Prognostic scales in advanced heart failure

Introduction

Heart transplantation (HT) is the treatment of choice for patients with advanced heart failure (HF) who remain symptomatic despite optimal medical therapy. Given that the prognosis of HT candidates is poor due to the small number of donors, the long waiting time, as well as potential perioperative complications, risk stratification is a crucial element of management in this population [1–3]. Over the years, to minimize mortality in patients awaiting HT, allocation policy has prioritized sicker candidates to receive donor hearts. However, not all candidates listed with the same status share similar risk of death while waiting for and after the HT. In addition, patients at the highest risk of waiting list death also present high rates of post-HT mortality [3, 4]. Therefore, optimal selection of HT candidates requires constant considerations of balance between waiting list mortality and post-transplant outcomes [4, 5]. Physicians involved in the care of advanced HF patients often estimate their risk of death incorrectly due to difficulties in assessing the relative weight of each prognostic parameter, personal beliefs, or previous experiences [2, 6]. Therefore, the assessment of prognosis cannot be based on the clinician’s knowledge alone, and an in-depth analysis with effective and simple prognostic tools is needed [7–10]. Prognostic scales are important tools for calculating the probability of a specific event; they enable holistic evaluation of the patient, taking into account many important clinical, demographic, and laboratory variables.
Among the available prognostic scales, only the Heart Failure Survival Score (HFSS) and the Seattle Heart Failure Model (SHFM) are used in everyday clinical practice. The International Society for Heart and Lung Transplantation (ISHLT) guidelines for the care of HT candidates recommend using the HFSS and SHFM scores in the assessment of prognosis of ambulatory patients with advanced HF qualified for HT [1]. However, there are several promising predictive scales such as the Model for End-Stage Liver Disease (MELD) and its modifications, the Index for Mortality Prediction After Cardiac Transplantation (IMPACT) and the RADIAL scale, that may also become valuable tools for risk stratification in the near future. In this review, we describe the prognostic scales commonly used for advanced HF, namely, the HFSS and SHFM, as well as new promising scales for evaluating waiting list mortality and post-transplant outcomes.

Heart Failure Survival Score

The HFSS is one of the widely used predictive models; it was developed in the 1990s by Aaronson et al. [8]. This scale was derived from the data of 268 ambulatory patients referred for consideration of HT from 1986 to 1991 and was validated in a group of 199 similar patients from 1993 to 1995. Multivariate analysis revealed independent risk factors that were used to create two versions of this scale: an invasive and a non-invasive one. The non-invasive version of the HFSS is calculated from a formula including HF etiology, peak oxygen uptake, mean arterial blood pressure, resting heart rate, serum sodium, left ventricular ejection fraction, and intraventricular conduction delay > 120 ms. In the invasive version, pulmonary capillary wedge pressure is taken into account in addition to the above variables. However, the addition of this catheterization-derived variable did not improve the risk stratification of patients in the final algorithm, so the non-invasive version of the HFSS scale is used more often in clinical practice [8]. The calculated HFSS score is assigned to one of three risk groups: low risk (≥ 8.10), medium risk (7.20 to 8.09), or high risk (≤ 7.19) [8]. According to Aaronson’s scale, high-risk patients should be prioritized for HT due to the high risk of death during the 1-year follow-up.
Many studies have confirmed good prognostic strength of the HFSS scale in assessing outcomes of HF patients [7–10]; however, most of them were conducted in the past era of HF therapy, when a minimal percentage of patients were treated with -blockers, mineralocorticoid receptor antagonists, and implantable devices. Therefore, the prognostic power of the HFSS scale in the current standard of care for HF patients might be limited. The available literature lacks prospective validated studies confirming the prognostic power of the HFSS scale in cohorts of HF patients treated with current medical therapy for HF [11].

Seattle Heart Failure Model

The Seattle Heart Failure Model (SHFM) was derived to predict a composite outcome of death, urgent HT, and ventricular assist device (VAD) implantation in a cohort of 1125 HF patients from the PRAISE I clinical trial database. The scale was then validated prospectively in five additional cohorts including 9942 HF patients from the ELITE2, Val-HeFT, UW, RENAISSANCE, and IN-CHF clinical trial databases [12]. The SHFM incorporates 20 variables representing the patient’s clinical characteristics (age, gender, NYHA class, weight, systolic blood pressure, ischemic etiology, left ventricular ejection fraction), laboratory data (serum sodium, hemoglobin, uric acid, total cholesterol, lymphocyte percentage), medications (-blocker, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, statin, aldosterone blocker, loop diuretic equivalent dose, allopurinol), and device therapy (implantable cardioverter-defibrillator, cardiac resynchronization therapy) [12]. Based on the scores derived from the above variables, the patient can be classified as low-, medium-, or high-risk. The scale provides a good estimate of mean, 1-, 2-, and 3-year survival and allows for the estimation of predictive benefits from adding pharmacological agents or device therapy to the patient’s treatment. In 2013, the SHFM was updated to include inotropes, intra-aortic balloon pumping, ventilation, ultrafiltration, and benefits from new VADs in order to enable better risk-of-death stratification in light of more recent guidelines for the management of HF [13]. A calculator for the current SHFM scale is available on the internet (http://depts.washington.edu/shfm). Several other studies have analyzed the accuracy of modified versions of the SHFM scale created by adding new prognostic factors such as renal function, diabetes mellitus, and brain natriuretic peptide [7, 14–17]. Although the SHFM allows an accurate estimation of the risk of death, the multitude of parameters required to calculate the total risk may limit its usefulness in everyday practice.

Model for End-Stage Liver Disease and its modifications

The MELD was originally developed to assess short-term prognosis in patients with cirrhosis undergoing elective placement of transjugular intrahepatic portosystemic shunts. Subsequently, it was adopted for prioritizing liver transplant candidates based on disease severity [18]. Currently, this scoring system provides valuable prognostic information in the population of patients with HF [19].
The standard MELD scale consists of three objective and easily obtainable variables: the international normalized ratio (INR), serum bilirubin, and serum creatinine [19]. Bilirubin is a well-established marker of hepatic metabolic function, while the INR reflects coagulopathy associated with synthetic dysfunction. The third component, i.e., creatinine level, is used to assess the severity of renal dysfunction. Kidney and liver dysfunction is commonly observed in HF patients and is closely correlated with adverse outcomes and increased risk of mortality [19]. Therefore, assessment of cardio-hepatic and cardio-renal interactions using the MELD scale may also improve risk stratification in HF patients [19]. The ability of the MELD score to predict clinical outcomes has been confirmed in different HF populations [20, 21]. However, the MELD score has one important limitation: it cannot be applied in patients treated with oral anticoagulants due to the distortion of INR values [21]. As an alternative to the traditional MELD system, the modified Model for End-Stage Liver Disease (modMELD) and the MELD excluding INR (MELD-XI) were developed [20–22]. The modMELD is identical to the standard version except for the substitution of the INR component with albumin [20], whereas the MELD-XI score is based on creatinine and bilirubin alone [21]. Given that INR is not used in their calculation, the modifications of the MELD scale remain accurate even if the patient receives oral anticoagulation. For this reason, the MELD-XI and modMELD scales seem to be superior to the standard MELD score, especially in cohorts of patients with advanced HF referred for HT evaluation or undergoing VAD implantation [19, 20, 23, 24].

Interagency Registry For Mechanically Assisted Circulatory Support classification

The most commonly used system for the subjective evaluation of the severity of HF symptoms is the classification of the New York Heart Association (NYHA) [25]. However, it does not allow accurate grading of risk, especially in populations with advanced HF, which prompted the development of the new INTERMACS (Interagency Registry For Mechanically Assisted Circulatory Support) classification. It consists of seven clinical profiles, ranging from patients on the brink of death (INTERMACS level 1), who have little chance of surviving, to patients who are clinically stable and do not currently have indications for urgent interventions (INTERMACS level 7) (Table I) [26]. The INTERMACS profiles were devised during the development of the database from a multicenter registry of VAD to unify the criteria describing the clinical characteristics of advanced HF patients, clarify the target populations for VAD implantation, and present the available treatment alternatives [4, 26, 27].
This scale also provides important prognostic information for HF patients receiving VADs [4, 26, 27]. Patients who do not require inotropic support before VAD implantation (INTERMACS profiles 4–7) have significantly better survival and shorter hospital stays than patients on high doses of inotropes (INTERMACS profiles 1–3) [28]. The INTERMACS profiles are also used to stratify prognosis after urgent HT [4, 29]. The mortality rate among INTERMACS level 1 patients is significantly higher than among INTERMACS level 2–4 patients in the first year after HT. The poor post-HT outcomes in INTERMACS level 1 patients are mostly due to the high incidence of primary graft failure and multiorgan dysfunction. In addition, the patients at INTERMACS level 1 are more likely to require preoperative mechanical circulatory support and greater doses of vasoactive amines; furthermore, dysfunction of the liver and kidneys in such patients is more severe [4, 29]. The main advantage of the INTERMACS classification that makes it useful in evaluating prognosis is its ability to precisely stratify the clinical and hemodynamic condition of candidates for HT or VAD implantation (Table I) [4, 26–29].

Index for Mortality Prediction After Cardiac Transplantation

The Index for Mortality Prediction After Cardiac Transplantation (IMPACT) was recently derived and internally validated to predict the likelihood of 1-year mortality after HT in a cohort of 21 378 patients from the United Network for Organ Sharing (UNOS) data [5]. The 50-point IMPACT risk score incorporates 12 preoperative recipient-specific variables with appropriate point values: age, serum bilirubin level, creatinine clearance, dialysis between listing and transplant, female sex, HF etiology, recent infection, intra-aortic balloon pump, mechanical ventilation before orthotopic heart transplantation, race, temporary circulatory support, VAD. According to the IMPACT scale, the rate of one-year survival deteriorates in patients achieving higher scores as follows: 0 to 2 points: 92.5%; 3 to 5 points: 89.9%; 7 to 9 points: 86.3%; and 10 or more points: 74.9%. Furthermore, the postoperative one-year mortality rate in patients with preoperative IMPACT scores of 20 or more exceeds 50% [5].
The IMPACT score as a predictor of short-term and long-term mortality after HT was also validated externally by Kilic et al. using data from the ISHLT [30]. However, the IMPACT score from the ISHLT cohort differs slightly from the original UNOS cohort [5, 30]. In the ISHLT cohort, the proportion of ischemic heart disease was lower, the average creatinine clearance and 1-year mortality risk after HeartMate II implantation was higher, and no information about the patient’s race was included [30]. Nevertheless, the ability of the IMPACT score to estimate one-year survival was comparable in the two cohorts [5, 30]. It seems that the IMPACT score can serve to drive clinical decisions regarding organ allocation and may prove especially useful in view of the increasing population of potential recipients and the significant shortage of donors. In the near future, the IMPACT scale may also become a valuable tool for facilitating discussions with patients and their families regarding prognosis after HT.

RADIAL Scale

The RADIAL scale was derived and validated to predict the development of primary graft failure (PGF), which is an important cause of early death and need for re-transplantation among HT recipients [31, 32]. This scale incorporates 4 recipient variables (right atrial pressure ≥ 10 mm Hg, age ≥ 60 years, diabetes mellitus, preoperative inotrope dependence), 1 donor variable (age ≥ 30 years), and 1 procedural variable (length of ischemic time ≥ 240 min). The RADIAL score is calculated by adding 1 point when a variable is present and 0 when it is absent, which results in a maximum of 6 and a minimum of 0 points [31]. The obtained score is then assigned to one of the risk strata: low (RADIAL < 2, PGF 12.1%), intermediate (RADIAL = 2, PGF risk 19.1%), or high (RADIAL > 2 PGF risk 27.5%) [32]. The scale is a promising tool for optimizing donor-recipient matching because it considers the interactions between factors associated with the recipient, donor, procedure, and HT duration. Importantly, this is the only scale that estimates the risk of PGF, which may be useful for implementing preventive or early therapeutic measures for PGF and may perhaps help reduce early mortality after HT. However, further prospective studies are needed to substantiate the usefulness of the RADIAL scale in everyday clinical practice.

Conclusions

The application of prognostic scales should be an essential component of the process of qualifying patients for advanced forms of therapy such as HT and VAD implantation. They enable accurate risk stratification and estimation of the potential benefits and threats associated with the therapy. Among the prognostic scales described in this paper, currently only HFSS, SHFM and INTERMACS are applied in everyday clinical practice.
According to the ISHLT guidelines, the HFSS and SHFM scales are used to assess the prognosis of ambulatory patients with advanced HF qualified for HT, while the INTERMACS is commonly used in patients receiving VAD.

Disclosure

The authors report no conflict of interest.

References

1. Mehra MR, Canter CE, Hannan MM, Semigran MJ, Uber PA, Baran DA, Danziger-Isakov L, Kirklin JK, Kirk R, Kushwaha SS, Lund LH, Potena L, Ross HJ, Taylor DO, Verschuuren EA, Zuckermann A; International Society for Heart Lung Transplantation (ISHLT) Infectious Diseases Council; International Society for Heart Lung Transplantation (ISHLT) Pediatric Transplantation Council; International Society for Heart Lung Transplantation (ISHLT) Heart Failure and Transplantation Council. The 2016 International Society for Heart Lung Transplantation listing criteria for heart transplantation: a 10-year update. J Heart Lung Transplant 2016; 35: 1-23.
2. Ford I, Robertson M, Komajda M, Böhm M, Borer JS, Tavazzi L, Swedberg K; SHIFT Investigators. Top ten risk factors for morbidity and mortality in patients with chronic systolic heart failure and elevated heart rate: The SHIFT Risk Model. Int J Cardiol 2015; 184: 163-169.
3. Singh TP, Milliren CE, Almond CS, Graham D. Survival benefit from transplantation in patients listed for heart transplantation in the United States. J Am Coll Cardiol 2014; 63: 1169-1178.
4. Barge-Caballero E, Segovia-Cubero J, Almenar-Bonet L, Gonzalez-Vilchez F, Villa Arranz, Delgado-Jimenez J, Lage-Galle E, Perez-Villa F, Lambert-Rodríguez JL, Manito-Lorite N, Arizon-Del Prado JM, Brossa-Loidi V, Pascual-Figal D, Fuente-Galan Lde L, Sanz-Julve M, Muńiz-Garcia J, Crespo-Leiro M. Preoperative INTERMACS profiles determine postoperative outcomes in critically ill patients undergoing emergency heart transplantation: analysis of the Spanish National Heart Transplant Registry. Circ Heart Fail 2013; 6: 763-772.
5. Weiss ES, Allen JG, Arnaoutakis GJ, George TJ, Russell SD, Shah AS, Conte JV. Creation of a quantitative recipient risk index for mortality prediction after cardiac transplantation (IMPACT). Ann Thorac Surg 2011; 92: 914-921.
6. Hauptman PJ, Swindle J, Hussain Z, Biener L, Burroughs TE. Physician attitudes toward end-stage heart failure: a national survey. Am J Med 2008; 121: 127-135.
7. Goda A, Williams P, Mancini D, Lund LH. Selecting patients for heart transplantation: comparison of the Heart Failure Survival Score (HFSS) and the Seattle heart failure model (SHFM). J Heart Lung Transplant 2011; 30: 1236-1243.
8. Aaronson KD, Schwartz JS, Chen TM, Wong KL, Goin JE, Mancini DM. Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. Circulation 1997; 95: 2660-2667.
9. Lund LH, Aaronson KD, Mancini DM. Validation of peak exercise oxygen consumption and the Heart Failure Survival Score for serial risk stratification in advanced heart failure. Am J Cardiol 2005; 95: 734-741.
10. Zugck C, Krüger C, Kell R, Körber S, Schellberg D, Kübler W, Haass M. Risk stratification in middle-aged patients with congestive heart failure: prospective comparison of the Heart Failure Survival Score (HFSS) and a simplified two-variable model. Eur J Heart Fail 2001; 3: 577-585.
11. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, Falk V, González-Juanatey JR, Harjola VP, Jankowska EA, Jessup M, Linde C, Nihoyannopoulos P, Parissis JT, Pieske B, Riley JP, Rosano GM, Ruilope LM, Ruschi-tzka F, Rutten FH, van der Meer P, Task Force Members. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Eur J Heart Fail 2016; 18: 891-975.
12. Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, Anand I, Maggioni A, Burton P, Sullivan MD, Pitt B, Poole-Wilson PA, Mann DL, Packer M. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation 2006; 113: 1424-1433.
13. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Drazner MH, Fona-row GC, Geraci SA, Horwich T, Januzzi JL, Johnson MR, Kasper EK, Levy WC, Masoudi FA, McBride PE, McMurray JJ, Mitchell JE, Peterson PN, Riegel B, Sam F, Stevenson LW, Tang WH, Tsai EJ, Wilkoff BL; American College of Cardiology Foundation; American Heart Association Task Force on Practice Guidelines. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013; 62: e147-e239.
14. May HT, Horne BD, Levy WC, Kfoury AG, Rasmusson KD, Linker DT, Mozaffarian D, Anderson JL, Renlund DG. Validation of the Seattle Heart Failure Model in a community-based heart failure population and enhancement by adding B-type natriuretic peptide. Am J Cardiol 2007; 100: 697-700.
15. Cabassi A, de Champlain J, Maggiore U, Parenti E, Coghi P, Vicini V, Tedeschi S, Cremaschi E, Binno S, Rocco R, Bonali S, Bianconcini M, Guerra C, Folesani G, Montanari A, Regolisti G, Fiaccadori E Prealbumin improves death risk prediction of BNP added Seattle Heart Failure Model: results from a pilot study in elderly chronic heart failure patients. Int J Cardiol 2013; 168: 3334-3339.
16. Giamouzis G, Kalogeropoulos AP, Georgiopoulou VV, Agha SA, Rashad MA, Laskar SR, Smith AL, Butler J. Incremental value of renal function in risk prediction with the Seattle Heart Failure Model. Am Heart J 2009; 157: 299-305.
17. Alba AC, Agoritsas T, Jankowski M, Courvoisier D, Walter SD, Guyatt GH, Ross HJ. Risk prediction models for mortality in ambulatory patients with heart failure: a systematic review. Circ Heart Fail 2013; 6: 881-889.
18. Malinchoc M, Kamath PS, Gordon FD, Peine CJ, Rank J, ter Borg PC. A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatology 2000; 31: 864-871.
19. Kim MS, Kato TS, Farr M, Wu C, Givens RC, Collado E, Mancini DM, Schulze PC. Hepatic dysfunction in ambulatory patients with heart failure: application of the MELD scoring system for outcome prediction. J Am Coll Cardiol 2013; 61: 2253-2261.
20. Chokshi A, Cheema FH, Schaefle KJ, Jiang J, Collado E, Shahzad K, Khawaja T, Farr M, Takayama H, Naka Y, Mancini DM, Schulze PC. Hepatic dysfunction and survival after orthotopic heart transplantation: application of the MELD scoring system for outcome prediction. J Heart Lung Transplant 2012; 31: 591-600.
21. Szyguła-Jurkiewicz B, Nadziakiewicz P, Zakliczynski M, Szczurek W, Chraponski J, Zembala M, Gasior M. Predictive value of hepatic and renal dysfunction based on the models for end-stage liver disease in patients with heart failure evaluated for heart yransplant. Transplant Proc 2016; 48: 1756-1760.
22. Heuman DM, Mihas AA, Habib A, Gilles HS, Stravitz RT, Sanyal AJ, Fisher RA. MELD-XI: a rational approach to “sickest first” liver transplantation in cirrhotic patients requiring anticoagulant therapy. Liver Transpl 2006; 13: 30-37.
23. Yang JA, Kato TS, Shulman BP, Takayama H, Farr M, Jorde UP, Mancini DM, Naka Y, Schulze PC. Liver dysfunction as a predictor of outcomes in patients with advanced heart failure requiring ventricular assist device support: use of the Model of End-stage Liver Disease (MELD) and MELD eXcluding INR (MELD-XI) scoring system. J Heart Lung Transplant 2012; 31: 601-610.
24. Szygula-Jurkiewicz B, Szczurek W, Skrzypek M, Zakliczyński M, Siedlecki Ł, Przybyłowski P, Zembala M, Gąsior M. One-year survival of ambulatory patients with end-stage heart failure: the analysis of prognostic factors. Pol Arch Intern Med 2017; 127: 254-260.
25. Raphael C, Briscoe C, Davies J, Ian Whinnett Z, Manisty C, Sutton R, Mayet J, Francis DP. Limitations of the New York Heart Association functional classification system and self-reported walking distances in chronic heart failure. Heart 2007; 93: 476-482.
26. Stevenson LW, Pagani FD, Young JB, Jessup M, Miller L, Kormos RL, Naftel DC, Ulisney K, Desvigne-Nickens P, Kirklin JK. INTERMACS profiles of advanced heart failure: the current picture. J Heart Lung Transplant 2009; 28: 535-541.
27. Stewart GC, Kittleson MM, Patel PC, Cowger JA, Patel CB, Mountis MM, Johnson FL, Guglin ME, Rame JE, Teuteberg JJ, Stevenson LW. INTERMACS (Interagency Registry for Mechanically Assisted Circulatory Support) profiling identifies ambulatory patients at high risk on medical therapy after hospitalizations for heart failure. Circ Heart Fail 2016; 9: e003032.
28. Stepanenko A, Potapov E, Jurmann B, Lehmkuhl HB, Dandel M, Siniawski H, Drews T, Hennig E, Kaufmann F, Jurmann MJ, Weng Y, Pasic M, Hetzer R, Krabatsch T. Outcomes of elective versus emergent permanent mechanical circulatory support in the elderly: a single-center experience. J Heart Lung Transplant 2010; 29: 61-65.
29. Barge-Caballero E, Paniagua-Martín MJ, Marzoa-Rivas R, Campo-Pérez R, Rodríguez-Fernández JÁ, Pérez-Pérez A, García-Bueno L, Blanco-Canosa P, Cancela ZG, Solla-Buceta M, Juffé-Stein A, Herrera-Noreńa JM, Cuenca-Castillo JJ, Muńiz J, Castro-Beiras A, Crespo-Leiro MG. Usefulness of the INTERMACS Scale for predicting outcomes after urgent heart transplantation. Rev Esp Cardiol 2011; 64: 193-200.
30. Kilic A, Allen JG, Weiss ES. Validation of the United States-derived Index for Mortality Prediction After Cardiac Transplantation (IMPACT) using international registry data. J Heart Lung Transplant 2013; 32: 492-498.
31. Segovia J, Cosío MD, Barceló JM, Bueno MG, Pavía PG, Burgos R, Serrano-Fiz S, García-Montero C, Castedo E, Ugarte J, Alonso-Pulpón L. RADIAL: a novel primary graft failure risk score in heart transplantation. J Heart Lung Transplant 2011; 30: 644-651.
32. Cosío Carmena MD, Gómez Bueno M, Almenar L, Delgado JF, Arizón JM, González Vilchez F, Crespo-Leiro MG, Mirabet S, Roig E, Pérez Villa F, Fernández-Yańez JF, Lambert JL, Manito N, Fuente L, Sanz Julve ML, Pascual D, Rábago G, Millán I, Alonso-Pulpón LA, Segovia J. Primary graft failure after heart transplantation: characteristics in a contemporary cohort and performance of the RADIAL risk score. J Heart Lung Transplant 2013; 32: 1187-1195.