1. Development and validation of a risk prediction model in patients with adult congenital heart disease.
- Author
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Baggen, Vivan J.M., Venema, Esmee, Živná, Renata, van den Bosch, Annemien E., Eindhoven, Jannet A., Witsenburg, Maarten, Cuypers, Judith A.A.E., Boersma, Eric, Lingsma, Hester, Popelová, Jana R., and Roos-Hesselink, Jolien W.
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CONGENITAL disorders , *CONGENITAL heart disease , *HEART diseases , *ARRHYTHMIA , *HEART failure , *PREDICTION models - Abstract
Abstract Aims To develop and validate a clinically useful risk prediction tool for patients with adult congenital heart disease (ACHD). Methods and results A risk model was developed in a prospective cohort of 602 patients with moderate/complex ACHD who routinely visited the outpatient clinic of a tertiary care centre in the Netherlands (2011−2013). This model was externally validated in a retrospective cohort of 402 ACHD patients (Czech Republic, 2004–2013). The primary endpoint was the 4-year risk of death, heart failure, or arrhythmia, which occurred in 135 of 602 patients (22%). Model development was performed using multivariable logistic regression. Model performance was assessed with C-statistics and calibration plots. Of the 14 variables that were selected by an expert panel, the final prediction model included age (OR 1.02, 95%CI 1.00–1.03, p = 0.031), congenital diagnosis (OR 1.52, 95%CI 1.03–2.23, p = 0.034), NYHA class (OR 1.74, 95%CI 1.07–2.84, p = 0.026), cardiac medication (OR 2.27, 95%CI 1.56–3.31, p < 0.001), re-intervention (OR 1.41, 95%CI 0.99–2.01, p = 0.060), BMI (OR 1.03, 95%CI 0.99–1.07, p = 0.123), and NT-proBNP (OR 1.63, 95%CI 1.45–1.84, p < 0.001). Calibration-in-the-large was suboptimal, reflected by a lower observed event rate in the validation cohort (17%) than predicted (36%), likely explained by heterogeneity and different treatment strategies. The externally validated C-statistic was 0.78 (95%CI 0.72–0.83), indicating good discriminative ability. Conclusion The proposed ACHD risk score combines six readily available clinical characteristics and NT-proBNP. This tool is easy to use and can aid in distinguishing high- and low-risk patients, which could further streamline counselling, location of care, and treatment in ACHD. Highlights • The ACHD risk calculator is based on readily available clinical characteristics and NT-proBNP. • The risk model provides a good discrimination between high- and low-risk patients with ACHD. • A web application is presented, which could further streamline counselling, location of care, and treatment in ACHD. • Other investigators are invited to collaborate in order to improve the absolute risk estimations of the current model. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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