Predicting the amputation risk for patients with diabetic foot ulceration - a Bayesian decision support tool

Correlation between Neutrophil-to-Lymphocyte Ratio and Diabetic Neuropathy in Chinese Adults with Type 2 Diabetes Mellitus Using Machine Learning Methods

Objective

One of the most frequent consequences of diabetes mellitus has been identified as diabetic peripheral neuropathy (DPN), and numerous inflammatory disorders, including diabetes, have been documented to be reflected by the neutrophil-to-lymphocyte ratio (NLR). This study aimed to explore the correlation between peripheral blood NLR and DPN, and to evaluate whether NLR could be utilized as a novel marker for early diagnosis of DPN among those with type 2 Diabetes Mellitus (T2DM).

Methods

We reviewed the medical records of 1154 diabetic patients treated at Tongji Hospital Affiliated to Tongji University from January 2022 to March 2023. These patients did not have evidence of acute infections, chronic inflammatory status within the past three months. The information included the clinical, laboratory, and demographic characteristics of the patient. Finally, a total of 442 T2DM individuals with reliable, complete, and accessible medical records were recruited, including 216 T2DM patients without complications (DM group) and 226 T2DM patients with complications of DPN (DPN group). One-way ANOVA and multivariate logistic regression were applied to analyze data from the two groups, including peripheral blood NLR values and other biomedical indices. The cohort was divided in a 7 : 3 ratio into training and internal validation datasets following feature selection and data balancing. Based on machine learning, training was conducted using extreme gradient boosting (XGBoost) and support vector machine (SVM) methods. K-fold cross-validation was applied for model assessment, and accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were used to validate the models' discrimination and clinical applicability. Using Shapley Additive Explanations (SHAP), the top-performing model was interpreted.

Results

The values of 24-hour urine volume (24H UV), lower limb arterial plaque thickness (LLAB thickness), carotid plaque thickness (CP thickness), D-dimer and onset time were significantly higher in the DPN group compared to the DM group, whereas the values of urine creatinine (UCr), total cholesterol (TC), low-density lipoprotein (LDL), alpha-fetoprotein (AFP), fasting c-peptide (FCP), and nerve conduction velocity and wave magnitude of motor and sensory nerve shown in electromyogram (EMG) were considerably lower than those in the DM group (P < 0.05, respectively). NLR values were significantly higher in the DPN group compared to the DM group (2.60 ± 4.82 versus 1.85 ± 0.98, P < 0.05). Multivariate logistic regression analysis revealed that NLR (P = 0.008, C = 0.003) was a risk factor for DPN. The multivariate logistic regression model scores were 0.6241 for accuracy, 0.6111 for precision, 0.6667 for recall, 0.6377 for F1, and 0.6379 for AUC. Machine learning methods, XGBoost and SVM, built prediction models, showing that NLR can predict the onset of DPN. XGBoost achieved an accuracy of 0.6541, a precision of 0.6316, a recall of 0.7273, a F1 value of 0.6761, and an AUC value of 0.690. SVM scored an accuracy of 0.5789, a precision of 0.5610, a recall of 0.6970, an F1 value of 0.6216, and an AUC value of 0.6170.

Conclusions

Our findings demonstrated that NLR is highly correlated with DPN and is an independent risk factor for DPN. NLR might be a novel indicator for the early diagnosis of DPN. XGBoost and SVM models have great predictive performance and could be reliable tools for the early prediction of DPN in T2DM patients. This trial is registered with ChiCTR2400087019.

Reamputation prevalence after minor feet amputations in patients with diabetic foot, a cross sectional study

INTRODUCTION

Reported prevalence for reamputation in diabetic foot is diverse, risk factors are not clear for minor amputations. This study aims to determine the prevalence for reamputation in diabetic foot from minor amputations and to evaluate associated factors for such outcome.

METHODS

Cross sectional study developed in 2hospitals. Patients hospitalized for diabetic foot ulcer requiring a minor amputation were included. A descriptive analysis of all variables is presented, as well as prevalence ratios (PR) and a multivariate logistic regression.

RESULTS

The prevalence was of 48% for 15 years. Toes were the most frequent minor amputation that required reamputation and above the knee amputation was the most frequent reamputation level (45%). Variables whose PR was associated to reamputation risk were: smoking history (PR 1.32, CI 95%: 1.02-1.67, P=0.03), vascular occlusion in doppler (PR 1.47, CI 95%: 1.11-1.73, P=0.01), revascularization (PR 1.73, CI 95%: 1.31-2.14, P=0.00002), Wagner> 3 (PR 1.75, CI 95%: 1.16-1.84, P=0.01) and leucocytosis> 11,000 (PR 1.39, CI 95%: 1.07-1.68, P=0.01). Leucocytosis> 11,000, Wagner> 3, vascular occlusion in doppler and revascularization were the variables that best predicted the outcome. Furthermore, leucocytosis was the best variable for predicting reamputation (OR 2.4, CI 95%: 1.1-5.6, P=0.04).

CONCLUSIONS

Reamputation prevalence was 48%. The toes were the minor amputation more frequently requiring reamputation and above the knee was the most frequent reamputation level. Risk for reamputation was associated with variables related to vascular compromise and infection.

Prediction models for amputation after diabetic foot: systematic review and critical appraisal

BACKGROUND

Numerous studies have developed or validated prediction models aimed at estimating the likelihood of amputation in diabetic foot (DF) patients. However, the quality and applicability of these models in clinical practice and future research remain uncertain. This study conducts a systematic review and assessment of the risk of bias and applicability of amputation prediction models among individuals with DF.

METHODS

A comprehensive search was conducted across multiple databases, including PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedical Literature Database (CBM), and Weipu (VIP) from their inception to December 24, 2023. Two investigators independently screened the literature and extracted data using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability.

RESULTS

A total of 20 studies were included in this analysis, comprising 17 development studies and three validation studies, encompassing 20 prediction models and 11 classification systems. The incidence of amputation in patients with DF ranged from 5.9 to 58.5%. Machine learning-based methods were employed in more than half of the studies. The reported area under the curve (AUC) varied from 0.560 to 0.939. Independent predictors consistently identified by multivariate models included age, gender, HbA1c, hemoglobin, white blood cell count, low-density lipoprotein cholesterol, diabetes duration, and Wagner's Classification. All studies were found to exhibit a high risk of bias, primarily attributed to inadequate handling of outcome events and missing data, lack of model performance assessment, and overfitting.

CONCLUSIONS

The assessment using PROBAST revealed a notable risk of bias in the existing prediction models for amputation in patients with DF. It is imperative for future studies to concentrate on enhancing the robustness of current prediction models or constructing new models with stringent methodologies.

The Effect of Lavandula stoechas on Wound Healing in an Experimental Diabetes Model

INTRODUCTION

Diabetic foot is a consequential and dangerous complication of diabetes, contributing to decreased quality of life, escalated hospitalizations, and increased mortality rates. Using an experimental model of diabetes, this study aims to investigate the effect of Lavandula stoechas on wound healing.

METHODS

A total of 35 albino Wistar rats, 250-350 grams in weight, were used. The rats were divided into five groups, seven rats in each group. Of these, 21 rats were induced with 50 mg/kg streptozotocin (STZ) to mimic the diabetic condition. Additionally, 14 rats remained non-diabetic and served as the control group. The diabetic rats were further divided into three subgroups. The non-diabetic group was split into two subgroups based on the dressing materials used (allicin, physiological serum, and control). Wound dimensions were assessed on Days 0, 7, 14, and 21. Biopsies were taken from the wound sites at the same time.

RESULTS

There were significant differences between groups on Days 7, 14, and 21. The percentage of healing was highest in the Lavandula Stoechas group on Days 7, 14, and 21. Microscopic examination of the biopsies supported accelerated wound healing on Days 7 and 14. Reduced mononuclear cell density and increased hair follicle and adipose tissue development were observed in the DM (diabetes mellitus)-Lavandula Stoechas group on Day 7. On Day 14, the DM-Lavandula Stoechas group increased collagen levels and hair follicles. Similarly, the non-DM-Lavandula Stoechas group showed reduced bullae, dermal edema, and intraepithelial edema on Day 7. This was followed by increased fibroblast levels on Day 14.

CONCLUSIONS

In conclusion, this study provides compelling evidence for the potential of Lavandula stoechas extract in the enhancement of diabetic wound healing. The multiple interactions revealed here highlight the need for further investigation into the underlying mechanisms. A cost-effective use of Lavandula stoechas opens up promising prospects in managing diabetic foot healing. This warrants additional research and clinical translation.

Clinical characteristics and risk factors of lower extremity amputation in the diabetic inpatients with foot ulcers

OBJECTIVES

To analyze clinical characteristics of the diabetic inpatients with foot ulcers and explore the risk factors of lower extremity amputation (LEA) in West China Hospital of Sichuan University.

METHODS

A retrospective analysis was performed based on the clinical data of the patients with diabetic foot ulcer (DFU) hospitalized in West China Hospital of Sichuan University from January 1, 2012 to December 31, 2020. The DFU patients were divided into three groups: non-amputation, minor amputation, and major amputation groups. The ordinal logistic regression analysis was used to identify the risk factors for LEA.

RESULTS

992 diabetic patients (622 males and 370 females) with DFU were hospitalized in the Diabetic Foot Care Center of Sichuan University. Among them, 72 (7.3%) (55 minor amputations and 17 major amputations) cases experienced amputation, and 21(2.1%) refused amputation. Excluding the patients who refused amputation, the mean age and duration of diabetes of and HbA1c the 971 patients with DFU, were 65.1 ± 12.3 years old, 11.1 ± 7.6 years, and 8.6 ± 2.3% respectively. The patients in the major amputation group were older and had longer course of diabetes for a longer period of time than those in the non-amputation and minor amputation groups. Compared with the non-amputation patients (55.1%), more patients with amputation (minor amputation (63.5%) and major amputation (88.2%)) suffered from peripheral arterial disease (P=0.019). The amputated patients had statistically lower hemoglobin, serum albumin and ankle brachial index (ABI), but higher white blood cell, platelet counts, fibrinogen and C-reactive protein levels. The patients with amputation had a higher incidence of osteomyelitis (P = 0.006), foot gangrene (P < 0.001), and a history of prior amputations (P < 0.001) than those without amputation. Furthermore, a history of prior amputation (odds ratio 10.194; 95% CI, 2.646-39.279; P=0.001), foot gangrene (odds ratio 6.466; 95% CI, 1.576-26.539; P=0.010) and ABI (odds ratio 0.791; 95% CI, 0.639-0.980; P = 0.032) were significantly associated with LEAs.

CONCLUSIONS

The DFU inpatients with amputation were older with long duration of diabetes, poorly glycemic control, malnutrition, PAD, severe foot ulcers with infection. A history of prior amputation, foot gangrene and a low ABI level were the independent predictors of LEA. Multidisciplinary intervention for DFU is essential to avoid amputation of the diabetic patients with foot ulcer.

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.

Classification Model for Diabetic Foot, Necrotizing Fasciitis, and Osteomyelitis

Simple Summary

Necrotizing fasciitis (NF) and osteomyelitis (OM) are severe complications in patients with diabetic foot ulcers (DFUs). Although NF and OM often cause results including limb amputation and death, definite diagnoses of these are challenging. To aid the prompt and proper diagnosis of NF and OM in patients with DFU, we developed and evaluated a novel prediction model based on machine learning technology. In summary, our prediction model appropriately discriminated the NF and OM from diabetic foot. Moreover, this prediction model has advantages in that it is based on the demographic data and routine laboratory results, which requires no additional examinations which are complicated or expensive.

Abstract

Diabetic foot ulcers (DFUs) and their life-threatening complications, such as necrotizing fasciitis (NF) and osteomyelitis (OM), increase the healthcare cost, morbidity and mortality in patients with diabetes mellitus. While the early recognition of these complications could improve the clinical outcome of diabetic patients, it is not straightforward to achieve in the usual clinical settings. In this study, we proposed a classification model for diabetic foot, NF and OM. To select features for the classification model, multidisciplinary teams were organized and data were collected based on a literature search and automatic platform. A dataset of 1581 patients (728 diabetic foot, 76 NF, and 777 OM) was divided into training and validation datasets at a ratio of 7:3 to be analyzed. The final prediction models based on training dataset exhibited areas under the receiver operating curve (AUC) of the 0.80 and 0.73 for NF model and OM model, respectively, in validation sets. In conclusion, our classification models for NF and OM showed remarkable discriminatory power and easy applicability in patients with DFU.

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.