Seyyed Amir Yasin Ahmadi1, Razieh Shirzadegan2, Nazanin Mousavi3, Ermia Farokhi4, Maryam Soleimaninejad4, Mehrzad Jafarzadeh5. 1. Student Research Committee, Iran University of Medical Sciences, Tehran, Iran. 2. Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran. 3. Student Research Committee, Lorestan University of Medical Sciences, Khorramabad, Iran. 4. Faculty of Medicine, Babol University of Medical Sciences, Babol, Iran. 5. Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran.
Abstract
OBJECTIVES: Although the risk factors for diabetic neuropathy and diabetic foot ulcer have been detected, there was no practical modeling for their prediction. We aimed to design a logistic regression model on an Iranian dataset to predict the probability of experiencing diabetic foot ulcers up to a considered age in diabetic patients. METHODS: The present study was a statistical modeling on a previously published dataset. The covariates were sex, age, body mass index (BMI), fasting blood sugar (FBS), hemoglobin A1C (HbA1C), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), insulin dependency, and statin use. The final model of logistic regression was designed through a manual stepwise method. To study the performance of the model, an area under receiver operating characteristic (AUC) curve was reported. A scoring system was defined according to the beta coefficients to be used in logistic function for calculation of the probability. RESULTS: The pretest probability for the outcome was 30.83%. The final model consisted of age (β1 = 0.133), BMI (β2 = 0.194), FBS (β3 = 0.011), HDL (β4 = -0.118), and insulin dependency (β5 = 0.986) (P < 0.1). The performance of the model was definitely acceptable (AUC = 0.914). CONCLUSION: This model can be used clinically for consulting the patients. The only negative predictor of the risk is HDL cholesterol. Keeping the HDL level more than 50 (mg/dl) is strongly suggested. Logistic regression modeling is a simple and practical method to be used in the clinic.
OBJECTIVES: Although the risk factors for diabetic neuropathy and diabetic foot ulcer have been detected, there was no practical modeling for their prediction. We aimed to design a logistic regression model on an Iranian dataset to predict the probability of experiencing diabetic foot ulcers up to a considered age in diabetic patients. METHODS: The present study was a statistical modeling on a previously published dataset. The covariates were sex, age, body mass index (BMI), fasting blood sugar (FBS), hemoglobin A1C (HbA1C), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), insulin dependency, and statin use. The final model of logistic regression was designed through a manual stepwise method. To study the performance of the model, an area under receiver operating characteristic (AUC) curve was reported. A scoring system was defined according to the beta coefficients to be used in logistic function for calculation of the probability. RESULTS: The pretest probability for the outcome was 30.83%. The final model consisted of age (β1 = 0.133), BMI (β2 = 0.194), FBS (β3 = 0.011), HDL (β4 = -0.118), and insulin dependency (β5 = 0.986) (P < 0.1). The performance of the model was definitely acceptable (AUC = 0.914). CONCLUSION: This model can be used clinically for consulting the patients. The only negative predictor of the risk is HDL cholesterol. Keeping the HDL level more than 50 (mg/dl) is strongly suggested. Logistic regression modeling is a simple and practical method to be used in the clinic.
Authors: Edward J Boyko; Jessie H Ahroni; Victoria Cohen; Karin M Nelson; Patrick J Heagerty Journal: Diabetes Care Date: 2006-06 Impact factor: 19.112
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Authors: Juan R Ulloque-Badaracco; Melany D Mosquera-Rojas; Enrique A Hernandez-Bustamante; Esteban A Alarcón-Braga; Ricardo R Ulloque-Badaracco; Ali Al-Kassab-Córdova; Percy Herrera-Añazco; Vicente A Benites-Zapata; Adrian V Hernandez Journal: Int J Clin Pract Date: 2022-08-10 Impact factor: 3.149