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
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...
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Series: | Journal of Diabetes Research |
Online Access: | http://dx.doi.org/10.1155/2021/5521493 |
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doaj-1f107d9d80e94f67964a46de0954d1282021-03-29T00:09:23ZengHindawi LimitedJournal of Diabetes Research2314-67532021-01-01202110.1155/2021/5521493Designing a Logistic Regression Model for a Dataset to Predict Diabetic Foot Ulcer in Diabetic Patients: High-Density Lipoprotein (HDL) Cholesterol Was the Negative PredictorSeyyed Amir Yasin Ahmadi0Razieh Shirzadegan1Nazanin Mousavi2Ermia Farokhi3Maryam Soleimaninejad4Mehrzad Jafarzadeh5Student Research CommitteeSocial Determinants of Health Research CenterStudent Research CommitteeFaculty of MedicineFaculty of MedicineEndocrine Research CenterObjectives. 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.http://dx.doi.org/10.1155/2021/5521493 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seyyed Amir Yasin Ahmadi Razieh Shirzadegan Nazanin Mousavi Ermia Farokhi Maryam Soleimaninejad Mehrzad Jafarzadeh |
spellingShingle |
Seyyed Amir Yasin Ahmadi Razieh Shirzadegan Nazanin Mousavi Ermia Farokhi Maryam Soleimaninejad Mehrzad Jafarzadeh 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 Journal of Diabetes Research |
author_facet |
Seyyed Amir Yasin Ahmadi Razieh Shirzadegan Nazanin Mousavi Ermia Farokhi Maryam Soleimaninejad Mehrzad Jafarzadeh |
author_sort |
Seyyed Amir Yasin Ahmadi |
title |
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 |
title_short |
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 |
title_full |
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 |
title_fullStr |
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 |
title_full_unstemmed |
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 |
title_sort |
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 |
publisher |
Hindawi Limited |
series |
Journal of Diabetes Research |
issn |
2314-6753 |
publishDate |
2021-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2021/5521493 |
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