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Updated: Oct 10, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk factors for relapse and nomogram for relapse probability prediction in patients with minor ischemic stroke
Xian-Feng Yu1, Wen-Wen Yin1, Chao-Juan Huang1
1Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China.
Background:
The identification of risk factors for recurrence in patients with minor ischemic stroke (MIS) is a critical medical need.
Aim:
To develop a nomogram for individualized prediction of in-hospital recurrence in MIS patients.
Methods:
Based on retrospective collection, a single-center study was conducted at the First Affiliated Hospital of Anhui Medical University from January 2014 to December 2019. Univariate and multivariate logistic regression analyses were used to determine the risk factors associated with MIS recurrence. The least absolute shrinkage and selection operator regression was performed for preliminary identification of potential risk factors. Uric acid, systolic blood pressure, serum total bilirubin (STBL), and ferritin were integrated for nomogram construction. The predictive accuracy and calibration of the nomogram model were assessed by the area under the receiver operating characteristic curve (AUC-ROC) and Hosmer-Lemeshow test, respectively.
Results:
A total of 2216 MIS patients were screened. Among them, 155 were excluded for intravascular therapy, 146 for unknown National Institutes of Health Stroke Scale score, 195 for intracranial hemorrhage, and 247 for progressive stroke. Finally, 1244 patients were subjected to further analysis and divided into a training set (n = 796) and a validation set (n = 448). Multivariate logistic regression analysis revealed that uric acid [odds ratio (OR): 0.997, 95% confidence interval (CI): 0.993-0.999], ferritin (OR: 1.004, 95%CI: 1.002-1.006), and STBL (OR: 0.973, 95%CI: 0.956-0.990) were independently associated with in-hospital recurrence in MIS patients. Our model showed good discrimination; the AUC-ROC value was 0.725 (95%CI: 0.646-0.804) in the training set and 0.717 (95%CI: 0.580-0.785) in the validation set. Moreover, the calibration between nomogram prediction and the actual observation showed good consistency. Hosmer-Lemeshow test results confirmed that the nomogram was well-calibrated (P = 0.850).
Conclusion:
Our present findings suggest that the nomogram may provide individualized prediction of recurrence in MIS patients.
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