围产期心肌病管理的研究进展
Research Progress in the Management of Peripartum Cardiomyopathy
DOI: 10.12677/acm.2026.1662296, PDF,   
作者: 张 榕：吉首大学株洲临床学院，湖南 株洲；吴 健*：中南大学湘雅医学院附属株洲医院全科医学科，湖南 株洲
关键词: 围产期心肌病；围产期心肌病的管理；人工智能；心力衰竭；Peripartum Cardiomyopathy； Management of Peripartum Cardiomyopathy； Artificial Intelligence； Heart Failure
摘要: 围产期心肌病(PPCM)是一种罕见且严重的心脏疾病，常见于孕期最后一个月或产后五个月内，主要表现为射血分数降低的心力衰竭。该疾病主要涉及心肌功能障碍，后期可能会进展为心力衰竭。最新的流行病学研究显示，全球PPCM的发病率和死亡率呈上升趋势。虽然确切的发病机制尚不清楚，但已有多因素病因被提出。已识别的风险因素包括子痫前期、非洲血统、高龄孕产妇和多胎妊娠。对于该疾病管理，全球循证研究有限，目前的管理策略缺乏标准化的方案。目前治疗主要遵循射血分数降低心力衰竭(HFrEF)的指南，治疗主要遵循射血分数降低的心力衰竭(HFrEF)指南，溴隐亭是唯一专门针对围产期心肌病进行研究的疗法。现有文献强调在疾病稳定期优化护理，突出了对针对性干预措施进行进一步研究以及应对后续妊娠中临床挑战的必要性。人工智能(AI)在彻底改变围产期心肌病(PPCM)的早期筛查和诊断路径方面具有巨大潜力。强大的由人工智能驱动的预测模型的开发有助于进行风险分层，如CARPREG风险指数用于预测孕产妇心脏并发症的可能，从而能够对高风险妊娠进行有针对性的监测。
Abstract: Peripartum cardiomyopathy (PPCM) is a rare yet severe cardiac condition occurring in pregnant individuals, typically manifesting as heart failure with reduced ejection fraction (HFrEF) during the final month of gestation or within five months postpartum. The disorder involves myocardial dysfunction that may progress to heart failure. Recent epidemiological studies indicate a rising global trend in PPCM incidence and mortality. While the exact pathogenesis remains unclear, multifactorial etiologies have been proposed. Identified risk factors include pre-eclampsia, African ancestry, advanced maternal age, and multifetal pregnancies. Current management strategies lack standardized protocols due to limited evidence-based research. Treatment primarily aligns with guidelines for heart failure with reduced ejection fraction (HFrEF), with bromocriptine emerging as the sole therapy specifically investigated for PPCM. Existing literature emphasizes optimizing care during stable phases of the disease, highlighting the need for further investigation into targeted interventions and addressing clinical challenges in subsequent pregnancy. Artificial intelligence (AI) holds significant promise for revolutionizing the early screening and diagnostic pathways for peripartum cardiomyopathy (PPCM). The development of robust AI-driven predictive models could facilitate risk stratification. For example, the CARPREG risk index is used to predict the likelihood of maternal cardiac complications, enabling targeted monitoring of high-risk pregnancies.
文章引用：张榕, 吴健. 围产期心肌病管理的研究进展[J]. 临床医学进展, 2026, 16(6): 926-938. https://doi.org/10.12677/acm.2026.1662296

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