Toward Personalized Heart Failure Management: Integrating Biomarkers and Multimodal Monitoring Strategies

Abstract

Purpose of Review

Effective heart failure (HF) management requires timely recognition and treatment of congestion in both inpatient and outpatient settings. As the complexity of HF care increases, there is a growing interest in improved guided therapy strategies, including both biomarker-driven approaches and non-invasive and invasive remote monitoring.

Recent Findings

While natriuretic peptides remain the cornerstone of biomarker-based prognostication, emerging biomarkers reflecting diverse pathophysiological processes, such as myocardial stress, fibrosis, and inflammation, offer potential for enhanced risk stratification, particularly when used in serial multimarker panels. Despite their prognostic value, biomarkers are not yet routinely employed in guided therapy. Additionally with the increasing availability hemodynamic remote monitoring strategies and devices, we could learn more about pathophysiological processes, multi-biomarker panels and their relation to congestion and risks for progression of HF which can help to further refine biomarker-guided therapy.

Summary

This review evaluates the potential use of biomarkers in guided therapy for HF and explores the rationale for their integration into multimodal monitoring frameworks aiming for a more personalized and dynamic model of HF care, in which different monitoring approaches can reinforce each other.

Introduction

The complexity of heart failure (HF) pathophysiology calls for a shift from standardized therapy approaches to more personalized management strategies that account for individual disease trajectories [1, 2]. In recent years, biomarker-guided therapy has been proposed as a promising strategy for improving HF management [3–5]. Biomarkers provide valuable insights into pathophysiological processes, including myocardial stress, fibrosis, inflammation, and fluid overload (Fig. 1). Moreover, circulating biomarkers play an important role in prognostication [6, 7]. While natriuretic peptides, such as NT-proBNP, are well established in clinical practice for diagnosing and predicting HF outcomes, therapy guided solely by NT-proBNP yielded inconsistent results [8]. However, recent advances in multimodal monitoring, integrating biomarkers with hemodynamic data, have emerged as potentially novel strategies to enhance HF management and address the limitations of traditional previous approaches [9].

Fig. 1.

Pathophysiological pathways and biomarkers in heart failure

Recent studies suggest that integrating biomarker panels with multiple biomarkers enhances both sensitivity and specificity for HF prognostication [10]. Moreover, repeated measurement of biomarkers has been shown to provide a more reliable and accurate assessment of pathophysiological processes and prognosis in HF patients [11]. While biomarkers alone have shown promise, their combination with real-time telemonitoring modalities has the potential to enhance pathophysiological understanding and early detection of decompensation and guide timely interventions. In the future, this approach could possibly enable a more comprehensive assessment of changes in biological characteristics through biomarker profiling [12].In recent years, there have been advances in the availability of invasive and non-invasive remote monitor sensors related to hemodynamics. The hemodynamic data revealed by these techniques could be combined with blood-biomarkers to gain more insight in their patterns and pathophysiological relationships with congestion to refine and improve biomarker-guided therapy strategies [13].

Blood-biomarkers remain relatively underutilized in the context of remote cardiac monitoring strategies for patients with HF, especially when compared to widely adopted non-invasive modalities such as various telemonitoring approaches [13]. A more comprehensive understanding of the interplay between circulating biomarker profiles and hemodynamic parameters could pave the way for more precise and individualized therapeutic interventions, ultimately enhancing broader patient outcomes in HF management.

This review provides an overview of the role of biomarkers in the diagnosis, risk stratification and management of HF, with a focus on two emerging themes.

To this end, we explore various monitoring strategies. We first discuss the requirements for the ideal blood biomarker, and which biomarkers are already known in the context of congestion in heart failure, and what insights biomarker-guided therapy studies have provided us so far. We then discuss recent insights into non-invasive and invasive monitoring strategies of heart failure patients that could enrich the field of biomarker guided therapy. Finally, we will discuss future perspectives, in which we will explore how the integration of biomarker data with remote monitoring measurements, including the application of Artificial Intelligence (AI) and implantable sensors, may offer new opportunities to improve pathophysiological insights, disease monitoring and therapeutic decision-making.

Criteria for Clinically Useful Biomarkers in Heart Failure

Biomarkers play an important role in the diagnosis, risk stratification and management of HF. However, their clinical utility depends on several criteria. An ideal biomarker must demonstrate biological validity, with a clear link to HF pathophysiology, and must be reproducible across various settings [8]. It should also provide prognostic and therapeutic value offering reliable insights into disease progression and support clinical decision-making. Additionally, biomarkers must be clinical applicable and interpretable, easy to measure, and cost-effective for routine use.

Biological Rationality

Biomarker linked to critical processes such as myocardial stress, myocardial remodeling, inflammation and congestion provide valuable insights into disease severity and progression [14, 15]. For instance, biomarkers like NT-proBNP and ST2, which are associated with myocardial stress and congestion provide meaningful prognostic value. When combined with fluid status or hemodynamic parameters, such as weight, blood pressure, and other signs of fluid status, these biomarkers enable real-time disease monitoring and dynamic tracking of disease progression. This integration is important for both diagnostic and therapeutic guidance, offering a more holistic understanding of HF.

Reproducibility and Reliability

For a biomarker to be integrated into clinical practice, it must provide consistent and reliable results across various settings and patient populations [14, 15]. Variability caused by comorbidities, physiological changes, or differences in assays can weaken its clinical utility.

Prognostic and Therapeutic Value

Within the context of congestion and clinical deterioration in HF, biomarkers should offer valuable prognostic insights and help guide therapeutic decisions [14, 15]. Biomarkers related to congestion (e.g., ST2, CA125, bio-ADM) have been shown to predict clinical outcomes and guide treatment decisions by indicating the severity of fluid overload and cardiac dysfunction.

Clinical Applicability and Timing in the Disease Process

Even if a biomarker demonstrates biological validity, reliability, and prognostic value, its widespread use depends on its feasibility for routine clinical practice [8, 15]. A clinically applicable biomarker must be easy to measure, cost-effective, and accessible in different healthcare settings.

An important factor in this regard is the timing of biomarker application throughout the disease process. In the acute phase, biomarkers such as NT-proBNP rise rapidly during clinical deterioration (Fig. 2) [16]. After initiation of therapy, biomarkers peak and then decline. In chronic HF, biomarkers typically remain stable or rise slightly over time, until a clinical event occurs [17]. Established biomarkers, such as natriuretic peptides, are the standard for clinical use, while emerging markers are still being evaluated [7].

Fig. 2.

The dynamic change of biomarker values over time. Legend: The blue line indicates the course of clinically useful biomarkers, such as NT-proBNP, in the acute setting. The orange line indicates this biomarker-level course in a chronic setting. The dashed line indicates the upper limit of the biomarker’s normal value. In the acute setting, there is a rapid clinical deterioration of HF accompanied by a rapid rise in biomarker level. After timely initiation of therapy, this biomarker reaches a maximum, after which it drops again and stabilizes. In a chronic setting, both the patient’s clinical features and biomarker values remain stable for an extended period of time, although biomarker levels may rise slowly leading up to a clinical event

A significant disadvantage of multiple prognostically relevant congestion biomarkers is that they are not yet clinically applicable. In the future, broader clinical availability of these markers would offer the possibility of developing a prognostically relevant biomarker panel. It should be integrated into standardized care pathways to become a regular part of patient management. Ideally, one or more biomarkers could be measured in a very accessible manner, such as via a point-of-care method in the home setting [18–20]. This would make biomarker-driven care more practical, accessible, and faster to implement.

Biomarkers and their Clinical Relevance in Heart Failure

Congestion-related biomarkers are particularly important in HF prognostication and disease management, as fluid overload is a driving factor in HF progression and decompensation. Traditional markers such as NT-proBNP reflect myocardial stretch and volume overload, while newer biomarkers offer insights into processes such as inflammation, endothelial dysfunction, and cardiac and extracellular matrix remodeling (Fig. 1) [6, 7]. Moreover, several biomarkers are known to be associated with pulmonary artery pressures and clinical congestion [6, 9]. In the following sections, both conventional and newer emerging biomarkers are discussed, with a focus on their prognostic significance in HF.

Biomarkers of Myocardial Stress and Cardiac Remodeling

Myocardial stress results from increased wall tension due to pressure or volume overload, while cardiac remodeling involves structural and functional changes, including fibrosis and ventricular dilation. Among the biomarkers that reflect these processes, NT-proBNP and ST2 are the most established [6].

NT-proBNP is released in response to ventricular wall stress linked to elevated filling pressures. Increased levels of NT-proBNP correlate with worsening congestion symptoms, increased risk of hospitalization and mortality, making it an useful biomarker for assessing volume status and guiding decongestive therapy [23].

ST2, a member of the interleukin-1 receptor family, is mainly associated with cardiac remodeling, fibrosis and inflammatory processes [6, 24]. Elevated levels of ST2 have been correlated with poorer outcomes in HF, and when combined with NT-proBNP, they enhance prognostic accuracy.

BioADM, an active form of adrenomedullin, plays an important role in regulating vascular tone and maintaining endothelial function. In HF, elevated Bio-ADM levels correlate with clinical congestion and hemodynamic stress, such as increased pulmonary capillary wedge pressure. It has shown potential as a biomarker for assessing congestion and guiding therapy, particularly in diuretic treatment [6, 25–29]. However, further research is needed to clarify its long-term value in monitoring congestion and influencing clinical outcomes.

Biomarkers of Extracellular Matrix (ECM) Remodeling and Fibrosis

ECM remodeling and fibrosis are important processes in HF, as they contribute to impaired cardiac function and exacerbate fluid accumulation, leading to congestion [24, 30]. ECM remodeling involves changes in the composition of the ECM, which can affect the heart’s structure and its ability to function properly. Fibrosis is the excessive buildup of fibrous tissue, which can impair normal heart function and lead to higher filling pressures. In this context, we explore three important biomarkers: MMP-2 and IGFBP-7.

MMP-2 (Matrix metalloproteinase-2) is essential for breaking down the ECM and supporting the remodeling process [31]. Higher levels of MMP-2 have been linked to the development of myocardial fibrosis, which can lead to stiffness in the heart and difficulty with filling during the diastolic phase. Increased MMP-2 activity is associated with worse outcomes in HF patients [32, 33].

IGFBP-7 (Insulin-like growth factor-binding protein 7) plays a role in regulating insulin-like growth factors, which are important for cell growth and survival [24]. In HF, IGFBP-7 is associated with fibrotic changes in the heart muscle. Increased levels of IGFBP-7 have been linked to increased cardiac fibrosis, a higher risk of incidence HF and poorer outcomes in HF patients [10, 34, 35].

Biomarkers of Inflammation and Immune Activation

Inflammation and activation of the immune system play an important pathophysiological role in the progression of HF. An exaggerated inflammatory response can lead to adverse cardiac changes and exacerbate fluid overload. Monitoring specific biomarkers associated with inflammation and immune activation could provide valuable insights into these processes and aid in assessing the severity of congestion in HF. In this context, CA125, GDF-15, CXCL16, U-PAR, and PSP-D are discussed in this section.

CA125 (mucin 16), is a glycoprotein primarily associated with volume overload conditions, including HF. Elevated CA125 levels correlate with tissue congestion, increased cardiac filling pressures, and right-sided HF, particularly in the presence of pleural effusion and peripheral edema [36]. Moreover, CA125 levels are significantly associated with HF disease severity, and its trajectory over time can predict long-term mortality and readmission in HF patients. CA125 has been suggested as a tool for guiding diuretic therapy, with studies showing that CA125-guided adjustments in diuretic treatment can improve outcomes. The biomarker is not influenced by age, kidney function, or left ventricular status, making it valuable for assessing congestion in a variety of HF phenotypes, including HFpEF [6, 36–40].

GDF-15 (growth differentiation factor 15) is a cytokine linked to stress and inflammation [41]. Elevated levels of GDF-15 are associated with worsening cardiac function and increased filling pressures.

Upcoming biomarkers like CXCL16, U-PAR, and PSP-D are linked to congestion-related processes, such as inflammation and vascular dysfunction. These could offer a better understanding of HF pathophysiology and management of fluid overload in HF. CXCL16 is a chemokine involved in immune cell recruitment to areas of inflammation. Elevated CXCL16 levels in HF are associated with increased inflammation, myocardial injury, and worse clinical outcomes, highlighting its potential role in fluid overload and broader vascular inflammation [42–44]. U-PAR (urokinase-type plasminogen activator receptor) regulates immune responses and tissue remodeling, with elevated levels in HF linked to increased inflammation, fibrosis, and fluid retention [45]. PSP-D (pulmonary surfactant-associated protein D) plays a role in regulating immune responses and maintaining vascular homeostasis. Elevated PSP-D levels in HF patients are associated with inflammation, endothelial dysfunction, and hemodynamic compromise, suggesting its potential as a marker for both inflammation and fluid overload [46–48].

Biomarkers of Vascular Function and Endothelial Dysfunction

Vascular function and the health of the endothelium are important aspects in understanding HF [6]. Endothelium dysfunction can lead to issues such as reduced blood vessel dilation, increased permeability of the blood vessel, and inflammation, worsening the overall hemodynamic status of patients with HF. In this context, lesser known biomarkers like (s)CD146, Notch 3 and PSP-D are recognized for their potential roles in endothelial dysfunction and vascular remodeling. These biomarkers could be of interest because they may provide additional insight into the pathophysiological processes that drive vascular alterations in HF. In this section, we will explore three important biomarkers: (s)CD146, Notch 3, and PSP-D and their roles in vascular function and endothelial dysfunction within the context of HF.

CD146, is a glycoprotein, of which the soluble form of CD146 (sCD146) is overexpressed in conditions involving inflammation, vascular injury, and endothelial dysfunction,. Elevated sCD146 levels have been associated with peripheral edema and venous congestion, suggesting its potential as a biomarker for congestion in HF. Although the evidence supporting its clinical utility is limited, preliminary studies indicate that sCD146 could help differentiate between central and peripheral congestion [6, 49, 50].

Notch 3 (neurogenic locus notch homologue protein 3) plays an important role in endothelial cell signaling and vascular remodeling, both essential for maintaining vascular homeostasis [51]. Dysregulation of Notch 3 signaling has been implicated in various cardiovascular diseases, including HF, where it may contribute to endothelial dysfunction and adverse vascular remodeling. While studies linking Notch 3 to HF are scarce, alterations in Notch 3 levels appear to correlate with the severity of HF, suggesting its potential as a biomarker for monitoring vascular health and congestion [52].

PSP-D, as discussed in Section II.c, also modulates immune responses affecting endothelial function [45].

Although many of the biomarkers discussed above show promise in explaining pathophysiological processes, predicting clinical deterioration and clinical events and possibly also in assisting disease management in heart failure patients, most of these markers are not yet available or regularly used in clinical practice. Further research is still needed to fully validate their clinical usefulness in monitoring congestion and tailoring therapy. In addition, research into integration with other monitoring modalities is useful, since different monitoring strategies may reinforce each other in the context of congestion at HF.

Biomarker-Guided Therapy in Acute vs. Chronic Heart Failure: Current Insights and Lessons Learned

HF can present as either acute heart failure (AHF) or chronic heart failure (CHF). Both require different management strategies. The use of biomarkers in prognostication and therapy has become an important tool for optimizing care, by enabling early diagnosis, risk stratification, and treatment monitoring [53]. However, the clinical application of biomarkers differs between these patient groups due to their pathophysiological characteristics and treatment goals [54]. In AHF, biomarkers could support rapid decision-making, guiding interventions at admission and discharge [55]. In CHF, serial biomarker measurements allow for long-term monitoring, helping to assess disease progression and adjust therapy accordingly [4]. In this section, we discuss the use of biomarkers in different types of HF and what we can learn about this from previous studies.

Biomarker-Guided Therapy in Acute and Chronic Heart Failure

The use of biomarkers for risk stratification and guiding interventions differs between AHF and CHF. AHF is characterized by rapid onset of or deterioration of symptoms, often with clinical decompensation due to congestion, volume overload or myocardial injury [6]. The primary aim in managing AHF is to achieve clinical stabilization and prevent deterioration or hospitalization. In this context, biomarker assessment helps clinicians to quickly identify high-risk patients, guide intensive inpatient management, and structure follow-up post-discharge [56]. In CHF, the main focus is on long-term disease monitoring and therapy optimization [54]. Unlike AHF, where biomarker assessment is primarily used during hospitalization, CHF management benefits from serial biomarker measurements that provide insights into disease progression and treatment response over time [10, 11, 47]. This allows healthcare providers to detect subtle changes that may indicate early decompensation, possibly enabling early intervention and preventing clinical decline.

Natriuretic peptides such as B-type natriuretic peptide (BNP) and NT-proBNP are well-established biomarkers in both AHF and CHF [6, 7]. Persisting elevated NT-proBNP levels at discharge from hospitalization for decompensated HF are associated with a higher risk of early readmission and mortality [57]. Serial measurements during hospitalization allow for treatment monitoring, guiding decisions on diuretics, vasodilators, and inotropic therapy. In CHF management, trends in NT-proBNP levels serve as indicators of therapy effectiveness or disease progression [5, 58–60]. A decline in NT-proBNP levels reflects effective treatment and stabilization of HF, while persistent elevation of NT-proBNP levels, despite optimized treatment, may indicate insufficient decongestion, ongoing myocardial stress or comorbidities complicating management.

Although well-established as prognostic markers, the additional benefit of a biomarker-guided treatment strategy based on natriuretic peptides has not been conclusively demonstrated in clinical practice. While natriuretic peptide-guided therapy has shown promise, clinical trials such as GUIDE-IT, PRIMA II, PROTECT, TIME-CHF and BATTLE-SCARRED have yielded mixed results (Table 1) [61–63]. GUIDE-IT found that NT-proBNP-guided therapy in HFrEF did not significantly improve outcomes compared to standard care, despite achieving greater NT-proBNP decrease [61]. Similarly, PRIMA II showed no significant benefit in mortality or readmissions for patients with acute decompensated HF, although more patients in the biomarker-guided group achieved a ≥ 30% NT-proBNP decline [62]. The PROTECT trial demonstrated that NT-proBNP-guided therapy could lead to better volume management and a reduction in cardiovascular events [63]. The TIME-CHF trial, conducted in older patients with chronic HF, showed that NT-proBNP-guided therapy improved outcomes in patients younger than 75 years, but not in the older subgroup, implying the impact of age on treatment effect [64]. Likewise, the BATTLE-SCARRED study found that biomarker-guided therapy was associated with reduced long-term mortality in patients under 75 years of age, but not in the overall cohort [65]. Moreover, several meta-analyses, which also included several smaller cohorts in addition to the studies mentioned above, have yielded conflicting or inconclusive evidence regarding natriuretic peptide-guided therapy in HF, although differences appear to exist across age groups and ejection fraction categories [4, 55].

Table 1.

Overview of randomized clinical trials on NT-proBNP guided therapy in heart failure

Trial	Population	Sample size (n)	Follow-up duration	Intervention	Comparator	Primary outcome	Results	Effect size, HR Ratio	P Value	Subgroup analysis	Conclusions
BATTLE-SCARRED (2006)	CHF, < 75 years	364	3 Years	NT-proBNP-guided therapy	Clinical care	3-year mortality	Reduced mortality in patients < 75 years	HR 0.75	P = 0.02	Younger patients, no benefit in > 75 years	Benefit mainly in younger patients, limited generalizability
PROTECT (2008)	CHF, outpatient setting	1110	2 Years	NT-proBNP ≤ 1000 pg/ml	Standard of care	HF events, EF, ventricular size	Fewer HF events, improved EF and ventricular size	HR 0.80	P = 0.04	Subgroup with lower baseline EF showed better response	Positive effects, selective population, relatively small study
TIME-CHF (2008)	CHF, age groups < 75 vs. ≥ 75	1003	3 Years	NT-proBNP-guided therapy	Clinical care	Composite mortality and hospitalizations	No overall benefit, better results in < 75 years subgroup	HR 0.85* * For < 75 years	P = 0.25 (Overall)	Better results in patients < 75 years	Age modifies effect, no overall benefit
PRIMA II (2018)	Acute decompensated HF, HFrEF	1234	6 Months	≥ 30% NT-proBNP reduction during admission	Standard of care	Mortality and HF re-admissions within 6 months	No significant difference in primary outcome, but more patients achieved ≥ 30% NT-proBNP reduction	HR 0.98	P = 0.56	No effect in subgroups	Biomarker reduction does not translate into clinical benefit in acute setting
GUIDE-IT (2018)	Chronic HFrEF, high-risk	1600	3 Years	NT-proBNP < 1000 pg/ml	Standard of care	CV death or HF hospitalization	No significant difference in primary outcome; greater NT-proBNP reduction in intervention group	HR 0.98	P = 0.88	Better NT-proBNP reduction but no benefit in outcomes	Mixed results; guided therapy did not improve hard outcomes despite NT-proBNP reduction

Although the concept of biomarker-guided therapy appears promising, current evidence does not sufficiently support the routine measurement of BNP or NT-proBNP for therapy titration to warrant formal recommendation in clinical guidelines [54]. This implies that there may be a gap in knowledge regarding the application and approach of biomarker-guided therapy, particularly the overreliance on single biomarkers, to guide treatment decisions. Biomarkers like NT-proBNP have proven useful in assessing cardiac stress and fluid overload, as indicators of prognosis, but fail to capture the full complexity of HF pathophysiology. In the future, a congestion panel could potentially be an appropriate solution to this challenge. In this context, the composition of a multi-biomarker panel, including for example ST2, BioADM, and CA125, could provide a more comprehensive view of underlying pathophysiological mechanisms that contribute to the development of congestion and clinical deterioration [6]. Future research should focus on integrating these biomarkers into a comprehensive framework that provides better insight into HF progression. A summary of advantages and disadvantages of a multimarker approach compared with a single biomarker measurement is shown in Table 2.

Table 2.

Advantages and disadvantages of a multimarker approach compared to a single biomarker measurement

	Single biomarker measurement	Multi-biomarker measurements
Pros	Simple interpretation and integration into clinical workflow. Established evidence (e.g. NT-proBNP and BNP are well-validated for diagnosis and prognostication). Lower laboratory and resource costs.	Captures diverse HF pathophysiology (e.g. myocardial injury, fibrosis, inflammation, etc.) Improved prognostic accuracy and risk stratification.Potential for personalized care: tailored therapy based on individual risk profiles.
Cons	Limited pathophysiological insights. Affected by confounders (e.g., renal function, BMI), resulting in potentially missed risk stratification in case of other active pathological processes. Ceiling effect: potential prognostic power plateaus, especially in case of complex comorbidities.	Complex interpretation requiring advanced analytics and/or interpretation algorithms. Higher cost due to increased laboratory expenses and resources. Potential redundancy: due to high correlation of multiple biomarkers, the added effect might be small. Unclear clinical utility: no universal or widely applicable multi-marker strategy is available for clinical use yet.

In addition to traditional outcomes such as hospitalization and mortality, broader endpoints such as hemodynamic parameters, symptom burden or quality of life could be explored. These outcomes could provide clinicians with a more nuanced view of a patient’s condition. Furthermore, it is important that relevant biomarkers become clinically available and can be easily obtained, for example in a point-of-care or hospital-at-home situation. In addition, it is likely that biomarker-based guidance approaches can be enhanced by integrating other measurements and modalities, including remote monitoring strategies using AI, telemonitoring, or invasive measurements. This warrants further investigation in the future. By addressing the gaps in the current framework, risk stratification and the initiation of timely interventions could be improved, thus improving HF management and patient outcomes.

Biomarker Guided Therapy in HF with Reduced and Preserved Ejection Fraction (HFrEF vs. HFpEF)

Distinct biomarkers have been associated with prognosis and clinical deterioration in heart failure with reduced ejection fraction (HFrEF) compared to heart failure with preserved ejection fraction (HFpEF). These differences must be considered when selecting biomarkers for studies aimed at developing biomarker-guided therapy strategies. Tailoring biomarker selection to the specific HF phenotype may improve the precision and effectiveness of such approaches. In HFrEF, biomarkers like NT-proBNP and troponins are more reliable indicators of cardiac stress and myocyte injury compared to HFpEF [7]. In contrast, pathophysiology in HFpEF is often heterogeneous, and can be influenced by comorbidities such as chronic kidney disease, obesity, inflammatory diseases, and atrial fibrillation, which complicates biomarker interpretation [41, 66]. Natriuretic peptides, which are highly reliable in HFrEF, have less predictive value in HFpEF due to factors like obesity and renal impairment. Emerging biomarkers linked to inflammation and ECM remodeling, such as GDF-15 and galectin-3, may offer new insights into HFpEF pathophysiology and prognostication. Studies suggest that GDF-15 is associated with increased risk of incident HFpEF, reflecting pathways of inflammation [41].

A multimarker approach, combining biomarkers, may enhance risk stratification and therapeutic decision-making especially in HFpEF, since it could account for the complexity of this syndrome.

Non-invasive Monitoring Techniques and their Additive Role To Biomarkers in Prognostication and Guided Therapy

As the role of biomarkers in monitoring HF progression has grown. Telemonitoring modalities, which track patient data remotely, offer a complementary approach from a different angle. We believe that integrating these two strategies could enhance patient care and management in HF. Non-invasive monitoring techniques have become important in HF management, enabling continuous tracking of patient status without the need for invasive procedures with associated risks. These technologies, including wearable devices, sensor-based systems, and telemonitoring platforms allow clinicians to monitor physiological parameters in real-time [67, 68].

Recent research, including meta-analyses highlight the growing promise of non-invasive monitoring systems. Wearable devices, such as ECG patches and blood pressure monitors, enable real-time tracking of parameters like heart rate, ECG, and oxygen saturation in HF patients. This continuous monitoring allows early detection of worsening symptoms and facilitates timely, data-driven interventions. However, proof for a general positive effect on clinical outcomes is still limited due to the heterogeneity among applied modalities and intervention strategies [67].

AI-driven algorithms are becoming increasingly important in optimizing noninvasive monitoring approaches. These algorithms could process large datasets from wearable devices, biomarkers, and telemonitoring platforms to facilitate automated risk stratification and early detection of decompensation [68, 69]. Research is ongoing to refine these models, aiming to improve their predictive accuracy and integrate them seamlessly into clinical practice. As AI systems become more reliable, they hold great potential to enhance the understanding of HF as well as to personalize treatment by providing real-time, actionable insights that guide clinicians in making informed decisions In addition, studies have demonstrated that combinations of non-invasive measurements and biomarkers, based on AI-algorithms, can provide accurate prognoses [70]. Future research will have to show whether multimodal prognostic models, potentially developed with the assistance of AI, can indeed improve the prognosis and management of HF patients.

Invasive Monitoring Techniques in Heart Failure

Invasive hemodynamic monitoring plays an increasing role in the management of HF. These technologies require the implantation of sensors to directly measure hemodynamic variables such as pulmonary artery pressure (PAP), and left atrial pressure (LAP), all of which are important for assessing the filling pressures and severity of congestion [71, 72].

PAP sensors are among the most widely used invasive management tools. Mean PAP is measured, to assess filling pressures as surrogate marker of congestion. PAP-guided therapy can reduce HF-related hospitalizations and improve patient quality of life by allowing early intervention [13, 72]. LAP sensors, which measure pressures within the left atrium, also show potential in optimizing HF therapy. Complementary technologies such as the IVC collapsibility index and diameter monitoring devices, provide insights into volume status, helping assess venous pressure and detect early signs of fluid overload [73, 74].

The integration of these invasive monitoring systems with biomarkers significantly enhances our understanding of HF pathophysiology as well as the applicability of biomarker guided therapy [72–75]. Serial biomarker measurements, such as NT-proBNP, PSP-D, IGFBP-7, MMP-2 and CD93, have strong correlations with invasive hemodynamic measures like PAP. These biomarkers can provide complementary insights into hemodynamic changes, even for patients without invasive sensors, highlighting the potential for noninvasive biomarker-guided therapy alongside invasive monitoring [9].

Another advancement is the integration of biomarkers with digital health technologies, including wearable devices and implantable sensors. A multiparametric telemonitoring system, for example, combines biomarkers like NT-proBNP with sensor data from a wearable device, enabling the prediction of clinical worsening with a sensitivity of approximately 70% and specificity of 89% [76–78]. This integration highlight how combining biomarkers with real-time hemodynamic data can improve predictive accuracy and decision-making in HF management.

Despite the promising advancements in invasive hemodynamic monitoring, challenges remain in the widespread adoption, including cost and the need for specialized training. Ongoing research is needed to refine these technologies and validate their efficacy across diverse HF populations. Currently, the hemodynamic data provides a novel data on the evolution of worsening heart failure and the response to therapy.

Future Perspectives

The Integration of Biomarkers and Non-invasive or Invasive Monitoring To Boost Knowledge of Pathophysiological Processes in Heart Failure

Biomarker insight can improve from remote monitoring data and vice versa, regarding pathophysiological insights and management strategies. HF is a dynamic condition, and continuous monitoring of both biochemical and hemodynamic changes could be beneficial for optimizing treatment. Traditional approaches often rely on single-point biomarker assessments, which provide valuable but static insights into disease severity. In this context, the adoption of a multiparametric approach, integrating various prognostic and guidance tools, as previously proposed, could be a valuable strategy to address the limitations of single-modality monitoring [2, 6].

Recent studies have demonstrated the benefits of integrating biomarkers with clinical parameters in HF, particularly for guiding early interventions [9]. The GUIDE-IT trial integrated NT-proBNP measurements with clinical data, showing improvements in prognostic accuracy. Although the primary outcome of hospitalization and mortality did not significantly improve, the trial underscored the potential of this combined approach to enhance personalized risk assessments [61].

In particular, biomarkers, such as NT-proBNP, PSP-D, IGFBP-7, and MMP-2, have demonstrated strong correlations with hemodynamic parameters, suggesting their potential as early indicators of congestion and other pathophysiological processes such as myocardial stress, fibrosis and inflammation [6, 7, 9, 14, 47]. These biomarkers can be used to complement traditional hemodynamic monitoring, providing a more comprehensive understanding of the disease progression (Fig. 3). Unlike conventional threshold based strategies, monitoring biomarker trends over time helps distinguish between temporary fluctuations and true disease progression, supporting more personalized treatment strategies [10].

Fig. 3.

Integrated monitoring. An integrated approach to monitoring heart failure patients could provide a more comprehensive pathophysiological understanding of disease stage and risk factors

Furthermore, the potential role of point-of-care biomarker testing is becoming more evident [79]. This would allow patients to monitor relevant biomarkers in the home setting, increasing the feasibility and accessibility of long-term monitoring. Such a development could improve patient adherence to treatment plans, reduce hospital visits and ultimately enhance clinical outcomes by detecting changes earlier.

Despite these promising advancements, challenges remain in the standardization of both biomarker and mPAP-guided therapies. Future research should focus on refining biomarker algorithms, validating findings across diverse HF populations, and exploring non-invasive biomarker panels as complementary tools to hemodynamic monitoring. By integrating AI-driven algorithms with biomarker-guided therapy, researchers can refine risk stratification models and provide more precise insights into optimal treatment pathways for patients. Ultimately, combining serial biomarker measurements with clinical HF-parameters, obtained from noninvasive or invasive monitoring modalities, allows clinicians to gain a deeper understanding of disease progression and responses to therapy. Both fields could be enhanced and enriched by using and integrating the information provided bio markers or telemonitoring modalities.

Integrating Biomarker Guided Therapy in a Multimodal Setting

Biomarker-guided therapy, as an easily accessible, minimally invasive management strategy, can benefit from emerging innovations in digital health, AI, and remote monitoring. These advancements have the potential to enable real-time disease monitoring, and improve early risk detection and personalized disease management. Further contributing to this is the recent development that testing biomarkers such as NT-proBNP at home via point-of-care analysis is becoming increasingly feasible, which lowers the threshold for biomarker analysis [19, 20]. The integration of AI-driven algorithms, home-based biomarker testing, and machine learning applications could enhance the clinical utility of biomarkers [80]. However, challenges related to cost, accessibility, and the need for standardized implementation must be addressed before these technologies can be widely adopted. Future research should focus on developing strategies for home-based testing of biomarkers or biomarker panels, such as serum creatinine or NT-proBNP, similar to the current glucose monitoring methods used in diabetes management. Just as continuous glucose monitoring has revolutionized diabetes care by allowing patients to manage their condition outside the traditional healthcare setting, a similar approach could be applied to HF biomarkers. This would enable patients to monitor their health at home, improving early detection and personalized treatment. Studies like the HABIT trial and the HOME HF study have already demonstrated the feasibility and clinical value of remote BNP monitoring for HF [19, 20]. These studies showed that daily home-based monitoring can track changes in biomarkers over time. Such an approach allows earlier intervention, potentially reducing hospital admissions and improving long-term patient outcomes. Furthermore, home-based biomarker monitoring could expand access to personalized HF management [18–20]. By combining these tests with smartphone apps, clinicians could make data-driven treatment adjustments based on biomarker trends [82], .

A biomarker-based approach in HF management offers several advantages. It is broader available, accessible, easily implemented in various clinical settings, and potentially more cost-effective than invasive monitoring methods. These benefits could be even further enhanced if point-of-care techniques for testing biomarkers, comparable to those used in glucose management for diabetics, were to become available for HF patients. However, limitations such as blood sampling discomfort, delays in obtaining results and within-person variability in biomarker levels must be considered. integrating biomarkers with invasive and non-invasive monitoring strategies could benefit HF pathophysiological understanding, prognostication and disease management, as information obtained by various techniques could complement each other. The integration of AI-driven models are becoming important in refining this multimodal approach. AI models can automate risk stratification, aiding the prediction of deterioration before clinical symptoms appear. Advances based on the integration of these multimodal management techniques, would and allow clinicians to make more informed decisions in real-time. Such advancements could ultimately improve patient outcomes, reduce hospital admissions, and enhance long-term management of heart failure. Ongoing research is necessary to validate these models and ensure their effectiveness across different patient groups, including those without access to remote monitoring.

Conclusion

Future HF management could benefit and further refined from a more personalized and dynamic model that integrates serial biomarker measurements with remote monitoring modalities. The combination of these modalities potentially enhances early detection of congestion and disease progression, thereby supporting more timely and individualized therapeutic interventions. Given the central role of congestion in HF outcomes, its targeted assessment through multimodal monitoring congestion related biomarker strategies appears promising and could benefit broader populations.

Author Contributions

All authors contributed to the conception and design of this review. The literature search was performed by YA and MBL and the draft and revision of this work was performed by all authors.

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors did not receive support from any organization for the submitted work.J.J.B. reports an independent research grant for ISS (Abbott) paid to the Institute and has had invited speaker engagements or advisory boards with Astra Zeneca, Abbott, Bayer, Boehringer, Novartis, Vifor and Zoll in the past 5 years. The other authors have nothing to disclose.