Lampreia Fabio, Madeira Catarina, Dores Hélder
Associação CoLAB TRIALS, Évora, Portugal.
CHRC, NOVA Medical School, Lisboa, Portugal.
Digit Health. 2024 Sep 5;10:20552076241277703. doi: 10.1177/20552076241277703. eCollection 2024 Jan-Dec.
The recent pandemic ushered in a marked surge in the adoption of digital health technologies (DHTs), necessitating remote approaches aiming to safeguard both patient and healthcare provider well-being. These technologies encompass an array of terms, including e-health, m-health, telemedicine, wearables, sensors, smartphone apps, digital therapeutics, virtual and augmented reality, and artificial intelligence (AI). Notably, some DHTs employed in critical healthcare decisions may transition into the realm of medical devices, subjecting them to more stringent regulatory scrutiny. Consequently, it is imperative to understand the validation processes of these technologies within clinical studies. Our study summarizes an extensive examination of clinical trials focusing on cardiovascular (CV) diseases and digital health (DH) interventions, with particular attention to those incorporating elements of AI. A dataset comprising 107 eligible trials, registered on clinicaltrials.gov and International Clinical Trials Registry Platform (ICTRP) databases until 19 June 2023, forms the basis of our investigation. We focused on clinical trials employing DHTs in the European context, revealing a diverse landscape of interventions. Devices constitute the predominant category (45.8%), followed by behavioral interventions (17.8%). Within the CV domain, trials predominantly span pivotal or confirmatory phases, with a notable presence of smaller feasibility and exploratory studies. Notably, a majority of trials exhibit randomized, parallel assignment designs. When analyzing the multifaceted landscape of trial outcomes, we identified various categories such as physiological and functional measures, diagnostic accuracy, CV events and mortality, patient outcomes, quality of life, treatment adherence and effectiveness, quality of hospital processes, and usability/feasibility measures. Furthermore, we delve into a subset of 15 studies employing AI and machine learning, describing various study design features, intended purposes and the validation strategies employed. In summary, we aimed to elucidate the diverse applications, study design features, and objectives of the evolving CV-related DHT clinical trials field.
近期的大流行促使数字健康技术（DHTs）的采用显著增加，这就需要采用远程方法，以保障患者和医疗服务提供者的健康。这些技术涵盖一系列术语，包括电子健康、移动健康、远程医疗、可穿戴设备、传感器、智能手机应用程序、数字疗法、虚拟现实和增强现实以及人工智能（AI）。值得注意的是，一些用于关键医疗决策的DHTs可能会转变为医疗设备领域，从而受到更严格的监管审查。因此，了解这些技术在临床研究中的验证过程至关重要。我们的研究总结了对聚焦于心血管（CV）疾病和数字健康（DH）干预措施的临床试验的广泛审查，尤其关注那些包含人工智能元素的试验。一个由107项符合条件的试验组成的数据集构成了我们调查的基础，这些试验截至2023年6月19日已在clinicaltrials.gov和国际临床试验注册平台（ICTRP）数据库上注册。我们关注在欧洲背景下采用DHTs的临床试验，揭示了干预措施的多样化格局。设备构成主要类别（45.8%），其次是行为干预（17.8%）。在心血管领域，试验主要处于关键或确证阶段，同时也有相当数量的小型可行性和探索性研究。值得注意的是，大多数试验采用随机、平行分组设计。在分析试验结果的多方面格局时，我们确定了各种类别，如生理和功能指标、诊断准确性、心血管事件和死亡率、患者结局、生活质量、治疗依从性和有效性、医院流程质量以及可用性/可行性指标。此外，我们深入研究了15项采用人工智能和机器学习的研究子集，描述了各种研究设计特征、预期目的以及所采用的验证策略。总之，我们旨在阐明不断发展的与心血管相关的DHT临床试验领域的多样化应用、研究设计特征和目标。
