Background: Many diabetic patients develop and progress to diabetic foot ulcers, which seriously affect health and quality of life and cause great economic and psychological stress, especially in elderly diabetic patients who often have various underlying diseases, and the consequences of their progression to diabetic foot ulcers are more serious and seriously affect elderly patients in surgery. Therefore, it is particularly important to analyze the influencing factors related to the progression of elderly diabetic patients to diabetic foot, and the column line graph prediction model is drawn based on regression analysis to derive the influencing factors of the progression of elderly diabetic patients to diabetic foot, and the total score derived from the combination of various influencing factors can visually calculate the probability of the progression of elderly diabetic patients to diabetic foot.
Objective: The influencing factors of progression deterioration to diabetic foot in elderly diabetic patients based on LASSO regression analysis and logistics regression analysis, and the column line graph prediction model was established by statistically significant risk factors.
Methods: The clinical data of elderly diabetic patients aged 60 years or older in the orthopedic ward and endocrine ward of the Third Hospital of Shanxi Medical University from 2015-01-01 to 2021-12-31 were retrospectively analyzed and divided into a modeling population (211) and an internal validation population (88) according to the random assignment principle. Firstly, LASSO regression analysis was performed based on the modeling population to screen out the independent influencing factors for progression to diabetic foot in elderly diabetic patients; Logistics univariate and multifactor regressions were performed by the screened influencing factors, and then column line graph prediction models for progression to diabetic foot in elderly diabetic patients were made by these influencing factors, using ROC (subject working characteristic curve) and AUC (their area under the curve), C-index validation, and calibration curve to initially evaluate the model discrimination and calibration. Model validation was performed by the internal validation set, and the ROC curve, C-index and calibration curve were used to further evaluate the column line graph model performance. Finally, using DCA (decision curve analysis), we observed whether the model could be used better in clinical settings.
Results And Conclusions: (1) LASSO (Least absolute shrinkage and selection operator) regression analysis yielded a more significant significance on risk factors for progression to diabetic foot in elderly diabetic patients, such as age, presence of peripheral neuropathy, history of smoking, duration of disease, serum lactate dehydrogenase, and high-density cholesterol; (2) Based on the influencing factors and existing theories, a column line graph prediction model for progression to diabetic foot in elderly diabetic patients was constructed. The working characteristic curves of subjects in the training group and their area under the curve (area under the curve = 0.840) were also analyzed simultaneously with the working characteristic curves of subjects in the external validation population and their area under the curve (area under the curve = 0.934), which finally showed that the model was effective in predicting column line graphs; (iii) the C-index in the modeled cohort was 0.840 (95%CI: 0.779-0.901) and the C-index in the validation cohort was 0.934 (95%CI: 0.887-0.981), indicating that the model had good predictive accuracy; the calibration curve fit was good; (iv) the results of the decision curve analysis showed that the model would have good results in clinical use; (v) it indicated that the established predictive model for predicting progression to diabetic foot in elderly diabetic patients had good test efficacy and helped clinically screen the possibility of progression to diabetic foot in elderly diabetic patients and give personalized interventions to different patients in time.
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http://dx.doi.org/10.3389/fendo.2023.1107830 | DOI Listing |
JAMA Netw Open
January 2025
Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
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Fracture-related infections are a significant burden to the patient, associated with high health care costs and use of resources. Therefore, prevention is more critical than treatment of infection. There are injury- and patient-related risk factors that are mostly not modifiable, with the exception of a few patient-specific ones such as control of blood glucose levels in patients with diabetes.
View Article and Find Full Text PDFJ Clin Microbiol
December 2024
Chrono-environnement UMR6249, CNRS, University of Franche-Comté, Besançon, Bourgogne-Franche-Comté, France.
Unlabelled: The aim of this study was to identify parameters influencing DNA extraction and PCR amplification efficiencies in an attempt to standardize Mucorales qPCR. The Fungal PCR Initiative Mucorales Laboratory Working Group distributed two panels of simulated samples to 26 laboratories: Panel A (six sera spiked with Mucorales DNA and one negative control serum) and Panel B (six Mucorales DNA extracts). Panel A underwent DNA extraction in each laboratory according to the local procedure and were sent to a central laboratory for testing using three different qPCR techniques: one in-house qPCR assay and two commercial assays (MucorGenius and Fungiplex).
View Article and Find Full Text PDFIntroduction: Endoscopic ablation is the mainstay treatment for dysplastic Barrett's esophagus (BE), of which radiofrequency ablation (RFA) and argon plasma coagulation (APC) are the most widely available options.
Objectives: We aimed to analyze the safety and outcomes of endoscopic ablation for BE within Polish centers.
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Diabetes Technol Ther
January 2025
Barbara Davis Center for Diabetes, University of Colorado Denver, Aurora, Colorado, USA.
Adults with type 1 diabetes (T1D) are increasingly overweight or obese, in part due to intensive insulin therapy. Newer non-insulin medications targeting both hyperglycemia and weight loss are approved for people with type 2 diabetes. These drugs also reduce cardiovascular disease, the major cause of mortality in people with diabetes.
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