original article

Oman Medical Journal [2023], Vol. 38, No. 4: e529 

Development and Validation of R-hf Risk Score in Acute Heart Failure Patients in the Middle East

Rajesh Rajan1*, Mohammed Al Jarallah1, Ibrahim Al-Zakwani2,3, Raja Dashti1, Kadhim Sulaiman4,5, Prashanth Panduranga4,5, Peter A Brady6 and Zhanna Kobalava7

1Department of Cardiology, Sabah Al Ahmed Cardiac Centre, Kuwait City, Kuwait

2Department of Pharmacology and Clinical Pharmacy, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat, Oman

3Oman and Gulf Health Research, Muscat, Oman

4Department of Cardiology, Royal Hospital, Muscat, Oman

5Director General of Specialized Medical Care, Ministry of Health, Muscat, Oman

6Department of Cardiology, Illinois Masonic Medical Center, Chicago IL, USA

7Department of Internal Diseases with Courses of Cardiology and Functional Diagnostics, Peoples` Friendship University of Russia, Moscow, Russia

article info


Objectives: The Rajan’s heart failure (R-hf) score was proposed to aid risk stratification in heart failure patients. The aim of this study was to validate R-hf risk score in patients with acute decompensated heart failure. Methods: R-hf risk score is derived from the product estimated glomerular filtration rate (mL/min), left ventricular ejection fraction (%), and hemoglobin levels (g/dL) divided by N-terminal pro-brain natriuretic peptide (pg/mL). This was a multinational, multicenter, prospective registry of heart failure from seven countries in the Middle East. Univariable and multivariable logistic regression was applied. Results: A total of 776 patients (mean age = 62.0±14.0 years, 62.4% males; mean left ventricular ejection fraction = 33.0±14.0%) were included. Of these, 459 (59.1%) presented with acute decompensated chronic heart failure. The R-hf risk score group (≤ 5) was marginally associated with a higher risk of all-cause cumulative mortality at three months (adjusted odds ratio (aOR) = 4.28; 95% CI: 0.90–20.30; p = 0.067) and significantly at 12 months (aOR = 3.84; 95% CI: 1.23–12.00; p = 0.021) when compared to those with the highest R score group (≥ 50). Conclusions: Lower R-hf risk scores are associated with increased risk of all-cause cumulative mortality at three and 12 months.

Heart failure (HF) is a major cause of increased cardiovascular mortality yet identification of most at-risk patients is challenging and HF management difficult.1 Early identification and initiation of guideline-directed medical therapy improves the outcomes. Major adverse cardiovascular events associated with HF are significant, and are associated with an increased risk of death.2 Several risk prediction tools are available for patients with HF, but many are not user-friendly and require entering multiple variables.3–23 In contrast, the Rajan's heart failure (R-hf) score is a unique risk-predicting tool that only requires four factors to be entered and is user-friendly for predicting mortality risk in HF patients with reduced ejection fraction (HFrEF).24–26


This study uses data collected as part of the Gulf CARE registry, a multinational, multicenter, prospective registry of HF27 in patients aged ≥ 18 years with a diagnosis of acute heart failure (AHF) admitted between 14 February 2012 and 14 November 2012 to 47 hospitals (research sites) in seven Middle Eastern countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, UAE, and Yemen) were recruited for the current study.28 Demographic information, behavioral risk factors, comorbidities, medical history, clinical presentations, investigation results, and in-hospital outcomes were all included in the baseline data. Follow-up at three months and outpatient clinic visits at one year were used to determine all-cause mortality.

All data collected were entered into the Gulf CARE website’s custom-made electronic case record form (www.gulfcare.org). The study was approved by all relevant ethics committees or review boards from each of the seven participating hospitals. The study was registered at www.clinicaltrials.gov (NCT01467973).

The clinical data standards for the American College of Cardiology and the 2008 European Society of Cardiology procedures were used to generate the data variables in the case record form, while the European Society of Cardiology criteria were employed to define AHF.29,30 Within one month of the index admission, khat chewing was stipulated as chewing khat plant/leaves (catha edulis containing cathinone, an amphetamine-like stimulant) can lead to hypertension, euphoria, dilated cardiomyopathy, and myocardial infarction.31 Chronic kidney disease was defined as serum creatinine levels > 177 mmol/L (or 2 mg/dL) for three months or an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2. Anemia was defined as hemoglobin (Hb) levels of 12 g/dL in women and 13 g/dL in men. The R-hf risk score was computed by multiplying eGFR (mL/min), left ventricular ejection fraction (%), and Hb levels (g/dL) by N-terminal pro-brain natriuretic peptide (NT-proBNP) (pg/mL). R-hf scores of < 5 were considered high risk, 5–10 were considered moderate risk, 11–50 were considered low risk, and R-hf values of > 50 were considered zero risk.24,25

Frequencies and percentages were used to present the categorical variables and mean and SD for continuous variables. Pearson’s chi-square tests (or Fisher’s exact tests for cells with expected values of < 5) were used to examine differences across R-hf score groups and ordinary least squares regression was used to analyze the data. To investigate the impact of R-hf risk score on all-cause mortality (primary outcome) at three-month and 12-month post-hospital discharge, we used three multivariable logistic regression models at the same time.

Age, gender, body mass index, smoking, khat chewing, peripheral vascular disease, hypertension, diabetes mellitus, prior stroke/transient ischemic attack, systolic blood pressure, and diastolic blood pressure were all factored into the model adjustments. The model adjustments also included coronary artery bypass graft procedure, in-hospital percutaneous coronary intervention, or in-hospital course (comprised of non-invasive ventilation, cardiogenic shock, intubation/ventilation, intra-aortic balloon pump, inotropes, atrial fibrillation requiring therapy, acute dialysis/ultrafiltration, blood transfusion, stroke, major bleeding, and systemic infection requiring therapy). Discharged drugs (diuretics, digoxin, clopidogrel, oral nitrates, beta-blockers, angiotensin-converting enzyme inhibitors, calcium channel blockers, aldosterone antagonists, aspirin, angiotensin II receptor blockers, ivabradine (If channel blocker)) are among the other changes to the model.

The Hosmer and Lemeshow goodness-of-fit metric was used to analyze the multivariable logistic model.32 A Hosmer and Lemeshow statistic with a p > 0.05 was deemed a good fit based on the chi-square distribution. The area under the receiver operating characteristic curve, commonly known as the C-index, was used to evaluate the logistic model’s discriminatory capacity.33 The a priori two-tailed level of significance was set at p < 0.05. For statistical analysis, STATA was employed (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP.).


A total of 5005 AHF patients from the Gulf CARE registry were screened. Seven hundred seventy-six (15.5%) patients were included after excluding 4229 (84.5%) patients with missing NT-proBNP levels. The baseline characteristics of the study cohort are shown in Table 1. The cohort had a mean age of 62.0±14.0 years, 62.4% were male and the mean left ventricular ejection fraction was 33.0±14.0%. Of these, 59.1% (n = 459) of patients presented with acute decompensated chronic heart failure. Hypertension (n = 579; 74.6%), coronary artery disease (n = 474; 61.1%), diabetes mellitus (n = 514; 66.2%), and hyperlipidemia (n = 336; 43.3%) were among the most common comorbid conditions [Table 1].

Table 1: Demographic and clinical characteristics of the cohort stratified by Rajan's heart failure (R-hf) score among patients with acute heart failure.



n (%)

(n = 776)

R-hf score risk category, n (%)



(n = 324)


(n = 127)


(n = 242)


(n = 83)


Age, mean ± SD, years

62.0 ± 14.0

63.0 ± 15.0

61.0 ± 15.0

62.0 ± 13.0

60.0 ± 14.0


Male gender

484 (62.4)

199 (61.4)

89 (70.1)

150 (62.0)

46 (55.4)


BMI, mean ± SD, kg/m2

29.5 ± 7.3

28.6 ± 7.0

28.6 ± 7.6

30.3 ± 7.2

32.0 ± 7.5

< 0.001


97 (12.5)

34 (10.2)

21 (16.5)

30 (12.4)

12 (14.5)



6 (0.8)

1 (0.3)

2 (1.6)

2 (0.8)

1 (1.2)



36 (4.6)

15 (4.6)

7 (5.5)

8 (3.3)

6 (7.2)


Medical history


336 (43.3)

135 (41.7)

55 (43.3)

105 (43.4)

41 (49.4)



474 (61.1)

201 (62.0)

83 (65.4)

143 (56.1)

47 (56.6)



579 (74.6)

240 (74.1)

93 (73.2)

177 (73.1)

69 (83.1)


Diabetes mellitus

514 (66.2)

219 (67.6)

74 (58.3)

160 (66.1)

61 (73.5)



38 (4.9)

25 (7.7)

4 (3.1)

6 (2.5)

3 (3.6)



95 (12.2)

26 (8.0)

12 (9.4)

40 (16.5)

70 (84.3)



68 (8.8)

35 (10.8)

9 (7.1)

2 (0.8)

4 (4.8)



136 (17.5)

55 (17.0)

23 (18.1)

47 (19.4)

11 (13.0)


Clinical parameters at presentation

HR, mean ± SD, bpm

76.0 ± 12.0

76.0 ± 14.0

76.0 ± 11.0

76.0 ± 11.0

73.0 ± 11.0


SBP, mean ± SD, mmHg

142.0 ± 43.0

141.0 ± 34.0

137.0 ± 32.0

141.0 ± 33.0

151.0 ± 39.0


DBP, mean ± SD, mmHg

80.0 ± 21.0

80.0 ± 20.0

81.0 ± 19.0

80.0 ± 21.0

82.0 ± 24.0


Crea, mean ± SD, µmol/L

136.0 ± 114.0

189.0 ± 154.0

108.0 ± 38.0

99.0 ± 44.0

80.0 ± 34.0

< 0.001

LVEF, mean ± SD, %

33.0 ± 14.0

30.0 ± 13.0

32.0 ± 13.0

36.0 ± 13.0

43.0 ± 12.0

< 0.001

eGFR, mean ± SD, mL/min/1.73m2

64.0 ± 36.0

47.0 ± 27.0

69.0 ± 31.0

75.0 ± 34.0

96.0 ± 41.0

< 0.001

Hb, mean ± SD, g/dL

12.2 ± 2.2

11.6 ± 2.1

12.3 ± 2.0

12.7 ± 2.1

12.8 ± 2.2

< 0.001

NT-proBNP, median (IQR), pg/mL











< 0.001

In-hospital course


65 (8.4)

18 (5.6)

9 (7.1)

23 (9.5)

15 (18.1)


Treatment course*

301 (38.8)

134 (41.4)

54 (42.5)

87 (36.0)

26 (31.3)


Admission diagnosis

De novo AHF

317 (40.9)

122 (37.7)

50 (39.4)

102 (42.1)

42 (50.6)



459 (59.1)

202 (62.3)

77 (60.6)

139 (57.4)

41 (49.4)

NYHA at discharge**



408 (54.1)

159 (51.6)

65 (52.0)

133 (55.9)

51 (61.4)


297 (39.4)

125 (40.6)

53 (42.4)

91 (38.2)

28 (33.7)


44 (5.8)

21 (6.8)

7 (5.6)

13 (5.5)

3 (3.6)


5 (0.7)

3 (1.0)

0 (0.0)

1 (0.4)

1 (1.2)

BMI: body mass index; CAD: coronary artery disease; PVD: Peripheral vascular disease; COPD: chronic obstructive pulmonary disease; TIA: transient ischemic attack; AF: atrial fibrillation; HR: heart rate; SBP: systolic blood pressure; DBP: diastolic blood pressure; Crea: first serum creatinin; LVEF: left ventricular ejection fraction; eGFR: estimated glomerular filtration rate; Hb: hemoglobin; NT-proBNP: N-terminal pro-brain natriuretic peptide; IQR: interquartile range; PCI: percutaneous coronary intervention; CABG: coronary artery bypass graft; AHF: acute heart failure; ADCHF: acute decompensated chronic heart failure; NYHA: New York Heart Association. *Treatment course, intubation/ventilation, cardiogenic shock, included non-invasive ventilation, intra-aortic balloon pump, inotropes, atrial fibrillation requiring therapy, acute dialysis/ultrafiltration, blood transfusion, major bleeding, systemic infection requiring therapy and stroke; HR was absent in 14 of the cases, whereas SBP and DBP were absent in 11. ** Those who died in hospitals were not included in the previous NYHA classification. Due to rounding, percentages may not add up to 100 %.
R-hf scores of 5: high danger; 5–10: moderate risk; 11–50: medium risk; > 50: negligible risk.

High (< 5), moderate (5–10), low (11–50), and minimal risk (> 50) patients accounted for 41.8% (n = 324), 16.4% (n = 127), 31.2% (n = 242), and 10.7% (n = 83), respectively. Patients with higher R-hf risk ratings had a higher rate of peripheral vascular disease (7.7% vs. 3.6%; p = 0.021), but were less likely to have in-hospital percutaneous coronary intervention/coronary artery bypass graft (5.6% vs. 18.1%; p = 0.003). The majority of patients received the usual HF treatment [Table 2]. Patients with higher R-hf risk scores were more likely to be on digoxin (15.6% vs. 4.9%; p = 0.023), but less likely to be on angiotensin II receptor blockers (15.2% vs. 21.0%; p < 0.001).

Table 2: Medication utilization of the cohort stratified by Rajan's heart failure (R-hf) score among patients with acute heart failure.



n (%)

(n = 776)

R-hf score risk category, n (%)



(n = 324)


(n = 127)


(n = 242)


(n = 83)

Prior medications


480 (61.9)

208 (64.2)

83 (65.4)

146 (60.3)

43 (51.8)



93 (12.0)

38 (11.7)

19 (15.0)

32 (13.2)

4 (4.8)


Oral nitrates

207 (26.7)

94 (29.0)

39 (30.7)

59 (24.4)

15 (18.1)



159 (20.4)

62 (19.1)

15 (11.8)

59 (24.4)

23 (27.7)



284 (36.6)

108 (33.3)

51 (40.2)

91 (37.6)

34 (41.0)



165 (21.3)

54 (16.7)

34 (26.8)

61 (25.2)

16 (19.3)



466 (60.1)

197 (60.8)

81 (63.8)

136 (56.2)

52 (62.7)



486 (62.6)

198 (60.1)

80 (63.0)

153 (63.2)

55 (66.3)



36 (4.6)

13 (4.0)

8 (6.3)

12 (5.0)

3 (3.6)



464 (59.8)

205 (63.3)

73 (57.5)

138 (57.0)

48 (57.8)


Aldosterone antagonists

150 (19.3)

58 (17.9)

31 (24.4)

48 (19.8)

13 (15.7)


IV medications during admission

IV frusemide – bolus

718 (92.5)

293 (90.4)

120 (94.5)

228 (94.2)

77 (92.8)


IV frusemide – infusion

117 (15.1)

58 (17.9)

19 (15.0)

30 (12.4)

10 (12.0)


IV nitrates – infusion

171 (22.0)

73 (22.5)

23 (18.1)

49 (20.2)

26 (31.3)


Discharged medications (n = 735)*


694 (94.4)

285 (94.4)

115 (95.8)

220 (94.8)

74 (91.4)



113 (15.4)

47 (15.6)

22 (18.3)

40 (17.2)

4 (4.9)


Oral nitrates

270 (36.7)

114 (37.7)

43 (35.8)

86 (37.1)

27 (33.3)



173 (23.5)

75 (24.8)

19 (15.8)

58 (25.0)

21 (25.9)



351 (47.8)

132 (43.7)

60 (50.0)

115 (50.0)

44 (54.3)



174 (23.7)

46 (15.2)

39 (32.5)

72 (31.0)

17 (21.0)

< 0.001


574 (78.1)

227 (75.2)

98 (81.7)

186 (80.2)

65 (80.2)



576 (78.4)

240 (79.5)

91 (75.8)

181 (78.0)

64 (79.0)



59 (8.0)

23 (7.6)

13 (10.8)

21 (9.1)

2 (2.5)



617 (83.9)

255 (84.4)

100 (83.3)

195 (84.1)

67 (82.7)


CCBs: calcium channel blockers; ACEIs: angiotensin converting enzyme inhibitors; ARBs: angiotensin receptor blockers.
R-hf scores of 5 were considered high risk; 5–10: moderate risk; 10–50: low risk; and > 50: minimal risk.
*Those who were discharged alive from the hospital and did not leave against medical advice (n = 735) were only given medications at discharge.
Due to rounding off, percentages may not add up to 100%.

The rates of all-cause mortality were 44 (13.6%) in high risk, 12 (9.4%) in moderate risk, 13 (5.4%) in low risk, and two (2.4%) in minimum risk at three-month, and 71 (21.9%) in high risk, 20 (15.7%) in moderate risk, 24 (9.9%) in low risk, and four (4.8%) in minimum risk at one-year. When compared to those with scores of > 50, those with scores < 5 were marginally associated with a higher risk of all-cause mortality at three months (adjusted odds ratio = 4.28; 95% CI: 0.90–20.30; p = 0.067) and significantly at 12 months (adjusted odds ratio = 3.84; 95% CI: 1.23–12.00; p = 0.021) after adjusting for demographic and clinical characteristics as well as medication use in the multivariable logistic regression model [Table 3].

Table 3: Impact of Rajan's heart failure (R-hf) scores on mortality.


R-hf score risk categories




(n = 324)


(n = 127)


(n = 242)


(n = 83)


n (%)

16 (4.9)

2 (1.6)

4 (1.7)

0 (0.0)



n (%)

44 (13.6)

12 (9.4)

13 (5.4)

2 (2.4)


aOR (95% CI);p-value

4.28 (0.90–20.30);


2.13 (0.37–12.30);


0.93 (0.16–5.27);




Goodness of fit statistics

HL p-value





n (%)

71 (21.9)

20 (15.7)

24 (9.9)

4 (4.8)

< 0.001

aOR (95% CI); p-value

3.84 (1.23–12.00);


2.03 (0.56–7.40);


1.24 (0.36–4.25);



< 0.001

Goodness of fit statistics

HL p-value


aOR: adjusted odds ratio; HL: Hosmer-Lemeshow; ROC: receiver operating characteristic.
*Due to the short sample size, a multivariable logistic model was not used for the in-hospital study. R-hf scores of 5: high risk; 5–10: moderate risk; 11–50: low risk; > 50: minimal risk.


This study is the first to employ the R-hf risk score (a derivative of eGFR, ejection fraction (EF), Hb, and NT-proBNP) in AHF to estimate all-cause mortality at three months (marginally) and 12 months after discharge. Patients with the lowest scores had the worst prognosis when using this measure. Prior risk prediction models that did not incorporate EF or renal function in their risk score had a lower mean death rate than expected.16,23,34–40 Our study showed that the R-hf risk score model was successful in predicting the prognosis and mortality of HFrEF patients. R-hf score < 5 was previously suggested to reflect a poor prognosis, which was confirmed by this study. Given our population, this score is exclusively applied to the Gulf CARE cohort, which is a largely Arab population. For physicians, the application and the calculator are available online and are easily accessible at https://www.hfriskcalc.in.24–26

In comparison to the R-hf risk score, other risk calculators have produced varying predictions when applied to various registries around the world. The Get With The Guidelines (GWTG)-HF risk score reasonably accurately predicts short-term in-hospital mortality, the Seattle Heart Failure Model and Meta-analysis Global Group in Chronic (MAGGIC) Heart Failure risk score have demonstrated utility for estimating long-term death one-to-two years after discharge.3 In terms of identifying relevant variables that potentially predict mortality, all the previously published HF risk scores had their advantages and disadvantages. Pro-BNP-guided HF treatment has a significant impact on HF patients’ prognosis.41 The R-hf risk score is simple, effective, and different from other existing HF risk scores since it includes the major variables implicated in HF pathophysiology, such as Hb, EF, eGFR, and pro-BNP.

The number of factors that must be entered into existing HF risk score calculators is a major disadvantage. The Seattle Heart Failure Model, for example, requires approximately 20 variables, whereas the MAGGIC score requires 13 and the GWTG-HF risk score contains seven variables.3 In contrast, the GWTG-HF risk score contains three variables but is also effective at predicting in-hospital and post-discharge mortality in HF patients.4 The majority of HF risk score calculators share four variables (age, blood pressure, renal function, and serum sodium) that they believe to be significant predictors of adverse outcomes.42 The majority of risk score models are more accurate than pre-hospitalization rates in predicting mortality.7 The R-hf score, like the GWTG score, predicts post-discharge mortality. The R-hf score can help identify high-risk patients and improve compliance, potentially lowering the rate of HF re-hospitalizations.

When compared to the MAGGIC score, eGFR was used instead of creatinine, with fewer variables needed in the R-hf risk score. The R-hf risk score was developed from a relatively small cohort from the Arabian Gulf Care registry of AHF patients when compared to the Seattle Heart Failure Model. Nevertheless, the advantage of the R-hf risk score is that it only includes four variables and yet outperforms more complicated models.43

The HF-ACTION predictive risk score model, like the R-hf risk score, is one of the risk scores that predicts mortality in HFrEF patients.9 The variables chosen in this case were also not user-friendly. It focused mostly on ambulatory HF patients, whereas our score was applied to AHF patients. They used blood urea nitrogen (BUN) as a potent variable, whereas we used eGFR, which is more accurate than BUN or creatinine alone in predicting outcomes.43–47

There are also important differences between the R-hf risk score and other published scores.26 The ESCAPE risk model and discharge score also selected ambulatory HF patients and excluded those patients who had baseline characteristics, which predicts worse outcomes.11 The R-hf risk score, by contrast, includes all patients who had AHF. Moreover, the development cohort for the CORONA prognostic risk model had older patients averaging 72 years, whilst other risk models had a lower age. Scores from the Seattle and CHARM models did not incorporate NT pro-BNP, which has been shown to improve risk stratification.12 Lee et al,17 clinical model for predicting mortality did not incorporate EF in the variables utilized for risk prediction. Moreover, BUN was the renal variable while eGFR was used in the R-hf risk score. Previous studies have reported more accurate risk prediction using eGFR.44–48

The major strength of this study is the inclusion of HF patients from seven countries based in the Middle East. Moreover, the current dataset was built from a registry, which could lead to bias as potential confounders such as iron levels and history of chronic anemia were not available for incorporation into the multivariable models. The impact of the R-hf score was only examined in AHF patients from the Middle Eastern nations. Multi-modality machine learning approach for risk stratification in HF is emerging,49 and further studies are needed to examine whether this score has similar predictive values for cohorts with other ethnicities. Finally, only HFrEF patients are eligible for this score and needs to be validated for other HF subtypes, such as HF with midrange, preserved, or recovered ejection fraction.50–53

The results of the current study may not be completely generalizable since just a few hospitals in various countries participated in the registry. This study was also unable to determine reasons for the underuse of drugs or procedures. The measurement of natriuretic peptides was optional because they are not routinely measured in all countries. There was no centralized evaluation of echocardiographic interpretation; it was left to the discretion of the person performing the investigation. The renal function of patients at discharge is unclear, and there are no statistics on the number of patients who improve their renal function.

This study did not record the cognitive and disability status of stroke patients, which has a statistically significant impact on mortality and morbidity. Because mortality rates at follow-up were only gathered at three-month and one-year intervals without the particular date of death of each patient, Kaplan–Meier curves could not be produced. Future research must address these limitations.


In AHF patients, the R-hf risk score is accurate and useful in predicting three- and 12-month mortality. Further investigation is needed to substantiate these findings and to determine the impact of the R-hf score on HF treatment strategies and outcomes. It may be best to apply the score to cohorts from diverse geographical areas for good validation.


The authors declared no conflicts of interest. No funding was received for this study.


Gulf CARE is an investigator-initiated study sponsored by the Gulf Heart Association and supported by Servier, Paris, France, and the Saudi Heart Association (for centers in Saudi Arabia). The patient’s informed consent was obtained, as well as ethical committee approval.


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