Designing a Logistic Regression Model for a Dataset to Predict Diabetic Foot Ulcer in Diabetic Patients: High-Density Lipoprotein (HDL) Cholesterol Was the Negative Predictor. (18th March 2021)

• Record Type: Journal Article
• Authors: Ahmadi, Seyyed Amir Yasin; Shirzadegan, Razieh; Mousavi, Nazanin; Farokhi, Ermia; Soleimaninejad, Maryam; Jafarzadeh, Mehrzad
• Other Names: Papanas Nikolaos Academic Editor.
• Is Part Of: Journal of diabetes research. Volume 2021(2021)
• Publication Date: 2021-03-18
• Subjects: Diabetes -- Periodicals; Diabetes -- Pathophysiology -- Periodicals; Diabetes -- Prevention -- Periodicals; Diabetes -- Etiology -- Periodicals; Diabetes -- Epidemiology -- Periodicals; Diabetes -- Pathogenesis -- Periodicals; 616.462005
• DOI: 10.1155/2021/5521493
• Languages: English
• ISSNs: 2314-6745

Abstract

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 (β 1 = 0.133 ), BMI (β 2 = 0.194 ), FBS (β 3 = 0.011 ), HDL (β 4 = − 0.118 ), and insulin dependency (β 5 = 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.