Nassar Mahmoud, Abosheaishaa Hazem, Elfert Khaled, Beran Azizullah, Ismail Abdellatif, Mohamed Mouhand, Misra Anoop, Essibayi Muhammed Amir, Altschul David J, Azzam Ahmed Y
Department of Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
Internal Medicine Department, Icahn School of Medicine at Mount Sinai, NYC H+H Queens, New York, NY, USA.
ASIDE Intern Med. 2025 Apr;1(2):24-33. doi: 10.71079/aside.im.03222516. Epub 2025 Mar 22.
The increasing utilization of real-world data platforms in medical research necessitates a comprehensive understanding of their methodological strengths and limitations. TriNetX has emerged as a significant platform for exploring large healthcare datasets. This review aims to critically evaluate the methodological framework and limitations of TriNetX, assess the impact of electronic health record coding accuracy on data reliability, and analyze the platform's capacity for generating generalizable real-world evidence in clinical research.
We conducted a comprehensive review examining TriNetX's data architecture, quality metrics, and research applications, focusing on data integrity, platform architecture, and the external validity of research findings.
The analysis reveals significant methodological considerations. TriNetX's reliance on retrospective data introduces biases such as selection bias and confounding variables. The coding accuracy of electronic health records, which have not been independently validated, is a critical determinant of data reliability. The demographic representation is limited, affecting the generalizability of results.
Despite its extensive use, TriNetX's effective utilization requires careful consideration of its inherent limitations. The platform's data, predominantly from insured populations in academic and acute care settings, may not fully represent broader demographic groups. Addressing these methodological constraints is crucial for enhancing the reliability and applicability of research findings derived from TriNetX.
TriNetX is a valuable resource for healthcare research. However, its limitations must be acknowledged, and future research should focus on standardizing data collection and enhancing data validation processes to mitigate platform-specific biases and improve the quality and applicability of the findings.
医学研究中对真实世界数据平台的使用日益增加，这就需要全面了解其方法学优势和局限性。TriNetX已成为探索大型医疗数据集的重要平台。本综述旨在批判性地评估TriNetX的方法学框架和局限性，评估电子健康记录编码准确性对数据可靠性的影响，并分析该平台在临床研究中生成可推广的真实世界证据的能力。
我们进行了一项全面综述，研究TriNetX的数据架构、质量指标和研究应用，重点关注数据完整性、平台架构以及研究结果的外部有效性。
分析揭示了重要的方法学考量。TriNetX对回顾性数据的依赖会引入选择偏倚和混杂变量等偏倚。未经独立验证的电子健康记录的编码准确性是数据可靠性的关键决定因素。人口统计学代表性有限，影响结果的可推广性。
尽管TriNetX被广泛使用，但其有效利用需要仔细考虑其固有局限性。该平台的数据主要来自学术和急性护理环境中的参保人群，可能无法充分代表更广泛的人口群体。解决这些方法学限制对于提高从TriNetX得出的研究结果的可靠性和适用性至关重要。
TriNetX是医疗保健研究的宝贵资源。然而，必须承认其局限性，未来的研究应专注于规范数据收集和加强数据验证过程，以减轻特定于该平台的偏倚，并提高研究结果的质量和适用性。
