Assessment of the CLOT (children's likelihood of thrombosis) real-time risk prediction model of hospital-associated venous thromboembolism in children with congenital heart disease - 04/05/24
, Ryan P. Moore, MS b, #, Daniel W. Byrne, MS b, Henry J. Domenico, MS b, Allison P. Wheeler, MD, MSCI c, d, Shannon C. Walker, MD c, d, Prince J. Kannankeril, MD, MSCI aAbstract |
Background |
Children with congenital heart disease (CHD) are at high risk for hospital-associated venous thromboembolism (HA-VTE). The children's likelihood of thrombosis (CLOT) trial validated a real-time predictive model for HA-VTE using data extracted from the EHR for pediatric inpatients. We tested the hypothesis that addition of CHD specific data would improve model prediction in the CHD population.
Methods |
Model performance in CHD patients from 2010 to 2022, was assessed using 3 iterations of the CLOT model: 1) the original CLOT model, 2) the original model refit using only data from the CHD cohort, and 3) the model updated with the addition of cardiopulmonary bypass time, STAT Mortality Category, height, and weight as covariates. The discrimination of the three models was quantified and compared using AUROC.
Results |
Our CHD cohort included 1457 patient encounters (median 2.0 IQR [0.5-5.2] years-old). HA-VTE was present in 5% of our CHD cohort versus 1% in the general pediatric population. Several features from the original model were associated with thrombosis in the CHD cohort including younger age, thrombosis history, infectious disease consultation, and EHR coding of a central venous line. Lower height and weight were associated with thrombosis. HA-VTE rate was 12% (18/149) amongst those with STAT Category 4-5 operation versus 4% (49/1256) with STAT Category 1-3 operation (P < .001). Longer cardiopulmonary bypass time (124 [92-205] vs. 94 [65–136] minutes, P < .001) was associated with thrombosis. The AUROC for the original (0.80 95% CI [0.75-0.85]), refit (0.85 [0.81-0.89]), and updated (0.86 [0.81-0.90]) models demonstrated excellent discriminatory ability within the CHD cohort.
Conclusion |
The automated approach with EHR data extraction makes the applicability of such models appealing for ease of clinical use. The addition of cardiac specific features improved model discrimination; however, this benefit was marginal compared to refitting the original model to the CHD cohort. This suggests strong predictive generalized models, such as CLOT, can be optimized for cohort subsets without additional data extraction, thus reducing cost of model development and deployment.
Le texte complet de cet article est disponible en PDF.Graphical Abstract |
Plan
Vol 272
P. 37-47 - juin 2024 Retour au numéro
Article précédent
- Ticagrelor with or without aspirin following percutaneous coronary intervention in high-risk patients with concomitant peripheral artery disease: A subgroup analysis of the TWILIGHT randomized clinical trial
- Mitchell Krucoff, Alessandro Spirito, Usman Baber, Samantha Sartori, Dominick J. Angiolillo, Carlo Briguori, David J. Cohen, Timothy Collier, George Dangas, Dariusz Dudek, Javier Escaned, C. Michael Gibson, Ya-Ling Han, Kurt Huber, Adnan Kastrati, Upendra Kaul, Ran Kornowski, Vijay Kunadian, Birgit Vogel, Shamir R. Mehta, David Moliterno, Gennaro Sardella, Richard A. Shlofmitz, Samin Sharma, Philippe Gabriel Steg, Stuart Pocock, Roxana Mehran
- Predicting the risk of 1-year mortality among patients hospitalized for acute heart failure in China
- Lihua Zhang, Wei Wang, Xiqian Huo, Guangda He, Yanchen Liu, Yan Li, Lubi Lei, Jingkuo Li, Boxuan Pu, Yue Peng, Jing Li
Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.
Déjà abonné à cette revue ?