Bulletti Carlo, Franasiak Jason M, Busnelli Andrea, Sciorio Romualdo, Berrettini Marco, Aghajanova Lusine, Bulletti Francesco M, Ata Baris
Help Me Doctor, Assisted Reproductive Technology, Gynecological Endocrinology and Reproductive Surgery, Cattolica, Italy.
Department of Obstetrics, Gynecology, and Reproductive Science, Yale University, New Haven, CT.
Mayo Clin Proc Digit Health. 2024 Aug 26;2(4):518-532. doi: 10.1016/j.mcpdig.2024.08.007. eCollection 2024 Dec.
The aim of this systematic review was to identify clinical decision support algorithms (CDSAs) proposed for assisted reproductive technologies (ARTs) and to evaluate their effectiveness in improving ART cycles at every stage vs traditional methods, thereby providing an evidence-based guidance for their use in ART practice. A literature search on PubMed and Embase of articles published between 1 January 2013 and 31 January 2024 was performed to identify relevant articles. Prospective and retrospective studies in English on the use of CDSA for ART were included. Out of 1746 articles screened, 116 met the inclusion criteria. The selected articles were categorized into 3 areas: prognosis and patient counseling, clinical management, and embryo assessment. After screening, 11 CDSAs were identified as potentially valuable for clinical management and laboratory practices. Our findings highlight the potential of automated decision aids to improve in vitro fertilization outcomes. However, the main limitation of this review was the lack of standardization in validation methods across studies. Further validation and clinical trials are needed to establish the effectiveness of these tools in the clinical setting.
本系统评价的目的是识别为辅助生殖技术(ART)提出的临床决策支持算法(CDSA),并评估其在改善ART周期各阶段效果方面相对于传统方法的有效性,从而为其在ART实践中的应用提供循证指导。我们在PubMed和Embase上检索了2013年1月1日至2024年1月31日发表的文章,以识别相关文章。纳入了关于将CDSA用于ART的英文前瞻性和回顾性研究。在筛选的1746篇文章中,116篇符合纳入标准。所选文章分为3个领域:预后与患者咨询、临床管理和胚胎评估。筛选后,确定了11种CDSA对临床管理和实验室实践具有潜在价值。我们的研究结果凸显了自动化决策辅助工具改善体外受精结局的潜力。然而,本评价的主要局限性在于各研究的验证方法缺乏标准化。需要进一步的验证和临床试验来确定这些工具在临床环境中的有效性。