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婴幼儿单纯室间隔缺损术前心力衰竭及术后不良事件发生预测因素的研究进展
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Abstract:
室间隔缺损(Ventricular Septal Defect, VSD)是临床上最常见的先天性心脏疾病(Congenital heart disease, CHD)，不仅可单独发生，也可与其他复杂心脏畸形共存。VSD手术治疗在降低患儿的病死率和提高其生活质量方面具有不可忽视的作用。手术前心力衰竭(Heart Failure, HF)的准确诊断及干预是提高手术成功率、降低术后不良事件发生的关键。在成人心力衰竭诊断中常将脑钠肽(Brain Natriuretic Peptide, BNP)等生物标志物作为诊断和治疗的依据，然而小儿心力衰竭及先天性心脏病，没有任何临床生物标志物作为诊断或治疗的标准指南。在信息时代，基于机器学习(Machine Learning, ML)算法建立的模型可提高对相关危险因素预测的准确性。本文结合相关文献对室间隔缺损术前心力衰竭及术后不良事件发生的预测因素进行总结。
Ventricular Septal Defect (VSD) is the most common congenital heart disease (CHD) clinically, which can occur either alone or in combination with other complex heart malformations. Surgical treatment of VSD plays a significant role in reducing mortality and improving the quality of life of affected children. Accurate diagnosis and intervention for heart failure (HF) before surgery are crucial for enhancing surgical success rates and minimizing postoperative adverse events. In adult heart failure diagnosis, biomarkers such as brain natriuretic peptide (BNP) are often used as a basis for diagnosis and treatment. Nevertheless, for pediatric heart failure and congenital heart disease, there are no clinical biomarkers serving as standard guidelines for diagnosis or treatment. In the information era, models based on machine learning (ML) algorithms can improve the accuracy of predicting relevant risk factors. This article summarizes the predictive factors for preoperative heart failure and postoperative adverse events in patients with ventricular septal defects, drawing on relevant literature.
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