Medicina oral, patologia oral y cirugia bucal · 2017
Use of the universal pain assessment tool for evaluating pain associated with TMD in youngsters with an intellectual disability.
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Dugashvili G, Van den Berghe L, Menabde G, Janelidze M, Marks L
Аннотация
BACKGROUND: Diabetic foot (DF) represents a serious complication associated with type 2 diabetes mellitus (T2DM), characterized by elevated rates of morbidity and mortality, alongside substantial healthcare expenditures. Early detection and intervention are essential for enhancing patient prognosis. The objective of this research is to establish a nomogram model for predicting diabetic foot (DF) risk in type 2 diabetes (T2DM).
METHODS: We conducted a retrospective analysis of 450 individuals with T2DM. Patients were divided into training and validation sets in a 7:3 ratio randomly. We utilized the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression to determine independent risk factors. The performance of the nomogram was evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
RESULTS: Six independent factors were incorporated into the nomogram: diabetic peripheral neuropathy (DPN), coronary heart disease (CHD), serum albumin (ALB) levels, high-density lipoprotein cholesterol levels (HDLC), fasting blood glucose levels (FBG), and the neutrophil percentage-to-hemoglobin ratio (NPHR). This model demonstrated impressive discrimination, achieving an area under the curve (AUC) of 0.945 (95% confidence interval (CI) [0.9226-0.968]) in the training cohort and 0.941 (95% CI [0.9021-0.9789]) in the validation cohort, while the calibration curves illustrated a positive alignment between predictions and the observed data. Additionally, the DCA revealed clinical benefits.
CONCLUSION: This nomogram enables individualized risk stratification, guiding clinicians to prioritize high-risk patients for preventive care, thereby reducing diabetes-related complications and optimizing resource utilization.
METHODS: We conducted a retrospective analysis of 450 individuals with T2DM. Patients were divided into training and validation sets in a 7:3 ratio randomly. We utilized the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression to determine independent risk factors. The performance of the nomogram was evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
RESULTS: Six independent factors were incorporated into the nomogram: diabetic peripheral neuropathy (DPN), coronary heart disease (CHD), serum albumin (ALB) levels, high-density lipoprotein cholesterol levels (HDLC), fasting blood glucose levels (FBG), and the neutrophil percentage-to-hemoglobin ratio (NPHR). This model demonstrated impressive discrimination, achieving an area under the curve (AUC) of 0.945 (95% confidence interval (CI) [0.9226-0.968]) in the training cohort and 0.941 (95% CI [0.9021-0.9789]) in the validation cohort, while the calibration curves illustrated a positive alignment between predictions and the observed data. Additionally, the DCA revealed clinical benefits.
CONCLUSION: This nomogram enables individualized risk stratification, guiding clinicians to prioritize high-risk patients for preventive care, thereby reducing diabetes-related complications and optimizing resource utilization.