Playford David, Stewart Simon, Watts Andrew, Kezurer Dean, Chan Yih-Kai, Strange Geoff
Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia.
Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia; BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom.
JACC Adv. 2025 Jun 23;4(7):101891. doi: 10.1016/j.jacadv.2025.101891.
Identification of left ventricular (LV) dysfunction following echocardiographic investigations remains problematic, particularly when the ejection fraction (EF) is preserved.
The authors examined the operational characteristics of artificial intelligence LV dysfunction (AI-LVD) identification from routinely obtained echocardiographic measurements.
Following initial training in 126,136 (imputation cohort) and 254,735 (training cohort) cases from the National Echo Database of Australia, the AI-LVD was tested in 81,509 cases (last echo January 1, 2000-May 21, 2019) with no mitral valve intervention or pacemaker. This cohort comprised 41,796 men (51.3%) aged 62.3 ± 17.1 years and 39,713 women aged 63.2 ± 18.4 years, in whom 4,490 (5.5%), 3,734 (4.6%), and 59,297 (72.7%) had reduced, mildly reduced, and preserved EF, while 13,988 (17.2%) had no recorded EF and 39,940 (45.2%) had "indeterminate" filling pressures.
Overall, the AI-LVD generated a (sex-specific) output in decile distributions consistent with increasingly higher levels of LV dysfunction and mortality-actual 5-year mortality rising from 5.7% to 66.3% and 2.3% to 64.2% in men and women, respectively. The prognostic capacity of the AI-LVD persisted in preserved EF, when adjusting for age, year of echo, and missing echo parameters-with adjusted hazard for all-cause mortality during 1,541 (812-2,682) days follow-up 4.93-fold (95% CI: 4.35-5.59) and 7.11-fold (95% CI: 5.85-8.64) higher in the highest vs lowest decile group in men and women, respectively.
A new AI-LVD algorithm using only echocardiographic measurements can reliably identify prognostically important LV dysfunction, including in preserved EF, even when key reporting parameters are missing. The AI-LVD can be used in real-time during routine echocardiography reporting.
在超声心动图检查后识别左心室（LV）功能障碍仍然存在问题，尤其是当射血分数（EF）保留时。
作者研究了从常规获得的超声心动图测量中识别人工智能左心室功能障碍（AI-LVD）的操作特征。
在对来自澳大利亚国家超声数据库的126,136例（插补队列）和254,735例（训练队列）病例进行初始训练后，对81,509例（最后一次超声心动图检查时间为2000年1月1日至2019年5月21日）未进行二尖瓣干预或安装起搏器的病例进行了AI-LVD测试。该队列包括41,796名男性（51.3%），年龄为62.3±17.1岁，以及39,713名女性，年龄为63.2±18.4岁，其中4,490例（5.5%）、3,734例（4.6%）和59,297例（72.7%）的EF降低、轻度降低和保留，而13,988例（17.2%）未记录EF，39,940例（45.2%）的充盈压“不确定”。
总体而言，AI-LVD生成的（按性别分类）输出呈十分位数分布，与左心室功能障碍和死亡率的升高水平一致——男性和女性的实际5年死亡率分别从5.7%升至66.3%和从2.3%升至64.2%。在调整年龄、超声心动图检查年份和缺失的超声心动图参数后，AI-LVD在EF保留的情况下仍具有预后能力——在1,541（812 - 2,682）天的随访期间，男性和女性最高十分位数组与最低十分位数组的全因死亡率调整后风险分别高出4.93倍（95%CI：4.35 - 5.59）和7.11倍（95%CI：5.85 - 8.64）。
一种仅使用超声心动图测量的新型AI-LVD算法能够可靠地识别具有预后重要性的左心室功能障碍，包括EF保留的情况，即使关键报告参数缺失。AI-LVD可在常规超声心动图报告期间实时使用。
