Diabetic Foot Examination

2013 ◽  
Vol 61 (3) ◽  
pp. 136-136 ◽  
Author(s):  
Stephanie Thatcher Weinsier
Keyword(s):  
High Risk ◽  
Occupational Health ◽  
Diabetic Foot ◽  
Cost Effective ◽  
Clinical Settings ◽  
Foot Complications ◽  
Timely Fashion

The 60-second tool (2012)© uses a cost-effective, simple, standardized approach to identifying, in a timely fashion, which individuals are at high risk for diabetic foot complications. Using this tool, occupational health nurses can screen for diabetic foot complications in varied clinical settings.

Management of Foot Problems in Diabetes

1996 ◽  
Vol 16 (1_suppl) ◽  
pp. 279-282 ◽  
Author(s):  
Jonathan E. Shaw ◽  
Andrew J.M. Boulton ◽  
Ram Gokal
Keyword(s):  
High Risk ◽  
Diabetic Foot ◽  
High Risk Patient ◽  
Team Approach ◽  
Foot Care ◽  
Pressure Relief ◽  
Foot Complications ◽  
Foot Problems ◽  
Appropriate Education ◽  
Careful Management

Many diabetic foot complications are preventable. This requires a team approach, aiming to identify the high-risk patient and provide appropriate education and foot care. An established ulcer needs careful management with the emphasis on pressure relief and establishing a good blood supply.

scholarly journals Genetic signatures for lineage/sublineage classification of HPV16, 18, 52 and 58 variants

2020 ◽  
Author(s):  
Zhihua Ou ◽  
Zigui Chen ◽  
Yanping Zhao ◽  
Haorong Lu ◽  
Wei Liu ◽  
...  
Keyword(s):  
High Risk ◽  
Eastern China ◽  
Cost Effective ◽  
High Accuracy ◽  
Marker Genes ◽  
Clinical Settings ◽  
High Risk Hpv ◽  
Genetic Signatures

Increasing evidences indicate that high-risk HPV variants are heterogeneous in carcinogenicity and ethnic dispersion. In this work, we identified genetic signatures for convenient determination of lineage/sublineage of HPV16, 18, 52 and 58 variants. Using publicly available genomes, we found that E2 of HPV16, L2 of HPV18, L1 and LCR of HPV52, and L2, LCR and E1 of HPV58 contain the proper genetic signature for lineage/sublineage classification. Sets of hierarchical signature nucleotide positions (SNPs) were further confirmed for high accuracy (>98%) by classifying HPV genomes obtained from Chinese females, which included 117 HPV16 variants, 48 HPV18 variants 117 HPV52 variants and 89 HPV58 variants. The circulation of HPV variants posing higher cancer risk in Eastern China, such as HPV16 A4 and HPV58 A3, calls for continuous surveillance in this region. The marker genes and signature nucleotide positions may facilitate cost-effective diagnostic detections of HPV variants in clinical settings.

scholarly journals Unilateral remote temperature monitoring to predict future ulceration for the diabetic foot in remission

2019 ◽  
Vol 7 (1) ◽  
pp. e000696 ◽  
Author(s):  
Lawrence A Lavery ◽  
Brian J Petersen ◽  
David R Linders ◽  
Jonathan D Bloom ◽  
Gary M Rothenberg ◽  
...  
Keyword(s):  
High Risk ◽  
Diabetic Foot ◽  
Lead Time ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Temperature Monitoring ◽  
Threshold Temperature ◽  
High Risk Population ◽  
Foot Complications ◽  
Sensitivity Specificity

ObjectiveDaily remote foot temperature monitoring is an evidence-based preventive practice for patients at risk for diabetic foot complications. Unfortunately, the conventional approach requires comparison of temperatures between contralaterally matched anatomy, limiting practice in high-risk cohorts such as patients with a wound to one foot and those with proximal lower extremity amputation (LEA). We developed and assessed a novel approach for monitoring of a single foot for the prevention and early detection of diabetic foot complications. The purpose of this study was to assess the sensitivity, specificity, and lead time associated with unilateral diabetic foot temperature monitoring.Research design and methodsWe used comparisons among ipsilateral foot temperatures and between foot temperatures and ambient temperature as a marker of inflammation. We analyzed data collected from a 129-participant longitudinal study to evaluate the predictive accuracy of our approach. To evaluate classification accuracy, we constructed a receiver operator characteristic curve and reported sensitivity, specificity, and lead time for four different monitoring settings.ResultsUsing this approach, monitoring a single foot was found to predict 91% of impending non-acute plantar foot ulcers on average 41 days before clinical presentation with a resultant mean 4.2 alerts per participant-year. By adjusting the threshold temperature setting, the specificity could be increased to 78% with corresponding reduced sensitivity of 53%, lead time of 33 days, and 2.2 alerts per participant-year.ConclusionsGiven the high incidence of subsequent diabetic foot complications to the sound foot in patients with a history of proximal LEA and patients being treated for a wound, practice of daily temperature monitoring of a single foot has the potential to significantly improve outcomes and reduce resource utilization in this challenging high-risk population.

scholarly journals A 16-year prospective cohort study to evaluate effects of long-term fluctuations in obesity indices of prediabetics on the incidence of future diabetes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shahla Safari ◽  
Maryam Abdoli ◽  
Masoud Amini ◽  
Ashraf Aminorroaya ◽  
Awat Feizi
Keyword(s):  
High Risk ◽  
Transition Probabilities ◽  
Clinical Settings ◽  
High Tendency ◽  
Mean Values ◽  
Latent Markov Model ◽  
Term Evaluation ◽  
Obesity Indices

AbstractThis study aimed to evaluate the patterns of changes in obesity indices over time in prediabetic subjects and to classify these subjects as either having a low, moderate, and high risk for developing diabetes in the future. This study was conducted among 1228 prediabetics. The patterns of changes in obesity indices based on three measurements including first, mean values during the follow-up period, and last visit from these indices were evaluated by using the latent Markov model (LMM). The mean (standard deviation) age of subjects was 44.0 (6.8) years and 73.6% of them were female. LMM identified three latent states of subjects in terms of change in all anthropometric indices: a low, moderate, and high tendency to progress diabetes with the state sizes (29%, 45%, and 26%), respectively. LMM showed that the probability of transitioning from a low to a moderate tendency to progress diabetes was higher than the other transition probabilities. Based on a long-term evaluation of patterns of changes in obesity indices, our results reemphasized the values of all five obesity indices in clinical settings for identifying high-risk prediabetic subjects for developing diabetes in future and the need for more effective obesity prevention strategies.

Nationwide prevalence and clinical characteristics of inpatient diabetic foot complications: A Peruvian multicenter study

2021 ◽  
Author(s):  
Marlon Yovera-Aldana ◽  
Sofia Sáenz-Bustamante ◽  
Yudith Quispe-Landeo ◽  
Rosa Agüero-Zamora ◽  
Julia Salcedo ◽  
...  
Keyword(s):  
Multicenter Study ◽  
Diabetic Foot ◽  
Clinical Characteristics ◽  
Foot Complications

Baby steps in the path of modifying the role of cardiologists for interpreting EKG for AMI

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Mehta ◽  
S Niklitschek ◽  
F Fernandez ◽  
C Villagran ◽  
J Avila ◽  
...  
Keyword(s):  
Human Resources ◽  
Ground Truth ◽  
Cost Effective ◽  
Clinical Settings ◽  
Dense Layer ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Original Dataset ◽  
Low Pass ◽  
Normal Branch

Abstract Background EKG interpretation is slowly transitioning to a physician-free, Artificial Intelligence (AI)-driven endeavor. Our continued efforts to innovate follow a carefully laid stepwise approach, as follows: 1) Create an AI algorithm that accurately identifies STEMI against non-STEMI using a 12-lead EKG; 2) Challenging said algorithm by including different EKG diagnosis to the previous experiment, and now 3) To further validate the accuracy and reliability of our algorithm while also improving performance in a prehospital and hospital settings. Purpose To provide an accurate, reliable, and cost-effective tool for STEMI detection with the potential to redirect human resources into other clinically relevant tasks and save the need for human resources. Methods Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: A total of 11,567 12-lead EKG records of 10-seconds length with sampling frequency of 500 [Hz], including the following balanced classes: unconfirmed and angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal and abnormal (200+ CPT codes, excluding the ones included in other classes). The label of each record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: The first and last 250 samples were discarded as they may contain a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI (STEMI in different locations of the myocardium – anterior, inferior and lateral); Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10; respectively. The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. Additional testing was performed with a subset of the original dataset of angiographically confirmed STEMI. Results See Figure Attached – Preliminary STEMI Dataset Accuracy: 96.4%; Sensitivity: 95.3%; Specificity: 97.4% – Confirmed STEMI Dataset: Accuracy: 97.6%; Sensitivity: 98.1%; Specificity: 97.2%. Conclusions Our results remain consistent with our previous experience. By further increasing the amount and complexity of the data, the performance of the model improves. Future implementations of this technology in clinical settings look promising, not only in performing swift screening and diagnostic steps but also partaking in complex STEMI management triage. Funding Acknowledgement Type of funding source: None

Doing More With Less: Pragmatic Implementation of Vancomycin Area-Under-the-Curve (AUC) Monitoring

2021 ◽  
pp. 089719002110272
Author(s):  
Joanne Huang ◽  
Jeannie D. Chan ◽  
Thu Nguyen ◽  
Rupali Jain ◽  
Zahra Kassamali Escobar
Keyword(s):  
Quality Improvement ◽  
High Risk ◽  
Therapeutic Drug Monitoring ◽  
Paradigm Shift ◽  
Area Under The Curve ◽  
Cost Effective ◽  
Practical Method ◽  
Therapeutic Drug ◽  
Short Term ◽  
Quality Improvement Process

Universal area-under-the-curve (AUC) guided vancomycin therapeutic drug monitoring (TDM) is resource-intensive, cost-prohibitive, and presents a paradigm shift that leaves institutions with the quandary of defining the preferred and most practical method for TDM. We report a step-by-step quality improvement process using 4 plan-do-study-act (PDSA) cycles to provide a framework for development of a hybrid model of trough and AUC-based vancomycin monitoring. We found trough-based monitoring a pragmatic strategy as a first-tier approach when anticipated use is short-term. AUC-guided monitoring was most impactful and cost-effective when reserved for patients with high-risk for nephrotoxicity. We encourage others to consider quality improvement tools to locally adopt AUC-based monitoring.

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