treatment and the underlying mechanism.

METHODS

Human umbilical vein endothelial cells (HUVECs) incubated with high-glucose culture medium were established for induction of diabetes model. The MTT assay, Annexin V/PI assay and oxidative stress detection were carried out on high-glucose-induced HUVECs. Dual-luciferase reporter assay was performed to prove the interaction of miR-489-3p and Sirt1. DFUs model was established to determine the efficiency of Rg1 and miR-489-3p in wound closure of DFUs in vivo.

RESULTS

Rg1 promoted cell proliferation, migration and angiogenesis, and reduced cell apoptosis in high-glucose-induced HUVECs. Knockdown of miR-489-3p alleviated the high-glucose-induced damage to HUVECs, while overexpression of miR-489-3p attenuated the protection effects of Rg1. Overexpression Sirt1 promoted wound healing in DFUs and Sirt1 was a direct target of miR-489-3p. In addition, animal experiments demonstrated that Rg1 promoted wound closure by regulating miR-489-3p/Sirt1 axis.

CONCLUSIONS

Rg1 alleviated the DFUs by increasing Sirt1 expression via miR-489-3p downregulation and promoting activation of PI3K/AKT/eNOS signaling.

Outcome predictors for wound healing in patients with a diabetic foot ulcer

The aim of this study was to identify diabetic foot ulcer (DFU) patients at risk for the development of a hard-to-heal wound. This is a post-hoc analysis of a prospective cohort study including a total of 208 patients with a DFU. The primary endpoints were time to healing and the development of a hard-to-heal-wound. Univariable and multivariable logistic and Cox regression analysis were used to study the associations of patient characteristics with the primary endpoints. The number of previous DFUs [odds ratio (OR): 1.42, 95% confidence interval (CI): 1.01-1.99, P = .04], University of Texas (UT) classification grade 2 (OR: 2.93, 95% CI: 1.27-6.72, P = .01), UT classification grade 3 (OR: 2.80, 95% CI: 1.17-6.71, P = .02), and a diagnosis of foot stand deformation (OR: 1.54, 95% CI: 0.77-3.08, P = .05) were significantly associated with the development of a hard-to-heal wound. Only UT classification grade 3 (HR: 0.61, 95% CI: 0.41-0.90, P = .01) was associated with time to healing. The number of previous DFUs, UT classification grade, and a diagnosis of foot deformation are significantly associated with development of a hard-to-heal wound in patients with a DFU. The only predictor significantly associated with time to healing was UT classification grade 3. These patient characteristics can be used to identify patients at risk for the development of hard-to-heal wounds, who might need an early intervention to prevent wound problems.