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

• Main Authors: Seyyed Amir Yasin Ahmadi, Razieh Shirzadegan, Nazanin Mousavi, Ermia Farokhi, Maryam Soleimaninejad, Mehrzad Jafarzadeh
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Journal of Diabetes Research
• Online Access: http://dx.doi.org/10.1155/2021/5521493

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.