■近年来,在文献中,使用人工智能(AI)模型进行个性化风险评估并预测经导管主动脉瓣植入术(TAVI)后患者结局已成为一个日益重要的话题.与传统风险评分相比,本研究旨在评估AI算法在预测TAVI后死亡率方面的预测准确性。
■遵循系统评价的首选报告项目和系统评价的荟萃分析(PRISMA)标准,进行了系统审查。我们在PubMed中搜索了四个数据库,Medline,Embase,和Cochrane-从2023年6月19日至6月24日,2023年。
■从2,239条确定的记录中,删除了1,504个重复项,筛选了735份手稿,10项研究纳入我们的综述.我们对5项研究和9,398例患者的汇总分析显示,与传统评分预测相比,与AI死亡率预测相关的平均曲线下面积(AUC)显著更高(MD:-0.16,CI:-0.22至-0.10,p<0.00001)。30天死亡率(MD:-0.08,CI:-0.13至-0.03,p=0.001)和1年死亡率(MD:-0.18,CI:-0.27至-0.10,p<0.0001)的亚组分析也显示,AI预测的平均AUC明显高于传统评分预测。所有10项研究和22,933例患者的合并平均AUC为0.79[0.73,0.85]。
与传统风险评分相比,AI模型在预测TAVI后死亡率方面具有更高的预测准确性。总的来说,这篇综述展示了AI在TAVI患者中实现个性化风险评估的潜力.
■此系统评价和荟萃分析已在国际前瞻性系统评价登记册(PROSPERO)下注册,注册名称“人工智能评估的经导管主动脉瓣置换术全因死亡率”和注册编号CRD42023437705。没有准备审查方案。登记时提供的信息没有修改。
■https://www.crd.约克。AC.英国/,PROSPERO(CRD42023437705)。
UNASSIGNED: In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve Implantation (TAVI) has been a topic of increasing relevance in literature. This study aims to evaluate the predictive accuracy of AI algorithms in forecasting post-TAVI mortality as compared to traditional risk scores.
UNASSIGNED: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Systematic Reviews (PRISMA) standard, a systematic
review was carried out. We searched four databases in total-PubMed, Medline, Embase, and Cochrane-from 19 June 2023-24 June, 2023.
UNASSIGNED: From 2,239 identified records, 1,504 duplicates were removed, 735 manuscripts were screened, and 10 studies were included in our
review. Our pooled analysis of 5 studies and 9,398 patients revealed a significantly higher mean area under curve (AUC) associated with AI mortality predictions than traditional score predictions (MD: -0.16, CI: -0.22 to -0.10, p < 0.00001). Subgroup analyses of 30-day mortality (MD: -0.08, CI: -0.13 to -0.03, p = 0.001) and 1-year mortality (MD: -0.18, CI: -0.27 to -0.10, p < 0.0001) also showed significantly higher mean AUC with AI predictions than traditional score predictions. Pooled mean AUC of all 10 studies and 22,933 patients was 0.79 [0.73, 0.85].
UNASSIGNED: AI models have a higher predictive accuracy as compared to traditional risk scores in predicting post-TAVI mortality. Overall, this
review demonstrates the potential of AI in achieving personalised risk assessment in TAVI patients.
UNASSIGNED: This systematic
review and meta-analysis was registered under the International Prospective Register of Systematic Reviews (PROSPERO), under the registration name \"All-Cause Mortality in Transcatheter Aortic Valve Replacement Assessed by Artificial Intelligence\" and registration number CRD42023437705. A
review protocol was not prepared. There were no amendments to the information provided at registration.
UNASSIGNED: https://www.crd.york.ac.uk/, PROSPERO (CRD42023437705).