Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral Waveforms
Background: Left ventricular (LV) pressure measurement is the clinical gold standard for assessing cardiac function; however, its reliance on invasive catheterization limits accessibility and widespread use. Objectives: This study aimed to develop a cuff-based machine learning (cuff-ML) approach for...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | JACC: Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772963X25005290 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849226856793899008 |
|---|---|
| author | Alessio Tamborini, PhD Arian Aghilinejad, PhD Ray V. Matthews, MD Morteza Gharib, PhD |
| author_facet | Alessio Tamborini, PhD Arian Aghilinejad, PhD Ray V. Matthews, MD Morteza Gharib, PhD |
| author_sort | Alessio Tamborini, PhD |
| collection | DOAJ |
| description | Background: Left ventricular (LV) pressure measurement is the clinical gold standard for assessing cardiac function; however, its reliance on invasive catheterization limits accessibility and widespread use. Objectives: This study aimed to develop a cuff-based machine learning (cuff-ML) approach for reconstructing LV pressure from noninvasive brachial waveforms as a bedside assessment of cardiac function. Methods: Subjects referred for nonemergent left heart catheterization were recruited for LV pressure and brachial cuff waveform measurement. The cuff-ML method was trained using brachial waveforms to predict LV pressure and was evaluated for morphology and parameters accuracy against invasive catheter measurements. Cardiac function was assessed based on the reduced LV peak pressure derivative ([+]dP/dt <1,200 mm Hg/s). Results: A total of 104 subjects, comprising 3,572 simultaneous LV and cuff-based brachial waveform pairs, were analyzed using a 70:30 train-test split (test cohort: 32 subjects, 1,023 cardiac cycles). The cuff-ML approach demonstrated high accuracy in reconstructing LV waveform shape compared to catheter measurements (median normalized root mean squared error = 8.2%). Pressure-based parameters, including maximum pressure (r = 0.92, P < 0.001), mean blood pressure (r = 0.94, P < 0.001), and developed pressure (r = 0.85, P < 0.001), showed strong correlations with invasive measurements. Cuff-ML-reconstructed waveforms identified abnormal systolic contractility (72% sensitivity, 73% specificity) on a beat-to-beat basis. Conclusions: Cuff-ML accurately reconstructs LV pressure from brachial cuff measurements. This noninvasive approach may be helpful for assessment of cardiac function and requires further study. |
| format | Article |
| id | doaj-art-4aad0618ad0a4f259c4ab5816f5a3992 |
| institution | Kabale University |
| issn | 2772-963X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JACC: Advances |
| spelling | doaj-art-4aad0618ad0a4f259c4ab5816f5a39922025-08-24T05:15:34ZengElsevierJACC: Advances2772-963X2025-09-014910210410.1016/j.jacadv.2025.102104Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral WaveformsAlessio Tamborini, PhD0Arian Aghilinejad, PhD1Ray V. Matthews, MD2Morteza Gharib, PhD3Department of Medical Engineering, California Institute of Technology, Pasadena, California, USA; Address for correspondence: Dr Alessio Tamborini, Department of Medical Engineering, California Institute of Technology, 1200 E California BLVD, Pasadena, California 91125, USA.Department of Medical Engineering, California Institute of Technology, Pasadena, California, USADivision of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USADepartment of Medical Engineering, California Institute of Technology, Pasadena, California, USABackground: Left ventricular (LV) pressure measurement is the clinical gold standard for assessing cardiac function; however, its reliance on invasive catheterization limits accessibility and widespread use. Objectives: This study aimed to develop a cuff-based machine learning (cuff-ML) approach for reconstructing LV pressure from noninvasive brachial waveforms as a bedside assessment of cardiac function. Methods: Subjects referred for nonemergent left heart catheterization were recruited for LV pressure and brachial cuff waveform measurement. The cuff-ML method was trained using brachial waveforms to predict LV pressure and was evaluated for morphology and parameters accuracy against invasive catheter measurements. Cardiac function was assessed based on the reduced LV peak pressure derivative ([+]dP/dt <1,200 mm Hg/s). Results: A total of 104 subjects, comprising 3,572 simultaneous LV and cuff-based brachial waveform pairs, were analyzed using a 70:30 train-test split (test cohort: 32 subjects, 1,023 cardiac cycles). The cuff-ML approach demonstrated high accuracy in reconstructing LV waveform shape compared to catheter measurements (median normalized root mean squared error = 8.2%). Pressure-based parameters, including maximum pressure (r = 0.92, P < 0.001), mean blood pressure (r = 0.94, P < 0.001), and developed pressure (r = 0.85, P < 0.001), showed strong correlations with invasive measurements. Cuff-ML-reconstructed waveforms identified abnormal systolic contractility (72% sensitivity, 73% specificity) on a beat-to-beat basis. Conclusions: Cuff-ML accurately reconstructs LV pressure from brachial cuff measurements. This noninvasive approach may be helpful for assessment of cardiac function and requires further study.http://www.sciencedirect.com/science/article/pii/S2772963X25005290brachial cuffcatheterizationleft ventricular pressuremachine learningperipheral waveformstransfer function |
| spellingShingle | Alessio Tamborini, PhD Arian Aghilinejad, PhD Ray V. Matthews, MD Morteza Gharib, PhD Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral Waveforms JACC: Advances brachial cuff catheterization left ventricular pressure machine learning peripheral waveforms transfer function |
| title | Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral Waveforms |
| title_full | Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral Waveforms |
| title_fullStr | Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral Waveforms |
| title_full_unstemmed | Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral Waveforms |
| title_short | Machine Learning Reconstruction of Left Ventricular Pressure From Peripheral Waveforms |
| title_sort | machine learning reconstruction of left ventricular pressure from peripheral waveforms |
| topic | brachial cuff catheterization left ventricular pressure machine learning peripheral waveforms transfer function |
| url | http://www.sciencedirect.com/science/article/pii/S2772963X25005290 |
| work_keys_str_mv | AT alessiotamboriniphd machinelearningreconstructionofleftventricularpressurefromperipheralwaveforms AT arianaghilinejadphd machinelearningreconstructionofleftventricularpressurefromperipheralwaveforms AT rayvmatthewsmd machinelearningreconstructionofleftventricularpressurefromperipheralwaveforms AT mortezagharibphd machinelearningreconstructionofleftventricularpressurefromperipheralwaveforms |