Developing a foot ulcer risk model: what is needed to do this in a real-world primary care setting?

Incidence of the first diabetic foot ulcer: A systematic review and meta-analysis

AIM

Investigate the incidence of the first diabetic foot ulcer.

METHOD

This is a systematic review with meta-analysis of cohort studies following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and using RevMan software. A systematic search of Medline databases via PubMed, Embase, Lilacs, Scopus databases, and Web of Science was performed until July 2021. In addition to investigating the incidence of the first diabetic foot ulcer, the influence of the variables of the Human Development Index (HDI), glycated hemoglobin, and follow-up time of the participants on the incidence of the first diabetic foot ulcer (DFU) was analyzed through meta-regression. For the meta-analysis of cumulative incidence and possible variable associations, RevMan software was used in the Metaprop data package with 95% confidence interval (CI).

RESULTS

A total of 9,772 articles were identified out of which 87 were selected and 12 studies ultimately included in the systematic review and meta-analysis. The meta-analysis of cumulative incidence was 5.65% (95% CI: 4.20; 7.57). By meta-regression, a significant inverse association was identified between DFU incidence and HDI (estimate - 2.38; 95% CI - 4.10--0.67; p = 0.01).

CONCLUSION

The study presents the cumulative incidence for the first DFU, an inexistent datum in the national and international literature, and the HDI was inversely associated with the incidence of DFU.

Development and Validation of a Risk Prediction Model for Foot Ulcers in Diabetic Patients

BACKGROUND

The current study analyzed the status and the factors of foot ulcers in diabetic patients and developed a nomogram and web calculator for the risk prediction model of diabetic foot ulcers.

METHODS

This was a prospective cohort study that used cluster sampling to enroll diabetic patients in the Department of Endocrinology and Metabolism in a tertiary hospital in Chengdu from July 2015 to February 2020. The risk factors for diabetic foot ulcers were obtained by logistic regression analysis. Nomogram and web calculator for the risk prediction model were constructed by R software.

RESULTS

The incidence of foot ulcers was 12.4% (302/2432). Logistic stepwise regression analysis showed that BMI (OR: 1.059; 95% CI 1.021-1.099), abnormal foot skin color (OR: 1.450; 95% CI 1.011-2.080), foot arterial pulse (OR: 1.488; 95% CI: 1.242-1.778), callus (OR: 2.924; 95%: CI 2.133-4.001), and history of ulcer (OR: 3.648; 95% CI: 2.133-5.191) were risk factors for foot ulcers. The nomogram and web calculator model were developed according to risk predictors. The performance of the model was tested, and the testing data were as follows: AUC (area under curve) of the primary cohort was 0.741 (95% CI: 0.7022-0.7799), and AUC of the validation cohort was 0.787 (95% CI: 0.7342-0.8407); the Brier score of the primary cohort was 0.098, and the Brier score of the validation cohort was 0.087.

CONCLUSIONS

The incidence of diabetic foot ulcers was high, especially in diabetic patients with a history of foot ulcers. This study presented a nomogram and web calculator that incorporates BMI, abnormal foot skin color, foot arterial pulse, callus, and history of foot ulcers, which can be conveniently used to facilitate the individualized prediction of diabetic foot ulcers.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Mortality in 98 type 1 diabetes mellitus and type 2 diabetes mellitus: Foot ulcer location is an independent risk determinant

INTRODUCTION

We previously demonstrated in both a longitudinal study and in meta-analysis (pooled relative-risk RR, 2.45) that all-cause mortality is significantly higher in people with diabetes foot ulceration (DFU) than with those without a foot ulcer. In this prospective study, we looked at the factors linked to mortality after presentation to podiatry with DFU.

METHODS

Ninety-eight individuals recruited consecutively from the Salford Royal Hospital Multidisciplinary Foot Clinic in Spring 2016 were followed up for up to 48 months. Data concerning health outcomes were extracted from the electronic patient record (EPR).

RESULTS

Seventeen people (17) had type 1 diabetes mellitus, and 81 had type 2 diabetes mellitus. Thirty-one were women. The mean age (range) was 63.6 (28-90) years with maximum diabetes duration 45 years. Mean HbA1c was 72 (95% CI: 67-77) mmol/mol; 97% had neuropathy (International Working Group on the Diabetic Foot (IWGDF) monofilament); 62% had vascular insufficiency (Doppler studies); 69% of ulcers were forefoot, and 23% of ulcers were hind foot in location. Forty of 98 (40%) patients died in follow-up with 27% of death certificates including sepsis (not foot-related) and 35% renal failure as cause of death. Multivariate regression analysis indicated a 6.3 (95% CI: 3.9-8.1) fold increased risk of death with hind foot ulcer, independent of age/BMI/gender/HbA1c/eGFR/total cholesterol level.

CONCLUSION

This prospective study has indicated a very high long-term mortality rate in individuals with DFU, greater for those with a hind foot ulcer and shown a close relation between risk of sepsis/renal failure and DFU mortality, highlighting again the importance of addressing all risk factors as soon as people present with a foot ulcer.

Prognostic models for predicting the risk of foot ulcer or amputation in people with type 2 diabetes: a systematic review and external validation study

AIMS/HYPOTHESIS

Approximately 25% of people with type 2 diabetes experience a foot ulcer and their risk of amputation is 10-20 times higher than that of people without type 2 diabetes. Prognostic models can aid in targeted monitoring but an overview of their performance is lacking. This study aimed to systematically review prognostic models for the risk of foot ulcer or amputation and quantify their predictive performance in an independent cohort.

METHODS

A systematic review identified studies developing prognostic models for foot ulcer or amputation over minimal 1 year follow-up applicable to people with type 2 diabetes. After data extraction and risk of bias assessment (both in duplicate), selected models were externally validated in a prospective cohort with a 5 year follow-up in terms of discrimination (C statistics) and calibration (calibration plots).

RESULTS

We identified 21 studies with 34 models predicting polyneuropathy, foot ulcer or amputation. Eleven models were validated in 7624 participants, of whom 485 developed an ulcer and 70 underwent amputation. The models for foot ulcer showed C statistics (95% CI) ranging from 0.54 (0.54, 0.54) to 0.81 (0.75, 0.86) and models for amputation showed C statistics (95% CI) ranging from 0.63 (0.55, 0.71) to 0.86 (0.78, 0.94). Most models underestimated the ulcer or amputation risk in the highest risk quintiles. Three models performed well to predict a combined endpoint of amputation and foot ulcer (C statistics >0.75).

CONCLUSIONS/INTERPRETATION

Thirty-four prognostic models for the risk of foot ulcer or amputation were identified. Although the performance of the models varied considerably, three models performed well to predict foot ulcer or amputation and may be applicable to clinical practice.

Competitive neural layer-based method to identify people with high risk for diabetic foot

BACKGROUND AND OBJECTIVE

To automatically identify patients with diabetes mellitus (DM) who have high risk of developing diabetic foot, via an unsupervised machine learning technique.

METHODS

We collected a new database containing 54 known risk factors from 250 patients diagnosed with diabetes mellitus. The database also contained a separate validation cohort composed of 73 subjects, where the perceived risk was annotated by expert nurses. A competitive neuron layer-based method was used to automatically split training data into two risk groups.

RESULTS

We found that one of the groups was composed of patients with higher risk of developing diabetic foot. The dominant variables that described group membership via our method agreed with the findings from other studies, and indicated a greater risk for developing such a condition. Our method was validated on the available test data, reaching 71% sensitivity, 100% specificity, and 90% accuracy.

CONCLUSIONS

Unsupervised learning may be deployed to screen patients with diabetes mellitus, pointing out high-risk individuals who require priority follow-up in the prevention of diabetic foot with very high accuracy. The proposed method is automatic and does not require clinical examinations to perform risk assessment, being solely based on the information of a questionnaire answered by patients. Our study found that discriminant variables for predicting risk group membership are highly correlated with expert opinion.

A novel method to quantify cutaneous vascular innervation

INTRODUCTION

To develop a new method to quantify the density of nerves, vessels, and the neurovascular contacts, we studied skin biopsies in diabetes and control subjects.

METHODS

Skin biopsies with dual immunofluorescent staining were used to visualize nerves and blood vessels. The density of nerves, vessels, and their neurovascular contacts were quantified with unbiased stereology. Results were compared with examination findings, validated questionnaires, and autonomic function.

RESULTS

In tissue from 19 controls and 20 patients with diabetes, inter-rater and intra-rater intraclass correlation coefficients were high (>0.85; P < .001) for all quantitative methods. In diabetes, the nerve densities (P < .05), vessel densities (P < .01), and the neurovascular densities (P < .01) were lower compared with 20 controls. Results correlated with autonomic function, examination and symptom scores.

DISCUSSION

We report an unbiased, stereological method to quantify the cutaneous nerve, vessel and neurovascular density and offer new avenues of investigation into cutaneous neurovascular innervation in health and disease.

Foot ulceration and its association with mortality in diabetes mellitus: a meta-analysis

BACKGROUND

Diabetic foot ulcers portend an almost twofold increase in all-cause mortality compared with diabetes on its own.

AIM

To investigate the association between diabetic foot ulcers and risk of death.

METHODS

We performed a meta-analysis of all observational studies investigating the association between diabetic foot ulcers and all-cause mortality. Risk ratios and risk differences were pooled in a random-effects model. The I² statistic was used to quantify heterogeneity between studies.

RESULTS

Altogether, we identified 11 studies that reported 84 131 deaths from any cause in 446 916 participants with diabetes during a total of 643 499 person-years of follow-up. The crude event rate for all-cause mortality in individuals with diabetes who did not develop foot ulceration was 22% lower at 181.5 deaths (per 1000 person-years) than in those who developed foot ulcers (230.8 per 1000 person-years). Diabetic foot ulceration was associated with an increased risk of all-cause mortality (pooled relative risk 2.45, 95% CI 1.85-2.85). We did not observe any tangible differences in risk of all-cause mortality from diagnosis in studies reporting a mean duration of follow-up of ≤3 years (relative risk 2.43, 95% CI 2.27-2.61) or >3 years (relative risk 2.26, 95% CI 2.13-2.40) years. Funnel plot inspection revealed no significant publication bias among studies included in this meta-analysis.

CONCLUSIONS

Our study shows an excess rate of all-cause mortality in people with diabetic foot ulceration when compared to those without foot ulceration. It is imperative that early interventions to prevent foot ulceration and modify cardiovascular disease risk factors are put in place to reduce excess mortality.