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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Development and Validation of a Risk Nomogram Model for Predicting Constipation in Patients with Type 2 Diabetes
Hai-Liang Yuan1, Xian Zhang2, Dong-Zhu Peng1
1Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, Zhejiang, People's Republic of China.
Summary
This study developed a risk nomogram to predict constipation in patients with type 2 diabetes mellitus (T2DM). The validated tool aids in early, personalized management of constipation for diabetic patients.
Area of Science:
- Endocrinology
- Gastroenterology
- Medical Informatics
Background:
- Constipation is a frequent complication in type 2 diabetes mellitus (T2DM) patients.
- It significantly impacts patient health and quality of life.
- Effective risk stratification is crucial for timely intervention.
Purpose of the Study:
- To establish and internally validate a predictive risk nomogram for constipation in T2DM patients.
- To assess the nomogram's predictive accuracy and clinical utility.
- To facilitate personalized risk management strategies.
Main Methods:
- Retrospective study of 746 T2DM patients across two medical centers.
- Development of a nomogram using logistic regression analysis on selected predictive factors.
- Internal and external validation using AUROC, calibration curves, and DCA.
Main Results:
- A five-variable nomogram (age, HbA1c, calcium, anxiety, regular exercise) was developed.
- The nomogram demonstrated good discrimination: AUROC of 0.908 (training), 0.867 (internal validation), 0.816 (external validation).
- Calibration and decision curve analyses confirmed good predictive agreement and clinical applicability.
Conclusions:
- A validated nomogram for predicting constipation risk in T2DM patients has been developed.
- This tool supports pretreatment risk assessment and personalized clinical decision-making.
- It offers potential for improved management of constipation in this population.
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