Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research

Testing an inverse modeling approach with gradient boosting regression for stroke volume estimation using patient thermodilution data

Stroke volume (SV) is a major indicator of cardiovascular function, providing essential information about heart performance and blood flow adequacy. Accurate SV measurement is particularly important for assessing patients with heart failure, managing patients undergoing major surgeries, and delivering optimal care in critical settings. Traditional methods for estimating SV, such as thermodilution, are invasive and unsuitable for routine diagnostics. Non-invasive techniques, although safer and more accessible, often lack the precision and user-friendliness needed for continuous bedside monitoring. We developed a modified method for SV estimation that combines a validated 1-D model of the systemic circulation with machine learning. Our approach replaces the traditional optimization process developed in our previous work, with a regression method, utilizing an in silico-generated dataset of various hemodynamic profiles to create a gradient boosting regression-enabled SV estimator. This dataset accurately mimics the dynamic characteristics of the 1-D model, allowing for precise SV predictions without resource-intensive parameter adjustments. We evaluated our method against SV values derived from the gold standard thermodilution method in 24 patients. The results demonstrated that our approach provides a satisfactory agreement between the predicted and reference data, with a MAE of 16 mL, a normalized RMSE of 21%, a bias of -9.2 mL, and limits of agreement (LoA) of [-47, 28] mL. A correlation coefficient of r = 0.7 (p < 0.05) was reported, with the predicted SV slightly underestimated (68 ± 23 mL) in comparison to the reference SV (77 ± 26 mL). The significant reduction in computational time of our method for SV assessment should make it suitable for real-time clinical applications.

Overnight stiffness index from finger photoplethysmography in relation to markers of cardiovascular risk and vascular ageing

Wearable technology, such as photoplethysmography (PPG), enables easily accessible individual health data with the potential for improved risk assessment. We hypothesized that the overnight stiffness index (OSI), derived from nocturnal finger PPG, could be used to assess cardiovascular risk and vascular ageing. Subjects with confirmed or suspected hypertension (n = 79, 56 males) underwent simultaneous ambulatory blood pressure monitoring (ABPM) and overnight sleep polygraphy with a continuous PPG registration. Overnight PPG-based pulse propagation time was used to calculate OSI. Associations between OSI and markers of cardiovascular risk, blood pressure, and indices of arterial stiffness, as indicators of vascular ageing, were assessed. Subjects were stratified into low and high OSI (according to median, 10.9 m/s). SCORE2/SCORE2-OP and Framingham risk scores were calculated. The high OSI group had higher SCORE2/SCORE2-OP (9.5 [5.5;12.5] vs 5.0 [4.0;6.5]), and OSI correlated with SCORE2/SCORE2-OP and Framingham risk score (rs = 0.40 and rs = 0.41; both P < 0.01). Indices of arterial stiffness were increased in the high OSI group including ABPM awake and asleep pulse pressures (59 ± 14 vs 50 ± 9 mmHg, P < 0.01, and 54 ± 14 vs 45 ± 7 mmHg, P < 0.001), and ambulatory arterial stiffness index (0.47 ± 0.12 vs 0.37 ± 0.11, P < 0.001), respectively. OSI correlated with 24-h and asleep pulse pressure also after adjusting for confounders. OSI was related to systolic ABPM (awake r = 0.42, asleep r = 0.55; both P < 0.001) and diastolic ABPM (asleep r = 0.36, P < 0.01). OSI, a novel PPG-based measure of nocturnal arterial stiffness, correlates with established cardiovascular risk scores and with blood pressure-derived indices of vascular ageing. This simple method may facilitate cardiovascular risk assessment using readily available medical and wearable consumer devices.

Age group classification based on optical measurement of brain pulsation using machine learning

Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups. Brain pulses were extracted from fNIRS using a single 830 nm wavelength. Four feature sets were derived from log-normal parameters estimated by pulse decomposition algorithm. ML experiments utilized support vector machines and random forest learners, along with maximum relevance minimum redundancy and principal component analysis for feature selection. Performance with increasing sample size was estimated using learning curve method. The best mean balanced accuracies for each feature set were over 75% (75.9%, 76.4%, 79.3%, 76.9%), indicating the pulse features containing age related information. Learning curves indicated stable classification performance with increasing sample size. The results demonstrate the potential of using single channel fNIRS in the analysis of aging.

Developing technologies to assess vascular ageing: a roadmap from VascAgeNet

Vascular ageing (vascular ageing) is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.

Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing

Purpose

Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health.

Methods

This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions.

Results

The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention.

Conclusion

This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.

Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning

Introduction

Aortic stiffness plays a critical role in the evolution of cardiovascular diseases, but the assessment requires specialized equipment. Photoplethysmography (PPG) and single-lead electrocardiogram (ECG) are readily available in healthcare and wearable devices. We studied whether a brief PPG registration, alone or in combination with single-lead ECG, could be used to reliably estimate aortic stiffness.

Methods

A proof-of-concept study with simultaneous high-resolution index finger recordings of infrared PPG, single-lead ECG, and finger blood pressure (Finapres) was performed in 33 participants [median age 44 (range 21-66) years, 19 men] and repeated within 2 weeks. Carotid-femoral pulse wave velocity (cfPWV; two-site tonometry with SphygmoCor) was used as a reference. A brachial single-cuff oscillometric device assessed aortic pulse wave velocity (aoPWV; Arteriograph) for further comparisons. We extracted 136 established PPG waveform features and engineered 13 new with improved coupling to the finger blood pressure curve. Height-normalized pulse arrival time (NPAT) was derived using ECG. Machine learning methods were used to develop prediction models.

Results

The best PPG-based models predicted cfPWV and aoPWV well (root-mean-square errors of 0.70 and 0.52 m/s, respectively), with minor improvements by adding NPAT. Repeatability and agreement were on par with the reference equipment. A new PPG feature, an amplitude ratio from the early phase of the waveform, was most important in modelling, showing strong correlations with cfPWV and aoPWV (r = -0.81 and -0.75, respectively, both P < 0.001).

Conclusion

Using new features and machine learning methods, a brief finger PPG registration can estimate aortic stiffness without requiring additional information on age, anthropometry, or blood pressure. Repeatability and agreement were comparable to those obtained using non-invasive reference equipment. Provided further validation, this readily available simple method could improve cardiovascular risk evaluation, treatment, and prognosis.

Validation of an early vascular aging construct model for comprehensive cardiovascular risk assessment using external risk indicators for improved clinical utility: data from the EVasCu study

BACKGROUND

Cardiovascular diseases (CVDs) remain a major global health concern, necessitating advanced risk assessment beyond traditional factors. Early vascular aging (EVA), characterized by accelerated vascular changes, has gained importance in cardiovascular risk assessment.

METHODS

The EVasCu study in Spain examined 390 healthy participants using noninvasive measurements. A construct of four variables (Pulse Pressure, Pulse Wave Velocity, Glycated Hemoglobin, Advanced Glycation End Products) was used for clustering. K-means clustering with principal component analysis revealed two clusters, healthy vascular aging (HVA) and early vascular aging (EVA). External validation variables included sociodemographic, adiposity, glycemic, inflammatory, lipid profile, vascular, and blood pressure factors.

RESULTS

EVA cluster participants were older and exhibited higher adiposity, poorer glycemic control, dyslipidemia, altered vascular properties, and higher blood pressure. Significant differences were observed for age, smoking status, body mass index, waist circumference, fat percentage, glucose, insulin, C-reactive protein, diabetes prevalence, lipid profiles, arterial stiffness, and blood pressure levels. These findings demonstrate the association between traditional cardiovascular risk factors and EVA.

CONCLUSIONS

This study validates a clustering model for EVA and highlights its association with established risk factors. EVA assessment can be integrated into clinical practice, allowing early intervention and personalized cardiovascular risk management.

2024 Recommendations for Validation of Noninvasive Arterial Pulse Wave Velocity Measurement Devices

BACKGROUND

Arterial stiffness, as measured by arterial pulse wave velocity (PWV), is an established biomarker for cardiovascular risk and target-organ damage in individuals with hypertension. With the emergence of new devices for assessing PWV, it has become evident that some of these devices yield results that display significant discrepancies compared with previous devices. This discrepancy underscores the importance of comprehensive validation procedures and the need for international recommendations.

METHODS

A stepwise approach utilizing the modified Delphi technique, with the involvement of key scientific societies dedicated to arterial stiffness research worldwide, was adopted to formulate, through a multidisciplinary vision, a shared approach to the validation of noninvasive arterial PWV measurement devices.

RESULTS

A set of recommendations has been developed, which aim to provide guidance to clinicians, researchers, and device manufacturers regarding the validation of new PWV measurement devices. The intention behind these recommendations is to ensure that the validation process can be conducted in a rigorous and consistent manner and to promote standardization and harmonization among PWV devices, thereby facilitating their widespread adoption in clinical practice.

CONCLUSIONS

It is hoped that these recommendations will encourage both users and developers of PWV measurement devices to critically evaluate and validate their technologies, ultimately leading to improved consistency and comparability of results. This, in turn, will enhance the clinical utility of PWV as a valuable tool for assessing arterial stiffness and informing cardiovascular risk stratification and management in individuals with hypertension.

Reimagining the Value of Brachial-Ankle Pulse Wave Velocity as a Biomarker of Cardiovascular Disease Risk-A Call to Action on Behalf of VascAgeNet

This review critiques the literature supporting clinical assessment and management of cardiovascular disease and cardiovascular disease risk stratification with brachial-ankle pulse wave velocity (baPWV). First, we outline what baPWV actually measures-arterial stiffness of both large central elastic arteries and medium-sized muscular peripheral arteries of the lower limb. Second, we argue that baPWV is not a surrogate for carotid-femoral pulse wave velocity. While both measures are dependent on the properties of the aorta, baPWV is also strongly dependent on the muscular arteries of the lower extremities. Increased lower-extremity arterial stiffness amplifies and hastens wave reflections at the level of the aorta, widens pulse pressure, increases afterload, and reduces coronary perfusion. Third, we used an established evaluation framework to identify the value of baPWV as an independent vascular biomarker. There is sufficient evidence to support (1) proof of concept; (2) prospective validation; (3) incremental value; and (4) clinical utility. However, there is limited or no evidence to support (5) clinical outcomes; (6) cost-effectiveness; (8) methodological consensus; or (9) reference values. Fourth, we address future research requirements. The majority of the evaluation criteria, (1) proof of concept, (2) prospective validation, (3) incremental value, (4) clinical utility and (9) reference values, can be supported using existing cohort datasets, whereas the (5) clinical outcomes and (6) cost-effectiveness criteria require prospective investigation. The (8) methodological consensus criteria will require an expert consensus statement. Finally, we finish this review by providing an example of a future clinical practice model.

Expert Consensus on the Clinical Use of Pulse Wave Velocity in Asia

Arterial stiffness is a progressive aging process that predicts cardiovascular disease. Pulse wave velocity (PWV) has emerged as a noninvasive, valid, and reliable measure of arterial stiffness and an independent risk predictor for adverse outcomes. However, up to now, PWV measurement has mostly been used as a tool for risk prediction and has not been widely used in clinical practice. This consensus paper aims to discuss multiple PWV measurements currently available in Asia and to provide evidence-based assessment together with recommendations on the clinical use of PWV. For the methodology, PWV measurement including the central elastic artery is essential and measurements including both the central elastic and peripheral muscular arteries, such as brachial-ankle PWV and cardio-ankle vascular index, can be a good alternative. As Asian populations are rapidly aging, timely detection and intervention of "early vascular aging" in terms of abnormally high PWV values are recommended. More evidence is needed to determine if a PWV-guided therapeutic approach will be beneficial to the prevention of cardiovascular diseases beyond current strategies. Large-scale randomized controlled intervention studies are needed to guide clinicians.