Megan Griffiths, Bhargava K. Chinni, Chantel Lokhorst, Johannes M. Douwes, Lynn A. Sleeper, Jennifer Tingo, Steven H. Abman, Erika B. Rosenzweig, Jennifer E. Schramm, Eric D. Austin, Mary P. Mullen, Alba Torrent-Vernetta, Carlos Labrandero, Raymond Benza, Maria Jesus del Cerro, Rolf M. F. Berger, Cedric Manlhiot, Allen D. Everett
UT Southwestern Medical Center. Johns Hopkins University. Beatrix Children’s Hospital, University Medical Center Groningen and University of Groningen. Boston Children’s Hospital and Harvard Medical School. Children’s Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine. University of Colorado. Maria Fareri Children’s Hospital at WMC Health and New York Medical College. Vanderbilt University Medical Center. Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus and Universitat Autònoma de Barcelona. “La Paz” University Hospital. Eastern Virginia Medical School and Joan Brock Virginia Health Sciences at Old Dominion University. University Hospital Ramón y Cajal. University of Toronto and Hospital for Sick Children.
United States, Netherlands, Spain and Canada
Circulation
Circulation 2026;
DOI: 10.1161/CIRCULATIONAHA.125.077391
Abstract
Background: Risk prediction is fundamental to pulmonary hypertension (PH) guideline-based care, yet pediatric-specific risk prediction models remain limited, relying primarily on single predictors, expert opinion, or application of adult models to children. The authors developed and externally validated a data-driven 1-year risk prediction model for pediatric PH.
Methods: Pediatric patients with PH (n=345; World Symposium on Pulmonary Hypertension groups 1 and 3) enrolled in the Pediatric Pulmonary Hypertension Network Registry (2014-2020; 50.4% male; median age, 4.9 years [interquartile range, 1.9-10.3]) were split into training (80%) and test cohorts (20%). The Dutch National Registry for Pulmonary Hypertension in Childhood (n=155 [1993-2020]) and the Spanish Registry of Pediatric Pulmonary Hypertension (n=327 [2009-2023]) were used for external validation. From 176 variables, BorutaSHAP feature selection with random forest identified 16 predictors for a 1-year outcome of time to death, transplant, Potts shunt, or atrial septostomy, modeled using extreme gradient boosting. Performance was assessed with the area under the receiver operating characteristic curve, confusion matrices, calibration, and Kaplan-Meier event-free survival.
Results: The final model achieved an area under the receiver operating characteristic curve of 0.90 (0.79-0.97) and 99% (96%-99%) negative predictive value in testing, dividing participants into 3 groups with strong outcome discrimination. External validation showed an area under the receiver operating characteristic curve of 0.76 (Dutch National Registry for Pulmonary Hypertension in Childhood, 0.70-0.81) and 0.77 (Spanish Registry of Pediatric Pulmonary Hypertension, 0.73-0.82) with negative predictive values of 93% (93%-97%) and 96% (93%-97%), respectively. Kaplan-Meier analysis significantly differentiated outcomes by risk group.
Conclusions: This multicenter, validated model provides good 1-year risk prediction in pediatric PH across World Symposium on Pulmonary Hypertension groups 1 and 3, providing a robust tool for clinical risk stratification to guide therapy and addressing a gap in pediatric PH care.
Category
Diagnostic Testing for Pulmonary Vascular Disease. Non-invasive Testing
Diagnostic Testing for Pulmonary Vascular Disease. Invasive Testing
Diagnostic Testing for Pulmonary Vascular Disease. Risk Stratification
Mechanical and Computer Models of Pulmonary Vascular Disease and Therapy
Age Focus: Pediatric Pulmonary Vascular Disease
Fresh or Filed Publication: Fresh (PHresh). Less than 1-2 years since publication
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