Role of confirmed and potential predictors of an unfavorable outcome in heart failure in everyday clinical practice

Heart failure (HF) is a cardiovascular disease with an ever increasing incidence [1]. In the National Health and Nutrition Examination Survey (NHANES) data in USA, in 6.2 million Americans, HF was diagnosed in the period 2013–2016 compared with 5.7 million in the period 2009–2012 [2]. This disease affects an estimated 26 million people worldwide, including 1–2% of the adult population of developed countries in America and Europe, and as many as 10% in people over 70 years [1‐8]. The prevalence of HF is estimated to be about 20/1000 people, and as high as 130/1000 people for those aged over 65 years [1‐8], which results in more than 1 million hospitalizations annually in both the USA and Europe. About 15 million people suffer from it in the whole of Europe. In Western Europe, there are over 5 million HF patients [1, 3, 4], and in Poland nearly 1 million (about 3% of the population). Another 10 million Poles are at risk of this disease—mainly people with hypertension, coronary artery disease, obesity, diabetes, and smoking cigarettes [9‐14]. In the USA, there are around 5 million HF sufferers. About 400,000 new cases of HF are diagnosed in the USA annually [2, 5]. The number of new cases of HF reported each year in Europe is approximately 2–3/1000. Among the 70–80 age group, 100/1000 people have HF every year [1, 3, 4]. By 2030, the number of HF patients will increase by half [1]. For example, in the USA, the number of HF patients will exceed 8 million people [2, 5]. By the year 2050, a quarter of the population will be older than 65 years of age in developed countries [1]. In 1950, in Europe, the average age of the population was 29.2 years, and by 1998, this had risen to 37.1 years. By 2050, the average age of the population is expected to be 47.7 years, leading to a higher prevalence of HF [15]. HF is the main cause of death worldwide [16]. Annual morbidity of HF in developed countries is 5–10 people per 1000 inhabitants [1, 3, 4]. HF is associated with high consumption of healthcare resources [4, 7]. This results in high costs of care for a patient with HF, which mostly results from repeated hospitalizations [4, 7]. HF, through reduced functional capacity, frequent exacerbations of disease, and repeated hospitalizations, results in poorer quality of life, decreased work productivity, and significantly increased costs of the public health system [16].

The main challenge in the treatment of HF is the availability of reliable prognostic models that would allow patients and doctors to develop realistic expectations about the prognosis and to choose the appropriate therapy and monitoring method. Prognosis assessment plays a special role in patients qualified for implantable device therapy or surgical treatment (including heart transplantation). Prognosis also plays an important role in planning terminal palliative care with the patient and his family. Not only does the predictor allow one to identify a high-risk patient in advance but it also allows one to monitor and implement individual preventive therapy. Secondly, identifying factors that contribute to poor prognosis can help develop new, targeted therapies [17]. This article begins with a review of individual markers that contribute to the risk of unfavorable outcome in HF.

Predictors should be easily obtainable and associated with some therapeutic and clinical results [17].

The 2016 European Society of Cardiology (ESC) guidelines on HF named over 70 predictors in HF patients [8]. A modified list is presented in Table 1.

The 2019 ACC Expert Consensus Decision Pathway on Risk Assessment, Management, and Clinical Trajectory of Patients Hospitalized With Heart Failure also named many predictors of unfavorable outcome during hospitalization in HF patients [18]. A modified list is presented in Table 2.

However, no single risk factor is sufficient to predict prognosis in HF. Results of a few markers must be interpreted together. Still it is important to find the most important and valuable panel of a few predictors and there are still ongoing studies assessing potential new ones.

Knowledge of a patient’s demographic, medical, and clinical data could play an important role in prediction of life expectancy. Previous studies have shown that male sex is more strongly associated with left ventricular systolic dysfunction, but female sex is more strongly associated with preserved left ventricular function [19‐21]. Ischemic etiology and coronary heart disease are strongly correlated with male sex [19‐21]. Pathophysiological mechanisms that could explain sex-related differences can be separated into differences in bio-hormonal system activity (inflammation, oxidative stress, sympathetic nervous system, hormonal system), various cardiovascular risk factors, and various comorbidities (coronary artery disease, atrial fibrillation, hypertension, obesity, and diabetes and/or insulin resistance) [19‐21]. These differences can influence mortality and morbidity differences between genders [21]. In LaMarca et al. study, animal models have shown that the sex-specific mitochondrial adaptation to effort is modulated by the estrogen receptor ERβ [22]. In the failing heart, sexual differences have been identified in the expression of genes involved in energy metabolism. Female pattern involves genes related to energy metabolism and regulation of transcription and translation while the male pattern involves genes related to muscular contraction. Failed female hearts maintain energy metabolism better than male hearts and are better protected against calcium overload. As a result, female sex can be protective against HF mortality [22].

Low socioeconomic status in adulthood and childhood is associated with worsened HF outcomes [23‐25]. Low socioeconomic status in childhood is associated with worse HF risk factors in adulthood, such as smoking [26, 27], high blood pressure [28‐30], obesity [31‐33], and coronary heart disease [34, 35].

In the general population, increased systolic blood pressure (SBP) is associated with unfavorable outcomes and higher risk of development of HF. In the Framingham Heart Study population, 91% of the participants with HF had a previous diagnosis of hypertension [6]. Compared with the normotensive individuals, patients with higher SBP had 2- and 3-fold increased risk of developing HF [2]. However, in patients with heart failure with reduced ejection fraction (HFrEF), high SBP is associated with better outcomes. SBP has a U-shaped association with mortality in patients with 30%≤ LVEF< 50% and a linear association with mortality in patients with LVEF< 30%. As a result, lower SBP is associated with increased mortality in HFrEF patients [36].

In the general population, increased body mass index (BMI) predisposes to development of HF (5% increase in risk for each 1 kg/m² increase in BMI) [37, 38]. In the Mahajan et al. meta-analysis, intentional weight loss in obese patients without HF was associated with a reduction in left atrial size (p = 0.02), a reduction in left ventricular mass index (p < 0.0001), and improvement in left ventricular diastolic function (p ≤ 0.0001) [39]. However, in the HF population, higher BMI is associated with lower risk of worsened outcomes—a 10% reduction in mortality for each 5-unit increase in BMI was observed in the Kenchaiah et al. [40] and Fonarow et al. studies [41]. In the Mahajan et al. meta-analysis, the “obesity paradox” was also observed for all-cause mortality, and for cardiovascular (CV) mortality in the overweight group (OR = 0.86 (95% CI 0.79 to 0.94), n = 11) [42].

The 2016 guidelines of the ESC identified cachexia and sarcopenia as important comorbidities of HF [8]. Cachexia (loss of body weight) develops in the course of disease in the catabolic stage. The cachectic patient may lose any type of tissue, leading to weight loss [43]. Cardiac cachexia has been observed as an independent risk factor of death in patients with HF [44, 45]. Sarcopenia (skeletal muscle wasting) is an important comorbid disease. In the Morley et al. study, sarcopenia was observed in 19.5% of all HFrEF patients [46]. The Bekfani et al. study with heart failure with preserved ejection fraction (HFpEF) patients confirmed a similar prevalence [47]. Reduced lean mass (LM) was independently associated with abnormal cardiorespiratory function and muscle strength, leading to worse prognosis and reduced quality of life in HF patients [44]. A multicenter Italian study identified sarcopenia as an important factor for prolonged hospitalization in HF patients admitted to acute care wards (5.1 days vs. 3.2 days) [48]. Many studies have also demonstrated that the loss of skeletal muscle mass is associated with loss of physical independence and, as a result, with significantly worsened prognosis and an increased risk of death in HF patients [43, 44, 46‐50].

The New York Heart Association (NYHA) functional classification is still useful for assessing syndrome severity, patient’s exercise tolerance and prognosis in HF patients [51, 52]. NYHA functional class correlates with the magnitude of signs of cardiovascular impairment in these patients and has been associated with mortality in HF [51‐54].

A list of other significant values from the medical history and clinical status of the patient is presented in Tables 1 and 2.

Echocardiography provides detailed information regarding cardiac structure and function [8, 17]. HFrEF can be easily diagnosed by echocardiography and is understood as left ventricular ejection fraction (LVEF) < 40% [8]. Diagnostic criteria for HFpEF have been far more problematic so far. In 2019, a writing committee initiated by the HFA of the ESC therefore produced an updated consensus recommendation—the HFA–PEFF diagnostic algorithm [55]. A modified version is presented in Table 3.

The CHARM trial [56] showed that each 10% reduction in EF was associated with a 39% increase in the risk of mortality, but this was only for EF below 45% [56]. Many measurements of structure and function of the cardiovascular system correlate with mortality in HF.

Many echocardiographic markers have prognostic value in HF (Fig. 1; Tables 1, 2, and 3).

Multiple impaired regulatory axes are seen in HF, including the renin-angiotensin-aldosterone system (RAAS), sympathetic regulation, neurohormonal regulation, and the cardiac stretch response. HF is associated with a chronic inflammatory state, oxidative stress, and in effect extracellular matrix remodeling [119]. Many prognostic biomarkers in HF have been identified. A classification of useful biomarkers based on their pathophysiological role in HF is presented in Table 5.

Nowadays, natriuretic peptides are the gold standard biomarkers [8]. Brain natriuretic peptide has been shown to predict morbidity, mortality, and hospitalization from HF in clinical practice [121‐123]. In the trial (PROTECT) by Januzzi et al. [124], patients who had NT-proBNP-guided therapy for heart failure benefited. Each natriuretic peptide has specific cut-off concentrations (Table 6). Plasma concentrations should be interpreted in the context of the clinical setting of the patient [125].

Previous studies have shown different biomarker profiles between patients with HFrEF, heart failure with mid-range ejection fraction (HFmrEF), and HFpEF. For example, in the Tromp et al. study [126], HFmrEF was associated with hemoglobin, red blood cells, BNP, galectin-3, endothelin-1, and syndecan-1. In contrast, HFrEF was mostly associated with BNP, kidney injury molecule-1 (KIM-1), troponin-I (TnI), red blood cells, and hemoglobin, whereas HFpEF was associated with BNP, angiogenin, hemoglobin, galectin-3, D-dimer, and inflammation markers (pentraxin-3, RAGE) and a remodeling marker (osteopontin). In another Tromp et al. study [127], the main proteins in HFrEF were NTproBNP, growth differentiation factor-15 (GDF-15), interleukin-1 receptor type 1 (ILR-1), and activating transcription factor 2, while central proteins in HFpEF were catenin beta-1 and integrin subunit beta-2. HFrEF was related to DNA binding transcription factor activity, regulation of nitric oxide biosynthesis, and cellular protein metabolism. However, HFpEF was related to cytokine response, extracellular matrix organization, and inflammation. In addition to the above, in the Nadar et al. study [120], markers of inflammation such as high-sensitivity C-reactive protein (hsCRP), ST2, and cystatin C (CysC) levels and markers of myocardial fibrosis such as galectin-3 were identified to be increased in HFpEF patients.

In the Bielecka-Dabrowa et al. study [128], biomarkers with different pathophysiological backgrounds (NT-proBNP, CT-1, TGF-β, and CysC) gave additive prognostic value for incident HF in hypertensive patients compared to NT-proBNP alone. Michalska-Kasiczak et al. [129] also noted that a single biomarker may not be sufficient in clinical practice in a heterogeneous group of HF patients. They suggested that is necessary to use a biomarker panel. Biomarker profiles of patients with HFmrEF, HFpEF, and HFrEF are different. The biomarkers in HFpEF are mainly based on inflammation, while in HFrEF, they are more cardiac stretch based, and in HFmrEF, they are related to both inflammation and cardiac stretch. Biomarkers associated with inflammation and heart remodeling are predictive in HFmrEF and HFpEF but not in HFrEF. These data could have important therapeutic consequences for the group of HF patients and suggest that it is necessary to use a biomarker panel [120].

According to recent news, neutrophil gelatinase-associated lipocalin (NGAL), a marker of renal injury, seems to be also a good factor in the diagnosis and prognostic prediction in HF [130].

Different miRNAs (miR423-5p, miR320a, and miR22) could be increased in patients with HF [131]. A recent study suggested that miR423-5p could be the best potential biomarker [132].

Hospitalization for HF within the last year has been significant risk factor of subsequent hospitalizations. The results of the QUALIFY survey showed that 30.4% of the patients had a history of at least two HF hospitalizations [133]. In the ESC-HF Pilot study, 57% of the HF patients had a history of previous hospitalizations [134] and, additionally, 24.75% of them were rehospitalized in a 1-year follow-up [135].

Different risk models were constructed to assess different clinical endpoints in the HF population [17]. Figure 3 provides a list of studies that have identified many prognostic values in HF (Fig. 3).

All of the presented models have shown only moderate probability in predicting death in HF [17]. Moreover, while their effects appear to be acceptable at the population level, they do not sufficiently predict outcome for an individual patient [147]. As we can see, there are also numerous parameters that may be used in clinical practice and should be used in order to determine the overall risk of our patients as accurately as possible.

Cited studies allow for the isolation of variables included in the models more often than others: sex, age, SBP, HR, NYHA class, LVEF, BUN level, serum creatinine, and sodium concentration. Other strong prognostic factors of mortality in HF, consistently reported in different models, include BNP/NT-proBNP concentration, weight or body mass index, and diabetes mellitus [17, 135‐147]. Nevertheless, there is no possibility at the moment to assess and monitor HF with a single parameter or a simple scale that would apply to the whole population of patients.

Despite the identification of many markers and models of poor prognosis, clinical decisions and guidelines in HF are still based mainly on several basic parameters, such as the presence of HF symptoms (NYHA class), LVEF, and the duration and morphology of the QRS complex [17]. But considering the cited works, all potential tools for assessing the risk of HF patients should be used if possible.