Predictors of treatment failure for diabetic foot complications

NewsBot · Feb 14, 2023

The use of the artificial neural network for three-month prognosis in diabetic foot syndrome
Anna A. Poradzka, Leszek Czupryniak
Journal of Diabetes and its Complications; Volume 37, Issue 2, February 2023, 108392

Aims
Making a reliable prognosis in new patients with diabetic foot syndrome (DFS) is challenging. We used the artificial neural network (ANN) to identify the patients who didn't heal in three months. We provided data for an application which helps predict the course of healing in DFS.

Methods
175 in-patients (213 limbs) with DFS ulcerations were enrolled in this prospective observational study and were followed up for three months. Thirty-five clinical variables were included in the statistical analysis.

Results
Six significant variables predicting the outcome of DFS treatment were identified: probe-to-bone test, presence of blood flow in Doppler probe, prior amputation within the foot, erythrocyte sedimentation rate, the area and duration of the ulceration. ANN was created with nine input neurons, six hidden nodes and two output neurons. The area under the ROC curve was 0.85. The total accuracy was 82.21 %, sensitivity 91.6 %, specificity 66.18 %.

Conclusions
ANN as a new prognosis method in DFS ulcers can be reliably used in the prediction, helping physicians and patients predict the course and outcome of the treatment. The algorithm can be particularly useful in identifying individuals who fail to be healed.
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NewsBot · Feb 16, 2023

Prediction of outcome in individuals with diabetic foot ulcers
based on diabetic ulcer severity score (DUSS) in a tertiary care
hospital, south india: A longitudinal study
Kalaiselvi Ponnusamy
Source

Background: In an effort to better categorise ulcers and enable comparison of the results of
standard management, a number of classification schemes for foot ulcers have been
developed. One of the most recent classification systems for wounds is the DUSS (Diabetic
Ulcer Severity Score), which still needs to be validated in order to evaluate the results of
diabetic foot ulcers. So, using the diabetic ulcer severity score, we conducted this study to
ascertain the prognosis of diabetic foot ulcers (DUSS) Methods: Total of 100 Diabetic
patients attending surgical outpatient clinic or admitted into the tertiary care hospital with
diabetic foot ulcers from January 2018 to June 2019 were included in the study.
Sociodemographic data along with information related to diabetes were collected using a
pretested semi structured questionnaire and the DUSS score was calculated for each patient
and analysed using STATA version 14. Results: The mean age of the study participants with
Diabetic foot was 57.2 (11.7). Males were commonly affected by Diabetic foot ulcers
accounting to 77%. Most commonly ulcers were of DUSS Score of 1 followed by Score 0.
Major amputation was done for 12% of patients, minor amputation was done in 16% of
patients in our study. Patients with raised DUSS score has more probability of having
amputation when to patients with lower score. Conclusion: The DUSS scoring system offers
a simple diagnostic tool for estimating the likelihood of healing or amputation. This scoring
system is very simple and straightforward, requires no sophisticated investigation tools, and
is simple to use even in busy OPDs
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NewsBot · Mar 16, 2023

Wound Healing and Healing Process in Patients with Diabetic Foot Ulcers: A Survival Analysis Study
M. Graça Pereira et al
Diabetes Research and Clinical Practice; 11 March 2023, 110623

Aims
This study aimed to identify demographic, clinical, and psychological contributors to DFU healing and favorable healing process.

Methods
Patients with a chronic DFU were evaluated at baseline (T0; n = 153), two months later (T1; n = 108), and six months later (T2; n = 71). Patients were evaluated on health literacy, perceived stress, anxiety, depression, and illness perceptions. Cox proportional hazard models were built to analyze the predictors of DFU healing and favorable healing process (wound area reduction) including the assessment of time to achieve those outcomes.

Results
More than half of patients had their DFU healed (56.1%) or showed a favorable healing process (83.6%). Median time for healing was 112 days, while for favorable process was 30 days. Illness perceptions were the only predictor of wound healing. Female gender, adequate health literacy, and being the first DFU predicted a favorable healing process.

Conclusions
This is the first study showing that beliefs about DFU are significant predictors of DFU healing, and that health literacy is a significant predictor of a favorable healing process. Brief, comprehensive interventions should be implemented, at the treatment initial stage, in order to change misperceptions and to promote DFU literacy and better health outcomes.
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NewsBot · Sep 12, 2023

Establishment and Reliability Evaluation of Prognostic Models in Diabetic Foot
Yu-Long Chen, Li-Ping Zhu, Wen-Can Xu, Xiao-Ping Yang, Leiquan Ji, Qiaohui Chen, Chu-Jia Lin
Altern Ther Health Med. 2023 Sep 8

Purpose: To study the risk factors affecting amputation and survival in patients with diabetic foot (DF) and to construct a predictive model using the machine learning technique for DF foot amputation and survival and evaluate its effectiveness.

Materials and methods: A total of 200 patients with DF hospitalized in the First Affiliated Hospital of Shantou University Medical College in China were selected via cluster analysis screening, Kaplan-Meier survival calculation, amputation rate and Cox proportional hazards model investigation of risk factors associated with amputation and death. In addition, we constructed various models, including Cox proportional hazards regression analysis, the deep learning method convolution neural network (CNN) model, backpropagation (BP) neural network model, and backpropagation neural network prediction model after optimizing the genetic algorithm. The accuracy of the 4 prediction models for survival and amputation was assessed, and we evaluated the reliability of these computational models based on the size of the area under the ROC curve (AUC), sensitivity and specificity.

Results: We found that the 1-year survival rate in patients with DF was 88.5%, and the 1-year amputation rate was 12.5%. Wagner's Classification of Diabetic Foot Ulcers grade, ankle-brachial index (ABI), low-density lipoprotein (LDL), and percutaneous oxygen partial pressure (TcPO2) were independent risk factors for amputation in patients with DF, while cerebrovascular disease, Sudoscan sweat gland function score, glycated hemoglobin (HbA1c) and peripheral artery disease (PAD) were independent risk factors for death in patients with DF. In addition, our results showed that in the case of amputation, the COX regression predictive model revealed an AUC of 0.788, sensitivity of 74.1% and specificity of 83.6%. The BP neural network predictive model identified an AUC of 0.874, sensitivity of 87.0% and specificity of 87.7%. An AUC of 0.909, sensitivity of 90.7% and specificity of 91.1% were found after optimizing the BP neural network prediction model via genetic algorithm. In the deep learning CNN model, the AUC, sensitivity and specificity were 0.939, 92.6%, and 95.2%, respectively. In the analysis of risk factors for death, the COX regression predictive model identified the AUC, sensitivity and specificity as 0.800, 74.1% and 85.9%, respectively. The BP neural network predictive model revealed an AUC, sensitivity and specificity of 0.937, 93.1% and 94.4%, respectively. Genetic algorithm-based optimization of the BP neural network predictive model identified an AUC, sensitivity and specificity of 0.932, 91.4% and 95.1%, respectively. The deep learning CNN model found the AUC, sensitivity and specificity to be 0.861, 82.8% and 89.4%, respectively.

Conclusion: To identify risk factors for death, the BP neural network predictive model and genetic algorithm-based optimizing BP neural network predictive model have higher sensitivity and specificity than the deep learning method CNN predictive model and COX regression analysis.
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Predictors of treatment failure for diabetic foot complications

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Predictors of treatment failure for diabetic foot complications

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

Predictors of the Biomechanical Effects of Foot Orthoses

Kinematic predictors of loading during running

Predictors of the Biomechanical Effects of Customized Foot Orthoses in Adults With Flat-Arched Feet.

Severity and duration of diabetic foot ulcer (DFU) before seeking care as predictors of healing time

Age-related reduction and independent predictors of toe flexor strength in middle-aged men

Predictors of Response to Physical Therapy Intervention for Plantar Heel Pain

Predictors of healing and functional outcome following transmetatarsal amputations

Share This Page

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