scholarly journals Assessment of Simple Bedside Wound Characteristics for a Prediction Model for Diabetic Foot Ulcer Outcomes

2020 ◽  
pp. 193229682094230
Author(s):  
Clara Bender ◽  
Simon Lebech Cichosz ◽  
Louise Pape-Haugaard ◽  
Merete Hartun Jensen ◽  
Susan Bermark ◽  
...  

Background: Evidence-based learning systems built on prediction models can support wound care community nurses (WCCNs) during diabetic foot ulcer care sessions. Several prediction models in the area of diabetic foot ulcer healing have been developed, most built on cardiovascular measurement data. Two other data types are patient information (i.e. sex and hemoglobin A1c) and wound characteristics (i.e. wound area and wound duration); these data relate to the status of the diabetic foot ulcer and are easily accessible for WCCNs. The aim of the study was to assess simple bedside wound characteristics for a prediction model for diabetic foot ulcer outcomes. Method: Twenty predictor variables were tested. A pattern prediction model was used to forecast whether a given diabetic foot ulcer would (i) increase in size (or not) or (ii) decrease in size. Sensitivity, specificity, and area under the curve (AUC) in a receiver-operating characteristics curve were calculated. Results: A total of 162 diabetic foot ulcers were included. In combination, the predictor variables necrosis, wound size, granulation, fibrin, dry skin, and age were most informative, in total an AUC of 0.77. Conclusions: Wound characteristics have potential to predict wound outcome. Future research should investigate implementation of the prediction model in an evidence-based learning system.

2021 ◽  
pp. 193229682199112
Author(s):  
Clara Bender ◽  
Simon Lebech Cichosz ◽  
Alberto Malovini ◽  
Riccardo Bellazzi ◽  
Louise Pape-Haugaard ◽  
...  

Background: Currently, evidence-based learning systems to increase knowledge and evidence level of wound care are unavailable to wound care nurses in Denmark, which means that they need to learn about diabetic foot ulcers from experience and peer-to-peer training, or by asking experienced colleagues. Interactive evidence-based learning systems built on case-based reasoning (CBR) have the potential to increase wound care nurses’ diabetic foot ulcer knowledge and evidence levels. Method: A prototype of a CBR-interactive, evidence-based algorithm-operated learning system calculates a dissimilarity score (DS) that gives a quantitative measure of similarity between a new case and cases stored in a case base in relation to six variables: necrosis, wound size, granulation, fibrin, dry skin, and age. Based on the DS, cases are selected by matching the six variables with the best predictive power and by weighing the impact of each variable according to its contribution to the prediction. The cases are ranked, and the six cases with the lowest DS are visualized in the system. Results: Conventional education, that is, evidence-based learning material such as books and lectures, may be less motivating and pedagogical than peer-to-peer training, which is, however, often less evidence-based. The CBR interactive learning systems presented in this study may bridge the two approaches. Showing wound care nurses how individual variables affect outcomes may help them achieve greater insights into pathophysiological processes. Conclusion: A prototype of a CBR-interactive, evidence-based learning system that is centered on diabetic foot ulcers and related treatments bridges the gap between traditional evidence-based learning and more motivating and interactive learning approaches.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bocheng Peng ◽  
Rui Min ◽  
Yiqin Liao ◽  
Aixi Yu

Objective. To determine the novel proposed nomogram model accuracy in the prediction of the lower-extremity amputations (LEA) risk in diabetic foot ulcer (DFU). Methods and Materials. In this retrospective study, data of 125 patients with diabetic foot ulcer who met the research criteria in Zhongnan Hospital of Wuhan University from January 2015 to December 2019 were collected by filling in the clinical investigation case report form. Firstly, univariate analysis was used to find the primary predictive factors of amputation in patients with diabetic foot ulcer. Secondly, single factor and multiple factor logistic regression analysis were employed to screen the independent influencing factors of amputation introducing the primary predictive factors selected from the univariate analysis. Thirdly, the independent influencing factors were applied to build a prediction model of amputation risk in patients with diabetic foot ulcer by using R4.3; then, the nomogram was established according to the selected variables visually. Finally, the performance of the prediction model was evaluated and verified by receiver working characteristic (ROC) curve, corrected calibration curve, and clinical decision curve. Results. 7 primary predictive factors were selected by univariate analysis from 21 variables, including the course of diabetes, peripheral angiopathy of diabetic (PAD), glycosylated hemoglobin A1c (HbA1c), white blood cells (WBC), albumin (ALB), blood uric acid (BUA), and fibrinogen (FIB); single factor logistic regression analysis showed that albumin was a protective factor for amputation in patients with diabetic foot ulcer, and the other six factors were risk factors. Multivariate logical regression analysis illustrated that only five factors (the course of diabetes, PAD, HbA1c, WBC, and FIB) were independent risk factors for amputation in patients with diabetic foot ulcer. According to the area under curve (AUC) of ROC was 0.876 and corrected calibration curve of the nomogram displayed good fitting ability, the model established by these 5 independent risk factors exhibited good ability to predict the risk of amputation. The decision analysis curve (DCA) indicated that the nomogram model was more practical and accurate when the risk threshold was between 6% and 91%. Conclusion. Our novel proposed nomogram showed that the course of diabetes, PAD, HbA1c, WBC, and FIB are the independent risk factors of amputation in patients with DFU. This prediction model was well developed and behaved a great accurate value for LEA so as to provide a useful tool for screening LEA risk and preventing DFU from developing into amputation.


2014 ◽  
Vol 15 (3) ◽  
pp. 267-281 ◽  
Author(s):  
Liza R. Braun ◽  
Whitney A. Fisk ◽  
Hadar Lev-Tov ◽  
Robert S. Kirsner ◽  
Roslyn R. Isseroff

Author(s):  
Dr. Devi Das Verma ◽  
Dr. Anil Kumar Saxena

Introduction:  Diabetes is one of the most prevalent metabolic chronic diseases due to the imbalance production of insulin. One of the studies reported that in 2010 worldwide 285 million adults had diabetes and this figure may be increase to 439 million by the year 2030. Globally Diabetic foot ulcers (DFUs) constitute major health problem in people that significantly contribute to morbidity and mortality in diabetes patients. Approximate 1.0% to 4.1% of the annual population-based incidences of a diabetic foot ulcer (DFU) were reported. Due to this the lifetime may be as high as 25%. In Asian countries diabetic foot ulcer are major problems which are different from European countries or developing countries.  From many studies reported diabetic foot problems in India are infectious and neuropathic in nature as compared to developed countries. According to World Health Organization (WHO) diabetic foot is defined as lower limb of a diabetic patient characterized by infection, potential risk of pathologic consequences ulceration or destruction of deep tissues associated with neurological abnormalities, various changes in peripheral neuropathy vasculopathy and superimposed infection that are mainly responsible foot ulceration. Ulcers are one kind of abscess which is difficult to treat because of poor wound healing that result from a combination of neuropathy, ischemia and hyperglycemia.  Aim: The main objective was to study the outcome of treatment modalities and it’s relating factors to complication in diabetic foot ulcer.  Material and method:  Total 60 diabetic foot ulcer patients with the age range from 20 to 70 years were included.  From all the patients’ detailed past and present history were recorded.  For all the patients, general, physical and local and systemic examinations were also done. Detail   laboratory examination like Fasting and Post Prandial Blood sugar levels, blood count, ECG, ESR, complete urine examination for the presence of ketone bodies and sugar, x-ray as well as culture and sensitivity of the discharge from ulcer were also done. Patients were treated with various treatment methods like conservative treatment, split skin grafting and amputation. Result: In this study male patients were more in proportion as compared to female. This study showed that maximum with the age group 14 -50 (43.3%) years old followed by 18.3% in 31-40 years old, 16.7% in 61-70 years old.  6.7% showed the least age group as 20 -30 years old.  Out of total 60 patients, 38.3% of the patients showed diabetic ulcer foot which was more whereas 15% showed diabetic gangrene foot which was least. 25% showed diabetic cellulites foot and 21.7% showed as diabetic abscess foot.  Conclusion: Globally as diabetes mellitus cases are increasing and it became rapidly the public health problem. This may be due to burden on economy, health system and on society to manage the diabetic foot problems. Diabetic foot management guidelines must be made into our practice protocols which may preventing limb loss, and decrease mortality and increase the quality of life of the patient. Hence for this it is only possible with the help of foot care education and health care workers.  Hence, foot infection is to put first and care for it like hands. Keywords: Diabetes, foot ulcers, infections, amputations.


2015 ◽  
Author(s):  
Fakhraddeen Muhammad ◽  
Lateefah Pedro ◽  
Hassan Suleiman ◽  
Enikuomehin Adenike ◽  
Rahila Mukhtar ◽  
...  

2019 ◽  
Author(s):  
Maksym Prystupiuk ◽  
Iuliia Onofriichuk ◽  
Lev Prystupiuk ◽  
Ludmila Naumova ◽  
Marianna Naumova

2017 ◽  
Vol 1 (1) ◽  
pp. 59-63
Author(s):  
Palamalai Dinakaran ◽  

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