医学
危险分层
瓣膜性心脏病
接收机工作特性
心脏病
二尖瓣反流
队列
疾病
内科学
心脏病学
作者
Swaraj Nandini Pande,J. Yavana Suriya,Sachit Ganapathy,Ajith Ananthakrishna Pillai,Santhosh Satheesh,Nivedita Mondal,K.T. Harichandra Kumar,Candice K. Silversides,Samuel C. Siu,Rohan D’Souza,Anish Keepanasseril
标识
DOI:10.1016/j.jacc.2023.07.023
摘要
Most risk stratification tools for pregnant patients with heart disease were developed in high-income countries and in populations with predominantly congenital heart disease, and therefore, may not be generalizable to those with valvular heart disease (VHD). The purpose of this study was to validate and establish the clinical utility of 2 risk stratification tools—DEVI (VHD-specific tool) and CARPREG-II—for predicting adverse cardiac events in pregnant patients with VHD. We conducted a cohort study involving consecutive pregnancies complicated with VHD admitted to a tertiary center in a middle-income setting from January 2019 to April 2022. Individual risk for adverse composite cardiac events was calculated using DEVI and CARPREG-II models. Performance was assessed through discrimination and calibration characteristics. Clinical utility was evaluated with Decision Curve Analysis. Of 577 eligible pregnancies, 69 (12.1%) experienced a component of the composite outcome. A majority (94.7%) had rheumatic etiology, with mitral regurgitation as the predominant lesion (48.2%). The area under the receiver-operating characteristic curve was 0.884 (95% CI: 0.844-0.923) for the DEVI and 0.808 (95% CI: 0.753-0.863) for the CARPREG-II models. Calibration plots suggested that DEVI score overestimates risk at higher probabilities, whereas CARPREG-II score overestimates risk at both extremes and underestimates risk at middle probabilities. Decision curve analysis demonstrated that both models were useful across predicted probability thresholds between 10% and 50%. In pregnant patients with VHD, DEVI and CARPREG-II scores showed good discriminative ability and clinical utility across a range of probabilities. The DEVI score showed better agreement between predicted probabilities and observed events.
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