Is high level of HbA1C an indicator for extended period of antibiotherapy in diabetic foot ulcers?

Doğan, Mustafa; Onar, Lutfi Cagatay; Aydin, Bilgehan; Gümüştaş, Seyit Ali

doi:10.14744/nci.2018.25582

Cited by 2 publications

(1 citation statement)

References 14 publications

(24 reference statements)

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“…As we all know, long-term exposure to hyperglycemia will damage vascular endothelial cells and basement membranes, hinder the blood supply of tissue microcirculation, and thus accelerate the occurrence and development of atherosclerosis and damage to the peripheral nerves of the foot under a hypercoagulable state, resulting in local ischemia of the foot ( 22 ). In this study, HbA1c was used to reflect the control level of blood glucose in patients within 2–3 months ( 23 ), which proved that hyperglycemia plays an important role in the risk of DF in patients with T2DM. Therefore, maintaining a stable blood glucose level in patients with T2DM is of great significance to prevent the occurrence of DF ( 24 ).…”

Section: Discussionmentioning

confidence: 97%

Development of predictive nomograms clinical use to quantify the risk of diabetic foot in patients with type 2 diabetes mellitus

Peng

Min

2023

Front. Endocrinol.

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ObjectiveThe aim of the study was to explore the risk factors for diabetic foot disease in patients with type 2 diabetes mellitus and to establish and verify the nomogram model of DF risk in patients with T2DM.MethodsThe clinical data of 705 patients with type 2 diabetes who were hospitalized in our hospital from January 2015 to December 2022 were analyzed retrospectively. According to random sampling, the patients were divided into two groups: the training set (DF = 84; simple T2DM = 410) and the verification set (DF = 41; simple T2DM = 170). Univariate and multivariate logistic regression analysis was used to screen the independent risk factors for DF in patients with T2DM in the training set. According to the independent risk factors, the nomogram risk prediction model is established and verified.ResultsLogistic regression analysis showed age (OR = 1.093, 95% CI 1.062–1.124, P <0.001), smoking history (OR = 3.309, 95% CI 1.849–5.924, P <0.001), glycosylated hemoglobin (OR = 1.328, 95% CI 1.173–1.502, P <0.001), leukocyte (OR = 1.203, 95% CI 1.076–1.345, and LDL-C (OR = 2.002, 95% CI 1.463–2.740), P <0.001) was independent risk factors for T2DM complicated with DF. The area of the nomogram model based on the above indexes under the ROC curve of the training set and the verification set is 0.827 and 0.808, respectively; the correction curve shows that the model has good accuracy; and the DCA results show that when the risk threshold is between 0.10–0.85 (training set) and 0.10–0.75 (verification set), the clinical practical value of the model is higher.ConclusionThe nomogram model constructed in this study is of high value in predicting the risk of DF in patients with T2DM and is of reference value for clinicians to identify people at high risk of DF and provide them with early diagnosis and individual prevention.

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Section: Discussionmentioning

confidence: 97%

Development of predictive nomograms clinical use to quantify the risk of diabetic foot in patients with type 2 diabetes mellitus

Peng

Min

2023

Front. Endocrinol.

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Performance of risk prediction models for diabetic foot ulcer: a meta-analysis

Guo,

Tu,

Liu

et al. 2024

PeerJ

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Background The number of prediction models for diabetic foot ulcer (DFU) risk is increasing, but their methodological quality and clinical applicability are uncertain. We conducted a systematic review to assess their performance. Methods We searched PubMed, Cochrane Library, and Embase databases up to 10 February 2024 and extracted relevant information from selected prediction models. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) checklist was used to assess bias risk and applicability. All statistical analyses were conducted in Stata 14.0. Results Initially, 13,562 studies were retrieved, leading to the inclusion of five development and five validation models from eight studies. DFU incidence ranged from 6% to 16.8%, with age and hemoglobin A1C (HbA1c) commonly used as predictive factors. All included studies had a high risk of bias, mainly due to disparities in population characteristics and methodology. In the meta-analysis, we observed area under the curve (AUC) values of 0.78 (95% CI [0.69–0.89]) for development models and 0.84 (95% CI [0.79–0.90]) for validation models. Conclusion DFU risk prediction models show good overall accuracy, but there is a risk of bias. Adherence to the PROBAST checklist is crucial for improving their clinical applicability.

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Is high level of HbA1C an indicator for extended period of antibiotherapy in diabetic foot ulcers?

Cited by 2 publications

References 14 publications

Development of predictive nomograms clinical use to quantify the risk of diabetic foot in patients with type 2 diabetes mellitus

Development of predictive nomograms clinical use to quantify the risk of diabetic foot in patients with type 2 diabetes mellitus

Performance of risk prediction models for diabetic foot ulcer: a meta-analysis

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