Transitioning from usual care to biomarker-based personalized and precision medicine in heart failure: call for action

“It is more relevant to know which person has the disease than to know which disease the person has” Hippocrates (460-370 BC) Successful treatment of patients with cardiovascular disease (CVD) has markedly improved through the deployment of new therapies typically evaluated in large randomized controlled trials to provide evidence-based guidance. While this approach has reduced morbidity and mortality, the total burden of CVD maintains an upward trajectory as the population ages and developing countries adopt a Western lifestyle. Further, patients who now frequently survive an acute cardiac event often subsequently require long-term treatment for chronic conditions (e.g. chronic heart failure [HF]). Chronic heart failure trials have led to several successes, but also to at least as many failures. In HF with reduced ejection fraction (HFrEF) the many ‘failed trials’ suggest it is likely better targeting of enrolled patient populations to fit the mechanism of action of many potential therapeutic agents could have led to success. Moxinidine, a centrally acting anti-adrenergic agent, could have proven beneficial in MOXCON1 if patients were selected on the basis of high adrenergic drive. Bosentan and other anti-endothelin agents could have proven beneficial if patients with elevated plasma endothelin were selected in the ENABLE2 and other trials. Similarly RECOVER and RENAISSANCE3 could have been positive if enrolling patients selected on the basis of inflammatory biomarkers to be responsive to the anti-inflammatory agent etanercept. The anti-vasopressin agent Tolvaptan, which is effective in patients in patients with hyponatremia, could have been beneficial in the EVEREST trial4 if patients with high vasopressin were selectively enrolled. Many more examples may be listed. Basically, the ‘one-fits-it-all’ and ‘average response therapy’ approaches may no longer be optimal in chronic diseases such as HF, and new approaches focused on individual variability are emerging. It must be recognized that HF is mechanistically diverse. Mechanistic phenotyping is soon to become a reality. There is, therefore, an increasing emphasis on tailoring management of CVD to fit the unique characteristics of the individual patient, and even better, to match the target patient population to the mechanism of action of therapeutic candidates. It is worthwhile to first define concepts to better understand the current state of the art for care of chronic HF, and the road that may follow: Standard practice: We might name it ‘imprecision medicine’. It incorporates assessment of symptoms and observation of physical signs compared between visits. The physician uses expert background, experience and judgment to diagnose and treat. Coupled with an increasing armamentarium of effective medications, this approach has led to great advances in understanding and management of HF over the past three decades, yet morbidity and mortality remain unacceptably high despite optimal application of currently available medical treatments.5 This is, in part, because therapies are not effective in all patients, and considerable gaps exist in real world application of such advances due to deficiencies in diagnosis, and risk stratification coupled to incomplete application of current evidence based therapies. Personalized medicine: This refers to collection and analysis of extensive information and data about a patient, including biomarkers, and beyond. The physician uses this information and data to make a more informed diagnosis and treatment plan. Personalized medicine is an evolution of person-centered care, with partnership between patients/carers and professional caregivers plus the incorporation of biological data (circulating peptide biomarkers, SNPs, miRNAs, etc.) for monitoring and establishing a therapeutic plan. Precision medicine: This approach uses computational network knowledge that aggregates and analyses information from patient cohorts, healthy populations and experimental systems to define disease mechanisms and reach toward therapies that more precisely address the mechanism of HF, for better diagnosis, prevention and/or treatment for each individual. Chronic heart failure specialists today are currently in transition from standard practice to personalized medicine, but this transition has been slow and inefficient reflecting insufficient knowledge and lack of confidence in modern advanced testing as well as medical inertia. This inertia is in part due to entrenched viewpoints regarding application of HF therapies deeply engrained in clinical practice guidelines.5 Development and implementation of biomarker-based personalized medicine in HF offers potential to significantly reduce clinical and financial burdens associated with HF (Figure 1). In current practice, HF specialists rely in anamnesis, physical examination, and data provided by ECG, echocardiography and conventional laboratory analysis for the risk assessment of an individual patient. Patients A and B are reflective of two real-life scenarios. With the data gathered in a ‘current practice’ setting it is impossible to discern the prognosis. The addition of two biomarkers surrogates of two distinct pathophysiological pathways in HF, namely NT-proBNP (reflective of strain) and ST2 (reflective of extracellular matrix remodelling) allowed a distinction of Patient A with a good outcome and Patient B with ominous evolution. Chronic heart failure is emerging as the epidemic of the 21st century. The prevalence of HF is approximately 1–2% of the adult population in developed countries and symptomatic HF affects ≥10% of people >70 years of age;6,7 a substantially greater number of patients with asymptomatic left ventricular dysfunction is suspected. Among people >65 years of age presenting to primary care with breathlessness on exertion, one in six will have unrecognized HF (mainly HF with preserved ejection fraction [HFpEF]).8 The lifetime risk of HF at age 55 years is 33% for men and 28% for women.9 Further, the most recent European data (ESC-HF pilot study) demonstrate that 12-month all-cause mortality rates for hospitalized and stable/ambulatory HF patients were 17 and 7%, respectively, and the 12-month hospitalization rates were 44 and 32%, respectively.10 While optimal medical care for HF with reduced ejection fraction (HFrEF) has been found to reduce morbidity and mortality, it is incorrect to declare victory in this context. Therapies used to treat HFrEF (beta blockers, angiotensin converting enzyme inhibitors, angiotensin receptor blockers, and mineralocorticoid receptor antagonists) are repurposed drugs, initially developed for other diagnoses, and though beneficial, are systematically under-applied in everyday practice. This is in part due to the fact that administering and titrating medications as in clinical trials is more challenging in clinical practice, as well as the fact that real world patients are more complex, older and more fragile. It is in this setting where biomarker-based personalized medicine has the biggest opportunity. For instance, the main driver of renin angiotensin aldosterone system (RAAS) inhibitors under use or under dosing is the excessive concern about hyperkalaemia and worsening renal function and inappropriate monitoring of serum potassium and renal function. Diuretic doses are not guided by an adequate assessment of congestion. It is conceivable that biomarkers may help avoid the very frequent under use and under titration of RAAS inhibitors especially of mineralocorticoid receptor antagonists (MRA), depriving patients from the full benefit of these life-saving agents. Better biomarker assisted monitoring might prevent over-titration of therapies in fragile patients if a response to therapy is detected below the standard target doses. Chronic heart failure doctors currently rely heavily on clinical acumen and imaging techniques (from echocardiography to the more recent MRI and PET), and only rarely request a cardiac biopsy to establish the diagnosis. Companion biomarkers, obtained from simple venipuncture, may emerge in this new era as the liquid biopsy of the heart. The experience gained in HF trials incorporating measurement of circulating biomarkers suggests such markers (e.g. natriuretic peptides) can aid in diagnosis, provide prognostic information useful for monitoring risk, and in certain contexts might be used for titration of therapy. Biomarkers may also aid the identification of apparently healthy people who are at excessive risk for developing cardiac disorders. However, major gaps in care exist between randomized clinical trials and real-world practice.11 As proof of concept, Lupón et al. recently developed a novel HF risk calculator that included multi-markers reflective of different pathophysiological pathways in heart failure (amino-terminal pro-B type natriuretic peptide [NT-proBNP], soluble [s]ST2, and highly sensitive troponin T [hs-TnT]); the Barcelona Bio-HF calculator (BCN Bio-HF calculator; www.bcnbiohfcalculator.org)12 is a web-based calculator that refines risk-stratification and allows for quick and easy interactive calculations of prognosis and life expectancy at the individual patient level (Figure 2). The Barcelona Bio-Heart Failure Calculator is a tool to risk-stratify HF patients taking advantage of biomarkers. The combination of clinical variables, treatment, and biomarkers (NT-proBNP, ST2, an hs-TnT) allows for a 1-, 2-, and 3-years mortality risk individual assessment. Further, it provides life expectancy information of a target patient both with and without the use of biomarkers. In a similar vein, a clinical and biomarker score was recently developed to predict relevant reverse remodelling (R2), the ST2-R2 score, which contains five clinical variables (i.e. non-ischaemic aetiology, left bundle branch block, HF duration, baseline LV ejection fraction, and beta blocker treatment) and a biomarker closely associated with LV remodelling (ST2). This score was recently shown to predict relevant R2 and was internally and externally validated.13 The ST2-R2 score provided proof-of-concept that R2 is a predictable phenomenon, and the addition of the interleukin receptor family member sST2 to several clinical parameters significantly improved accuracy of prediction. Additionally, patients with higher ST2-R2 scores had approximately 80% lower risk of death relative to the patients with the lowest scores.14 These data may be important in the management of HF patients and may influence treatment decision-making, i.e. in patients with a very high probability of significant R2 the clinician might opt for medical treatment first before planning on device implantation, while patients with low probability of R2 might be treated more aggressively. A number of limitations of multi-marker panels should be acknowledged. These include potential multiplexing and analytical challenges in assaying multiple markers at once as well as the challenges of interpretation for the clinician due to different cut-offs for each of the separate markers. Nevertheless, it can be anticipated that scoring calculators and algorithms will increasingly use circulating biomarkers in combination with clinical variables to allow appropriate surveillance and fully informed counselling of HF patients, their families and other stakeholders in the process of patient care. Over the last 15 years, the hypothesis that intensified treatment directed at reducing natriuretic peptide concentrations in addition to meeting guideline mandated standards of care may improve the outcomes of patients with HF has been tested in several prospective clinical trials.15 Individual trial results have varied between inconclusive to positive but serial meta-analyses have consistently reported a mortality benefit from biomarker-guided therapy. Published studies of BNP-guided therapy have not fully resolved the equipoise on this question (https://www.dcri.org/research/news/dcri-news-/guide-it-halted). More data are forthcoming regarding the value of biomarker measurement to ‘guide’ HF care, but such results should be interpreted within the context of the trial execution: it remains less clear if a biomarker tool to improve care will have as much impact in highly skilled academic medical centres vs. primary care. The structure of care and chain of care is so important to evaluate drug titration, taking into consideration side effects and co-existing co-morbidities which may have a greater impact on prognosis than HF per se. Too often the chain of a well-established doctor–patient relationship with good continuity is broken. Skilled academic centres have well-trained HF specialists and generally more time as well as a multidisciplinary team to optimize care of the HF patient permitting a good continuity of drug titration, education and so forth, whereas in primary care the GP tends to act as a ‘one man band’ with little time and limited access to technical resources (i.e. echocardiography). Fortunately, continuity of care is becoming increasingly important and primary care awareness in this regard is quickly growing. As well, present trials have looked at biomarkers as a generic trigger for titration of therapies, rather than a one-marker/one-drug approach. In addition to revisiting the strategy in the event of new effective drugs, such as the angiotensin receptor neprilysin inhibitor sacubitril/valsartan, further studies should examine the potential utility of other markers, such as sST2, either alone or in combination with natriuretic peptides.16 sST2 manifests much less variability than do natriuretic peptides which may be ideal for following changes with treatment.16 However, optimal biomarker targets remain to be defined. Designing trials of biomarker-guided therapy has many challenges. (i) Being almost always unblinded, they are prone to bias. (ii) The control group, most frequently ‘usual care’ group, is open to contamination and has inevitably better outcome than in real non-trial ‘usual care.’ iii) Being per essence ‘strategy-trials’ rather than simple intervention trials, causality is difficult to establish. For instance, it is hard to decide whether benefit is driven by the biomarker-guided change in therapy, or just by more intense and/or more optimal disease management. (iv) Therapy optimisation as a result of change in the tested biomarker may be left to the decision of the investigator, only instructed to follow best guideline medical therapy, or decided per-protocol using more or less sophisticated algorithms, which, although guideline-based, may vary according to the protocol. Most of biomarker-guided (and for instance telemedicine) trials suffer from any one or many of these important design issues. 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