Department of Cardiology, Herlev and Gentofte University Hospital, Copenhagen, Denmark.
Department of Cardiology, Herlev and Gentofte University Hospital, Copenhagen, Denmark.
Int J Cardiol. 2025 Jan 1;418:132636. doi: 10.1016/j.ijcard.2024.132636. Epub 2024 Oct 10.
Global longitudinal strain (GLS) is recognized as a powerful predictor of heart failure (HF). However, the entire strain curve may entail important prognostic information regarding HF risk that might be undiscovered by only focusing on the peak strain value.
The hypothesis of the present study was, that analysis of the entire strain curve using unsupervised machine learning (uML) would reveal novel ventricular deformation patterns capable of predicting incident HF independently of GLS.
Longitudinal strain curves from 3710 subjects from the general population without prevalent HF were analyzed using uML.
Mean age was 56 years and 43 % were male. During a median follow-up of 5.3 years, 92 subjects (2.5 %) developed HF. The uML algorithm generated a hierarchical clustering tree (HCT) resulting in 10 different clusters. Generally, the strain curves displayed reduced early diastolic strain to peak-strain ratio with an increasing incidence rate of HF. In multivariable Cox regressions, cluster 9 was significantly associated with increased risk of HF when compared to cluster 2-5, and 7-8 [For cluster 3: HR 8.95, 95 %CI: 2.08;38.48, P = 0.003] even though the subjects of cluster 9 were younger, displayed healthier clinical baseline characteristics, and only had slightly reduced GLS. The mean strain curve of cluster 9 displayed an early systolic lengthening followed by a late and reduced contraction specifically related to the basal lateral segment.
The unsupervised machine learning algorithm identified unknown strain patterns beyond GLS presumably related to increased risk of HF.
整体纵向应变（GLS）被认为是心力衰竭（HF）的强有力预测因子。然而，仅关注峰值应变值可能会忽略整个应变曲线所包含的重要预后信息，而这些信息可能与 HF 风险有关。
本研究的假设是，使用无监督机器学习（uML）分析整个应变曲线可以揭示出新颖的心室变形模式，这些模式可以独立于 GLS 预测 HF 的发生。
使用 uML 分析来自无明显 HF 的一般人群的 3710 名受试者的纵向应变曲线。
受试者的平均年龄为 56 岁，其中 43%为男性。在中位数为 5.3 年的随访期间，92 名受试者（2.5%）发生 HF。uML 算法生成了一个层次聚类树（HCT），共生成了 10 个不同的聚类。一般来说，应变曲线显示出舒张早期应变与峰值应变比值降低，HF 的发生率逐渐增加。在多变量 Cox 回归中，与聚类 2-5 和 7-8 相比，聚类 9 与 HF 风险增加显著相关[对于聚类 3：HR 8.95，95%CI：2.08；38.48，P=0.003]，尽管聚类 9 的受试者更年轻，具有更健康的临床基线特征，且 GLS 仅略有降低。聚类 9 的平均应变曲线显示出早期收缩期延长，随后是晚期和收缩减少，这与基底外侧节段特别相关。
无监督机器学习算法确定了 GLS 以外的未知应变模式，这些模式可能与 HF 风险增加有关。
