Abstract
OBJECTIVES: Risk prediction scores are important for early diagnosis and treatment of diseases. Diabetic peripheral neuropathy (DPN) is a common complication of type 2 diabetes, but the early diagnosis is challenging. This study developed a risk prediction model for DPN based on modifiable risk factors. MATERIALS AND METHODS: The study included 315 type 2 diabetes patients with and without DPN. Demographic, biochemical, and diagnostic data were collected. Multinomial logistic regression analysis was used to identify independent risk factors for DPN. RESULTS: Hemoglobin% and total red blood cells were identified as independent risk factors for DPN, used to develop a risk prediction score. CONCLUSION: The risk prediction score developed in this study can be used by physicians to quickly assess a patient's risk of DPN and select appropriate therapeutic options. Routine monitoring of modifiable risk factors can improve DPN prognosis. Patients stratified by risk scores can better understand their risk and seek appropriate care.