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Updated: Aug 5, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development of prognostic nomogram model to predict syncope recurrence in children with vasovagal syncope
Rui Sun1,2, Yingying Kang2,3, Mingming Zhang2
1Department of Cardiology, Capital Institute of Pediatrics-Peking University Teaching Hospital, Beijing, China.
Backgrounds:
Vasovagal syncope (VVS) is a common form of syncope. In children with VVS, recurrent syncope or presyncope can affect the physical and mental health of both children and parents, which markedly impairs quality of life.
Objectives:
We aimed to identify factors at baseline that can predict the recurrence of syncope or presyncope over a 5-year follow-up period, and further to develop a prognostic nomogram model.
Methods:
This cohort is bidirectional in design. From July 2017 to August 2022, children with VVS were included and followed up every 3 to 6 months. Head-up Tilt Test (HUTT) was performed for diagnosing VVS. Data were analyzed using STATA software, and risk estimates are presented as hazard ratio (HR) and 95% confidence interval (CI).
Results:
Total 352 children with VVS who had complete information were included in this study. Median follow-up time was 22 months. Overall, supine mean arterial pressure (MAP-supine) in HUTT and baseline urine specific gravity (USG) were associated with the significant risk of syncope or presyncope recurrence (HR: 0.70 and 3.00, respectively; both P < 0.05). Calibration and discrimination analyses revealed that the addition of MAP-supine and USG can result in a better fit. A prognostic nomogram model based on significant factors annexed with five traditional promising factors was finally constructed, with strong discriminative and predictive abilities (C-index approaching 0.700, P < 0.05).
Conclusions:
Our findings indicated that MAP-supine and USG can independently predict the significant risk of syncope recurrence in children with VVS, and the prediction was more obvious in a nomogram model.
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