Artificial intelligence-enabled non-invasive quantification of left ventricular end-systolic pressure: an in silico analysis.
- 1Faculty of Life Sciences and Medicine, King's College London, London, UK. [email protected].
- 2Faculty of Life Sciences and Medicine, King's College London, London, UK.
Abstract
Background
Invasive pressure-volume loop (PVL) analysis is the gold standard method to quantify myocardial physiology. To enhance broader clinical applicability, non-invasive approaches to derive left ventricular (LV) PVLs have gained prominence, typically estimating LV end-systolic pressure (ESP) from brachial blood pressure (BP) alone. However, this approach does not account for factors that may modulate the pressure waveform as it propagates from the LV to the brachial artery, notably arterial stiffness. We trained an artificial-intelligence (AI) model to predict invasive LVESP from a set of non-invasive hemodynamic parameters and compared its performance with commonly used equations for estimating LVESP using solely brachial BPs.Methods
An in silico cohort of healthy adults (N = 3,837, age: 48.6 ± 17.0y), previously shown to have good agreement for aortic and brachial BP with in vivo data, was split into training and validation cohorts using an internal hold-out validation approach. Clinically relevant parameters were selected as inputs to a random forest machine learning model.Results
The best performing model included systolic and diastolic brachial BP, heart rate, age, and carotid-femoral pulse wave velocity (PWV), and reliably predicted LVESP (root mean square error: 0.99 mmHg). AI-derived LVESP correlated more strongly with simulated invasive LVESP (r = 0.994) than commonly used equations (r = 0.909-0.908). Bland-Altman analysis demonstrated significant bias in brachial BP-only equations (mean bias: -4.01, limits of agreement [LoA] [± 1.96 standard deviations]: -11.1 to 3.06 mmHg), with the direction of bias varying across different ages. AI-derived LVESP displayed smaller bias (mean bias: 0.002, LoA: -1.94 to 1.94 mmHg) and had negligible age-related bias across the lifespan.Conclusions
In this hypothesis generating study, a novel non-invasive, AI-enabled approach to LVESP quantification incorporating age, PWV and other key hemodynamic parameters alongside brachial BPs yielded smaller average error across the lifespan than commonly used brachial BP-only equations. These results warrant validation in vivo to ascertain real-world predictive performance.
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BibTeX
@article{long2026artificial,
title = {{Artificial intelligence-enabled non-invasive quantification of left ventricular end-systolic pressure: an in silico analysis.}},
author = {Edouard Long and Roberta Dunn and Vitaliy Androshchuk},
journal = {BMC cardiovascular disorders},
year = {2026},
month = aug,
doi = {10.1186/s12872-026-06358-8},
url = {https://europepmc.org/article/MED/42958052},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence-enabled non-invasive quantification of left ventricular end-systolic pressure: an in silico analysis.
AU - Edouard Long
AU - Roberta Dunn
AU - Vitaliy Androshchuk
PY - 2026
DA - 2026/08/04
JO - BMC cardiovascular disorders
DO - 10.1186/s12872-026-06358-8
UR - https://europepmc.org/article/MED/42958052
AB - Background Invasive pressure-volume loop (PVL) analysis is the gold standard method to quantify myocardial physiology. To enhance broader clinical applicability, non-invasive approaches to derive left ventricular (LV) PVLs have gained prominence, typically estimating LV end-systolic pressure (ESP) from brachial blood pressure (BP) alone. However, this approach does not account for factors that may modulate the pressure waveform as it propagates from the LV to the brachial artery, notably arterial stiffness. We trained an artificial-intelligence (AI) model to predict invasive LVESP from a set of non-invasive hemodynamic parameters and compared its performance with commonly used equations for estimating LVESP using solely brachial BPs.Methods An in silico cohort of healthy adults (N = 3,837, age: 48.6 ± 17.0y), previously shown to have good agreement for aortic and brachial BP with in vivo data, was split into training and validation cohorts using an internal hold-out validation approach. Clinically relevant parameters were selected as inputs to a random forest machine learning model.Results The best performing model included systolic and diastolic brachial BP, heart rate, age, and carotid-femoral pulse wave velocity (PWV), and reliably predicted LVESP (root mean square error: 0.99 mmHg). AI-derived LVESP correlated more strongly with simulated invasive LVESP (r = 0.994) than commonly used equations (r = 0.909-0.908). Bland-Altman analysis demonstrated significant bias in brachial BP-only equations (mean bias: -4.01, limits of agreement [LoA] [± 1.96 standard deviations]: -11.1 to 3.06 mmHg), with the direction of bias varying across different ages. AI-derived LVESP displayed smaller bias (mean bias: 0.002, LoA: -1.94 to 1.94 mmHg) and had negligible age-related bias across the lifespan.Conclusions In this hypothesis generating study, a novel non-invasive, AI-enabled approach to LVESP quantification incorporating age, PWV and other key hemodynamic parameters alongside brachial BPs yielded smaller average error across the lifespan than commonly used brachial BP-only equations. These results warrant validation in vivo to ascertain real-world predictive performance.
SN - 1471-2261
ER -