van Ravensberg Annemiek E, Scholte Niels T B, Omar Khader Aaram, Brugts Jasper J, Bruining Nico, van der Boon Robert M A
Department of Cardiology, Erasmus MC, Cardiovascular Institute, Thorax Center, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands.
Eur Heart J Digit Health. 2024 Mar 13;5(3):288-294. doi: 10.1093/ehjdh/ztae021. eCollection 2024 May.
Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques.
The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low values of up to 0.04, and the classification models resulted in AUC values of up to 0.59.
In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.
早期发现充血已被证明可改善心力衰竭（HF）患者的预后。然而，用于指导治疗的有创血流动力学参数获取途径有限。本研究旨在开发一种模型，利用传统统计学和机器学习（ML）技术的非侵入性测量来估计有创测量的肺毛细血管楔压（PCWP）。
该研究纳入了2017年至2022年在鹿特丹伊拉斯姆斯医学中心接受右侧心导管插入术的患者。有创测量的PCWP作为结果。模型特征包括动脉血压、饱和度、心率（变异性）、体重和温度的非侵入性测量。使用了各种传统和ML技术，并分别使用回归模型的均方根误差（RMSE）和分类模型的曲线下面积（AUC）评估性能。总共纳入了853例手术，其中31%以HF作为主要诊断，49%的PCWP为12 mmHg或更高。队列的平均年龄为59±14岁，52%为男性。心率变异性与PCWP的相关性最高，为0.16。所有回归模型的RMSE值均低至0.04，分类模型的AUC值高达0.59。
在本研究中，传统和基于ML的非侵入性方法与PCWP的相关性有限。这突出了传统HF监测与血流动力学参数之间的弱相关性，并强调了单一非侵入性测量的局限性。未来的研究应探索趋势分析和其他特征，以改善非侵入性血流动力学监测，因为该领域显然需要进一步进展。
