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Updated: Jun 10, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk stratification for single-agent chemoresistance in low-risk gestational trophoblastic neoplasia: a multicenter
Wenna Zhao1, Qin Yao2, Haifeng Qiu3
1Qilu Hospital of Shandong University, Department of Obstetrics and Gynecology, Jinan, China; Qilu Hospital of Shandong University, Shandong Key Laboratory of Reproductive Health and Birth Defects Prevention and Control, Jinan, Shandong, China; The Second Affiliated Hospital of Shandong First Medical University, Department of Obstetrics and Gynecology, Tai'an, Shandong, China.
Objective:
Single-agent chemotherapy is the standard treatment for patients with low-risk gestational trophoblastic neoplasia. However, approximately 25% to 30% of these patients exhibit chemoresistance and require subsequent multi-agent chemotherapy. This study sought to develop a risk stratification to identify patients at high risk of chemoresistance to single-agent chemotherapy.
Methods:
The study cohort comprised 350 patients with low-risk gestational trophoblastic neoplasia from 3 tertiary centers from 2013 to 2022. All patients underwent standard chemotherapy and were randomly assigned in a ratio of 7:3. Logistic regression was performed to identify risk factors for chemoresistance. The final model was presented as a nomogram, and its performance was evaluated in terms of discrimination, calibration, and clinical utility.
Results:
Pre-treatment serum human chorionic gonadotropin above 10,000 mIU/mL (odds ratio 11.70, 95% confidence interval 4.37 to 35.09), antecedent pregnancy of a non-hydatidiform mole (odds ratio 4.94, 95% confidence interval 2.07 to 12.16), and lung metastasis (odds ratio 3.07, 95% confidence interval 1.52 to 6.50) were independently associated with chemoresistance. A prediction model incorporating these factors demonstrated strong discriminative power, with an area under the receiver operating characteristic curve of 0.793 in the training cohort and 0.811 in the validation cohort. Furthermore, calibration curves and decision curve analysis indicated good calibration and clinical utility. Ultimately, we established a risk stratification approach that can identify 3 populations as the high-risk sub-groups for single-agent chemoresistance.
Conclusion:
Based on the multi-center data, we developed a predictive model and a risk stratification approach to estimate the probability of single-agent chemoresistance in patients with low-risk gestational trophoblastic neoplasia. This model may provide additional insight for individualized risk assessment, although its direct clinical utility requires further validation.
