Machine-learning approaches for risk prediction in transcatheter aortic valve implantation: Systematic review and meta-analysis

荟萃分析 心脏病学 医学 内科学 计算机科学 人工智能
作者
Xander Jacquemyn,Emanuel Van Onsem,Keith A. Dufendach,James A. Brown,Dustin Kliner,Catalin Toma,Derek Serna–Gallegos,Michel Pompeu Sá,Ibrahim Sultan
出处
期刊:The Journal of Thoracic and Cardiovascular Surgery [Elsevier BV]
被引量:4
标识
DOI:10.1016/j.jtcvs.2024.05.017
摘要

Objectives With the expanding integration of artificial intelligence (AI) and machine learning (ML) into the structural heart domain, numerous ML models have emerged for the prediction of adverse outcomes following transcatheter aortic valve implantation (TAVI). We aim to identify, describe, and critically appraise ML prediction models for adverse outcomes after TAVI. Key objectives consisted in summarizing model performance, evaluating adherence to reporting guidelines, and transparency. Methods We searched PubMed, SCOPUS, and Embase through August 2023. We selected published machine learning models predicting TAVI outcomes. Two reviewers independently screened articles, extracted data, and assessed the study quality according to the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Outcomes included summary C-statistics and model risk of bias assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). C-statistics were pooled using a random-effects model. Results Twenty-one studies (118,153 patients) employing various ML algorithms (76 models) were included in the systematic review. Predictive ability of models varied: 11.8% inadequate (C-statistic <0.60), 26.3% adequate (C-statistic 0.60–0.70), 31.6% acceptable (C-statistic 0.70–0.80), and 30.3% demonstrated excellent (C-statistic >0.80) performance. Meta-analyses revealed excellent predictive performance for early mortality (C-statistic: 0.81 [95% CI, 0.65-0.91]), acceptable performance for 1-year mortality (C-statistic: 0.76 [95% CI, 0.67-0.84]), and acceptable performance for predicting permanent pacemaker implantation (C-statistic: 0.75 [95% CI, 0.51-0.90]). Conclusion ML models for TAVI outcomes exhibit adequate to excellent performance, suggesting potential clinical utility. We identified concerns in methodology and transparency, emphasizing the need for improved scientific reporting standards.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
SuperGh发布了新的文献求助10
1秒前
lsybf完成签到,获得积分10
1秒前
重要谷冬发布了新的文献求助10
1秒前
ydb123完成签到,获得积分20
1秒前
nuannuan完成签到,获得积分10
2秒前
卡萨卡萨发布了新的文献求助20
3秒前
赵佳露完成签到,获得积分10
3秒前
seventeen完成签到,获得积分10
5秒前
狂野酒窝发布了新的文献求助10
5秒前
ydb123发布了新的文献求助10
5秒前
香蕉觅云应助孙孙博士采纳,获得10
5秒前
zho发布了新的文献求助30
6秒前
stardust完成签到 ,获得积分10
7秒前
7秒前
布衣完成签到,获得积分20
8秒前
研友_VZG7GZ应助twob采纳,获得10
8秒前
舒心的水卉发布了新的文献求助200
8秒前
葳蕤完成签到,获得积分10
9秒前
久久发布了新的文献求助10
9秒前
10秒前
华仔应助爱睡午觉采纳,获得10
10秒前
Ki_Ayasato发布了新的文献求助30
10秒前
大壳完成签到,获得积分10
13秒前
13秒前
13秒前
Orange应助给大佬递茶采纳,获得10
13秒前
小二郎应助Mac采纳,获得10
14秒前
14秒前
14秒前
沉默寻凝完成签到,获得积分10
14秒前
小蘑菇应助感动的薄荷采纳,获得10
15秒前
SuperGh完成签到,获得积分10
15秒前
我要毕业完成签到,获得积分10
15秒前
15秒前
简单的储发布了新的文献求助10
15秒前
舒心的水卉完成签到,获得积分10
16秒前
16秒前
田様应助ZWK采纳,获得10
16秒前
无望幽月发布了新的文献求助10
18秒前
江思可完成签到,获得积分10
18秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3971942
求助须知:如何正确求助?哪些是违规求助? 3516448
关于积分的说明 11182992
捐赠科研通 3251713
什么是DOI,文献DOI怎么找? 1796075
邀请新用户注册赠送积分活动 876216
科研通“疑难数据库(出版商)”最低求助积分说明 805403