Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL.
Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL.
Am Heart J. 2023 Jul;261:64-74. doi: 10.1016/j.ahj.2023.03.008. Epub 2023 Mar 25.
Artificial intelligence (AI), and more specifically deep learning, models have demonstrated the potential to augment physician diagnostic capabilities and improve cardiovascular health if incorporated into routine clinical practice. However, many of these tools are yet to be evaluated prospectively in the setting of a rigorous clinical trial-a critical step prior to implementing broadly in routine clinical practice.
To describe the rationale and design of a proposed clinical trial aimed at evaluating an AI-enabled electrocardiogram (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria.
The protocol will enroll 1,000 pregnant and postpartum women who reside in Nigeria in a prospective randomized clinical trial. Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. Women aged 18 and older, seen for routine obstetric care at 6 sites (2 Northern and 4 Southern) in Nigeria will be included. Participants will be randomized to the study intervention or control arm in a 1:1 fashion. This study aims to enroll participants representative of the general obstetric population at each site. The primary outcome is a new diagnosis of cardiomyopathy, defined as left ventricular ejection fraction (LVEF) < 50% during pregnancy or within 12 months postpartum. Secondary outcomes will include the detection of impaired left ventricular function (at different LVEF cut-offs), and exploratory outcomes will include the effectiveness of AI-ECG tools for cardiomyopathy detection, new diagnosis of cardiovascular disease, and the development of composite adverse maternal cardiovascular outcomes.
This clinical trial focuses on the emerging field of cardio-obstetrics and will serve as foundational data for the use of AI-ECG tools in an obstetric population in Nigeria. This study will gather essential data regarding the utility of the AI-ECG for cardiomyopathy detection in a predominantly Black population of women and pave the way for clinical implementation of these models in routine practice.
Clinicaltrials.gov: NCT05438576.
人工智能（AI），尤其是深度学习模型，如果将其纳入常规临床实践，有可能增强医生的诊断能力，改善心血管健康。然而，在严格的临床试验环境中，对这些工具中的许多工具进行前瞻性评估——这是在常规临床实践中广泛实施之前的关键步骤——仍有待进行。
描述一项拟议临床试验的原理和设计，该试验旨在评估一种用于尼日利亚产科人群中心肌病检测的人工智能心电图（AI-ECG）。
该方案将在尼日利亚招募 1000 名居住在尼日利亚的孕妇和产后妇女，进行一项前瞻性随机临床试验。尼日利亚是全世界报道的围产期心肌病发病率最高的国家。年龄在 18 岁及以上、在尼日利亚 6 个地点（北部 2 个和南部 4 个）接受常规产科护理的妇女将被纳入。参与者将以 1:1 的比例随机分配到研究干预组或对照组。本研究旨在招募每个地点一般产科人群的代表性参与者。主要结局是怀孕期间或产后 12 个月内新诊断为心肌病，定义为左心室射血分数（LVEF）<50%。次要结局将包括左心室功能障碍（在不同的 LVEF 截止值）的检测，探索性结局将包括 AI-ECG 工具对心肌病检测、新诊断心血管疾病和复合不良产妇心血管结局的有效性。
本临床试验侧重于新兴的围产心脏病学领域，将为尼日利亚产科人群中使用 AI-ECG 工具提供基础数据。本研究将收集关于 AI-ECG 用于检测主要是黑人妇女心肌病的效用的重要数据，并为这些模型在常规实践中的临床应用铺平道路。
Clinicaltrials.gov：NCT05438576。
