==== Front J Saudi Heart AssocJ Saudi Heart AssocJournal of the Saudi Heart Association1016-73152212-5043Elsevier S1016-7315(17)30165-310.1016/j.jsha.2018.06.001ArticleThe prognostic impact of hyperglycemia on clinical outcomes of acute heart failure: Insights from the heart function assessment registry trial in Saudi Arabia Aljohar Alwaleed aAlhabib Khalid F. bKashour Tarek bHersi Ahmad bAl Habeeb Waleed bUllah Anhar bElasfar Abdelfatah cAlmasood Ali dGhabashi Abdullah eMimish Layth fAlghamdi Saleh gAbuosa Ahmed hMalik Asif iHussein Gamal Abdin jAl-Murayeh Mushabab kAlFaleh Hussam halfaleh@ksu.edu.sab⁎a Department of Internal Medicine, King Saud University Medical City, King Saud University, Riyadh, Saudi ArabiaaSaudi Arabiab Department of Cardiac Sciences, King Fahad Cardiac Center, College of Medicine, King Saud University, Riyadh, Saudi ArabiabSaudi Arabiac Department of Cardiovascular Medicine, King Salman Heart Center, King Fahd Medical City, Riyadh, Saudi ArabiacSaudi Arabiad Adult Cardiology Department, Prince Sultan Cardiac Center, Riyadh, Saudi ArabiadSaudi Arabiae Adult Cardiology Department, Prince Sultan Cardiac Center, Hafouf, Saudi ArabiaeSaudi Arabiaf Department of Internal Medicine /Cardiovascular Unit, King Abdulaziz University Hospital, Jeddah, Saudi ArabiafSaudi Arabiag Department of Cardiology, Madina Cardiac Center, AlMadina AlMonaoarah, Saudi ArabiagSaudi Arabiah Department of Cardiology, National Guard Hospital, Jeddah, Saudi ArabiahSaudi Arabiai Department of Cardiology, King Fahad General Hospital, Jeddah, Saudi ArabiaiSaudi Arabiaj Department of Cardiology, North West Armed Forces Hospital, Tabuk, Saudi ArabiajSaudi Arabiak Department of Internal Medicine, Armed Forces Hospital Southern Region, Khamis Mushayt, Saudi ArabiakSaudi Arabia⁎ Corresponding author at: Department of Cardiac Sciences, King Fahad Cardiac Center, College of Medicine, King Saud University, Post Office Box 7805, Riyadh 11472, Saudi Arabia. halfaleh@ksu.edu.sa12 6 2018 10 2018 12 6 2018 30 4 319 327 23 12 2017 14 4 2018 1 6 2018 © 2018 The Authors2018This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Background The prognostic impact of hyperglycemia (HG) in acute heart failure (AHF) is controversial. Our aim is to examine the impact of HG on short- and long-term survival in AHF patients. Methods Data from the Heart Function Assessment Registry Trial in Saudi Arabia (HEARTS) for patients who had available random blood sugar (RBS) were analyzed. The enrollment period was from October 2009 to December 2010. Comparisons were performed according to the RBS levels on admission as either <11.1 mmol/L or ≥11.1 mmol/L. Primary outcomes were hospital adverse events and short- and long-term mortality rates. Results A total of 2511 patients were analyzed. Of those, 728 (29%) had HG. Compared to non-HG patients, hyperglycemics had higher rates of hospital, 30-day, and 1-year mortality rates (8.8% vs. 5.6%; p = 0.003, 10.4% vs. 7.2%; p = 0.007, and 21.8% vs. 18.4%; p = 0.04, respectively). There were no differences between the two groups in 2- or 3-year mortality rates. After adjustment for relevant confounders, HG remained an independent predictor for hospital and 30-day mortality [odds ratio (OR) = 1.6; 95% confidence interval (CI) 1.07–2.42; p = 0.021, and OR = 1.55; 95% CI 1.07–2.25; p = 0.02, respectively]. Conclusion HG on admission is independently associated with hospital and short-term mortality in AHF patients. Future research should focus on examining the impact of tight glycemic control on outcomes of AHF patients. Keywords Acute heart failureHEARTSHyperglycemiaMortalityRandom blood glucoseDisclosure: Authors have nothing to disclose with regard to commercial support. ==== Body Abbreviations ACSAcute Coronary Syndrome AHFAcute Heart Failure BMIBody Mass Index DBPDiastolic Blood Pressure DLDDyslipidemia DMDiabetes Mellitus EFEjection Fraction eGFREstimated Glomerular Filtration Rate HEARTSHeart Function Assessment Registry Trial In Saudi Arabia HGHyperglycemia HRHeart Rate HTNHypertension IHDIschemic Heart Disease IQRInterquartile Range RBSRandom Blood Sugar SBPSystolic Blood Pressure SDStandard Deviation 1 Introduction Acute heart failure (AHF) continues to be a burdensome problem to healthcare systems and is a leading cause of frequent hospitalizations and long-term medical care [1]. Multiple illnesses coexist with HF and influence its prognosis [2], [3], [4]. Diabetes mellitus (DM) is known as one of the most commonly associated comorbidities in HF patients with a prevalence ranging from 25% to 40% [5], [6]. Data from major HF registries indicate that DM worsens hospital outcomes and increases short-term mortality rates [6], [7], [8], [9], [10], [11]. Although the impact of DM on HF outcomes is known, the role of hyperglycemia (HG), whether new-onset or in the context of a preexisting DM, remains controversial [12], [13], [14], [15], [16], [17], [18], [19], [20]. Several reports have suggested a negative impact of HG on AHF mainly affecting hospital outcomes and overall survival [12], [13], [14], [15], [16], [17], [18], yet others have not shown similar findings [19], [20]. HG in acute coronary syndromes (ACS) has been widely investigated. The Diabetes Mellitus Insulin-Glucose Infusion in Acute Myocardial Infarction (DIGAMI) trial showed a survival benefit in ACS patients with tight glycemic control [21]. This was later confirmed in other major trials [22], [23], [24]. Currently, the 2013 American Heart Association/American College of Cardiology guidelines recommend targeting sugar levels <180 mg/dL [25]. Glycemic control has become an integral part of the standard management of ACS, however the impact of extrapolating this evidence across the spectrum of all cardiovascular diseases is yet to be determined. We sought to determine the relationship between HG and hospital adverse outcomes, as well as short- and long-term mortality rates in AHF patients using data from the Heart Function Assessment Registry Trial in Saudi Arabia (HEARTS). 2 Materials and methods HEARTS protocol has been described previously [26], [27]. Briefly, HEARTS is a prospective registry that enrolled 2609 consecutive patients with a primary admission diagnosis of AHF. Eighteen tertiary care centers in different regions of Saudi Arabia participated in this registry. Enrollment took place between October 2009 and December 2010, with clinical follow-up until January 2013. The definition of HF was according to the European Society of Cardiology guidelines for the diagnosis and treatment of acute and chronic HF [28]. The study was approved by the institutional review board at each participating hospital and complied with the Declaration of Helsinki. Patients were eligible for this analysis if baseline random blood sugar (RBS) values were available. The diagnosis of DM was based on medical records documentation, patient self-reporting, or if the patient was taking diabetic medications. Patients were labeled as having HG if their RBS was ≥11.1 mmol/L, according to the American Diabetes Association guidelines [29]. We described patients’ baseline characteristics, therapies, hospital course, and hospital mortality rates. Additionally, we obtained the vital status after 30 days, 1 year, 2 years, and 3 years following hospital discharge by a telephone interview and verified these data as needed using hospital records. 2.1 Statistical analysis Categorical data were summarized with absolute numbers and percentages. Numeric data were summarized with mean and standard deviation (SD) or median and interquartile range (IQR). Comparisons between different groups were performed using Chi-square test or Fisher’s exact for categorical variables and independent sample t test or Mann–Whitney U test for continuous variables. Kaplan–Meier analysis was applied to plot the cumulative survival and differences between curves were assessed using the log-rank test. We used logistic regression models to estimate unadjusted and adjusted odds ratios (OR) for mortality rates. We adjusted for age, sex, estimated glomerular filtration rate (eGFR), ACS, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), AHF type, ejection fraction (EF), dyslipidemia (DLD), anemia, hypertension (HTN), and DM. Logistic regression with interaction terms was used to test the statistical significance of the interaction between HG and other baseline factors. To estimate the strength of association in subgroups we used OR with 95% confidence intervals (CI). A two-sided p value <0.05 was considered statistically significant. All analyses were performed using SAS/STAT software, version 9.2 (SAS Institute Inc., Cary, NC, USA.) and R software (R Foundation for Statistical Computing, Vienna, Austria). 3 Results Of the 2609 patients enrolled in HEARTS, 2511 (96.2%) patients were eligible for the present analysis. A total of 1783 (71%) were in the non-HG group, while 728 (29%) had HG at baseline. HG patients were generally older and had higher BMIs. Further, they were more likely to be diabetic, hypertensive, and dyslipidemic (p < 0.001 for all comparisons; Table 1).Table 1 Baseline characteristics. Overall 2511 Non-HG 1783 (71%) HG 728 (29%) p Demographics Age 61.34 ± 15 60.65 ± 15.6 63.38 ± 12.4 <0.001 Saudi 2135 (85) 1521 (85.3) 614 (84.3) 0.539 Male 1653 (65.8) 1207 (67.7) 446 (61.3) 0.002 Body mass index 29.2 ± 6.7 29 ± 6.7 29.7 ± 6.7 0.028 Risk factors Diabetes mellitus 1629 (65.1) 955 (53.8) 674 (92.7) <0.001 Smoker/ex-smoker 844 (33.6) 614 (34.4) 230 (31.6) 0.171 Hypertension 1781 (71.4) 1195 (67.4) 586 (81.3) <0.001 Dyslipidemia 870 (36.8) 550 (32.5) 320 (47.8) <0.001 History of cardiovascular diseases Heart failure 1607 (64.2) 1179 (66.3) 428 (59) <0.001 Ischemic heart disease 1342 (54) 908 (51.6) 434 (59.9) <0.001 TIA/stroke 241 (9.6) 156 (8.8) 85 (11.7) 0.025 PAD 97 (3.9) 60 (3.4) 37 (5.1) 0.044 PCI 326 (13) 222 (12.5) 104 (14.3) 0.219 CABG 257 (10.3) 175 (9.8) 82 (11.3) 0.282 RHD 172 (6.9) 137 (7.7) 35 (4.8) 0.010 Other VHD 359 (14.4) 271 (15.3) 88 (12.2) 0.045 Atrial fibrillation 390 (15.6) 313 (17.6) 77 (10.6) <0.001 VT/VF 60 (2.4) 50 (2.8) 10 (1.4) 0.033 ICD 216 (8.6) 179 (10.1) 37 (5.1) <0.001 CRT 81 (3.2) 67 (3.8) 14 (1.9) 0.018 History of other chronic medical illnesses Anemia 1116 (44.6) 781 (44) 335 (46.2) 0.308 CKD on dialysis 70 (9.5) 45 (8.7) 25 (11.4) 0.256 CKD not on dialysis 668 (90.5) 473 (91.3) 195 (88.6) Chronic lung disease 179 (7.1) 131 (7.4) 48 (6.6) 0.505 Data are presented as n (%) or mean ± SD. CABG = coronary artery bypass grafting; CKD = chronic kidney disease; CRT = cardiac resynchronization therapy; HG = hyperglycemia; ICD = implantable cardioverter defibrillator; PAD = peripheral arterial disease; PCI = percutaneous coronary intervention; RHD = rheumatic heart disease; SD = standard deviation; TIA = transient ischemic attack; VF = ventricular fibrillation; VHD = valvular heart disease; VT = ventricular tachycardia. Compared with patients with HG, non-HG patients were more likely to have a history of HF, valvular heart diseases (rheumatic and nonrheumatic), arrhythmias (both atrial and ventricular), and to have undergone cardiac device implantation. However, vascular comorbidities such as ischemic heart disease (IHD), strokes/transient ischemic attacks, and peripheral arterial disease were significantly higher among patients with HG (Table 1). Table 2 demonstrates the types, etiologies, and exacerbating factors of AHF. HG patients were more likely to present with acute de novo HF while non-HG patients were more likely to present with acute on chronic HF (p < 0.001 for group comparison). IHD was the prime etiology for AHF in patients with HG, while nonischemic etiologies of AHF were seen more often in non-HG patients (p < 0.001 for group comparison). ACS and uncontrolled HTN were the main reasons for AHF exacerbation among HG patients, and had occurred more frequently compared to the non-HG group.Table 2 Heart failure types, etiologies, and exacerbating factors for acute heart failure. Overall 2511 Non-HG 1783 (71%) HG 728 (29%) p Acute heart failure type Acute de novo HF 904 (36) 604 (33.9) 300 (41.2) <0.001 Acute on Chronic HF 1607 (64) 1179 (66.1) 428 (58.8) Etiology Ischemic 1419 (56.5) 937 (52.5) 482 (66.2) <0.001 Nonischemic 1092 (43.5) 846 (47.4) 246 (33.8) HF exacerbation factors NSTACS 702 (28) 440 (24.7) 262 (36) <0.001 STEMI 266 (10.6) 164 (9.2) 102 (14) <0.001 Uncontrolled hypertension 506 (20.1) 332 (18.6) 174 (23.9) 0.003 Noncompliance to HF medications 523 (20.8) 403 (22.6) 120 (16.5) <0.001 Noncompliance to diet 628 (25) 493 (27.6) 135 (18.5) <0.001 Worsening renal failure 443 (17.6) 341 (19.1) 102 (14) 0.002 Arrhythmia 275 (10.9) 210 (11.8) 65 (8.9) 0.038 Infections 524 (20.9) 363 (20.4) 161 (22.1) 0.326 COPD exacerbation 94 (3.7) 74 (4.1) 20 (2.7) 0.093 Data are presented as n (%). COPD = chronic obstructive pulmonary disease; HF = heart failure; HG = hyperglycemia, NSTACS = non-ST elevation myocardial infarction; STEMI = ST-elevation myocardial infarction. Patients with HG had a higher mean baseline SBP (134.5 vs. 126.6, p < 0.001), higher rates of positive troponin levels (51.3% vs. 32.6%, p < 0.001), and a higher proportion of low eGFR defined as <60 mL/min/1.73 m2 (60.2% vs. 51.0%, p < 0.001). Non-HG patients were more likely to have severe left ventricular systolic dysfunction (50.6% vs. 39.7%; p < 0.001). Among the patients who underwent coronary angiogram during the same admission (n = 720), significant left main, three-vessel, and double-vessel disease were more frequently seen in patients with HG. Further comparisons in clinical presentations and baseline investigations are depicted in Table 3.Table 3 Clinical presentation and investigations. Overall 2511 Non-HG 1783 (71%) HG 728 (29%) p Hemodynamic parameters Systolic blood pressure 128.7 ± 31.3 126.6 ± 30.8 134.5 ± 32.5 <0.001 Diastolic blood pressure 74.1 ± 17.9 73.8 ± 18.2 75.5 ± 17.6 0.030 Heart rate 88.8 ± 21 87.9 ± 20.9 91.4 ± 21.3 <0.001 Lab results RBS (mmol/L), median (IQR) 8 (6) 7 (3) 15 (6) <0.001 Sodium (mmol/L) 135.1 ± 5.3 135.5 ± 5.2 134.2 ± 5.6 <0.001 BUN (μmol/L) 11.9 ± 9.1 11.7 ± 9.2 12.2 ± 8.8 0.288 Hemoglobin (g/dL) 12.4 ± 2.2 12.5 ± 2.21 12.4 ± 2.3 0.521 Creatinine (µmol/L) 109 (70) 108 (69) 118 (58) 0.197 Pro BNP (pmol/L), median (IQR) 675 (668) 705 (650) 664 (1095) 0.632 Troponin positive 848 (38) 516 (32.6) 332 (51.3) <0.001 eGFR < 60 1346 (53.7) 909 (51) 437 (60.2) <0.001 Electrocardiography Wide QRS duration 361 (14.4) 272 (15.3) 89 (12.3) 0.049 Left bundle branch block 288 (11.5) 197 (11.1) 91 (12.5) 0.300 LV systolic function by echocardiography Normal (EF > 55%) 332 (13.8) 224 (13.1) 108 (15.6) <0.001 Mild (EF = 40–55%) 321 (13.3) 202 (11.8) 119 (17.2) Moderate (EF = 30–39.9%) 611 (25.4) 420 (24.5) 191 (27.6) Severe (EF < 30%) 1141 (47.4) 866 (50.6) 275 (39.7) Coronary angiography (n = 720) LMD/TVD 255 (34.7) 166 (31.2) 89 (44.1%) 0.001 Significant double VD 113 (15.4) 70 (13.2) 43 (21.3) 0.006 Significant single VD 97 (13.2) 74 (13.9) 23 (11.4) 0.367 Nonsignificant CAD 79 (10.8) 60 (11.3) 19 (9.4) 0.465 Normal 176 (24) 153 (28.8) 23 (11.4) <0.001 Data are presented as n (%) or mean ± SD, unless otherwise indicated. BNP = brain natriuretic peptide; BUN = blood urea nitrogen; CAD = coronary artery disease; EF = ejection fraction; eGFR = estimated glomerular filtration rate; HG = hyperglycemia; IQR = interquartile range; LMD = left main disease; RBS = random blood sugar; SD = standard deviation; TVD = three vessel disease; VD = vessel disease. Hospital therapies and discharge medications are shown in Fig. 1. β-blockers and aldosterone antagonists use was higher in non-HG patients, both prior to hospital admission, and upon discharge, while aspirin and statin therapy were prescribed more frequently in HG patients upon discharge.Figure 1 Differences in evidence-based medical therapies used before admission (A) and at discharge (B). *p < 0.05. AA = aldosterone antagonists; ACEI = angiotensin converting enzyme inhibitors; ARB = angiotensin receptors blockers; BB = β-blockers. Hospital procedures, complications, as well as hospital, short-, and long-term mortality rates are shown in Table 4. Compared to HG patients, the non-HG group were more likely to receive device therapies (implantable cardioverter defibrillators and cardiac resynchronization therapy) and were less likely to require mechanical ventilation. Apart from a higher rate of hospital recurrence of AHF in patients with non-HG (33.1% vs. 28.2%; p = 0.015), there were no differences in the rate of hospital complications between the two groups.Table 4 Adverse hospital outcomes, 30-day, 1-, 2-, and 3-year mortality rates. Overall 2511 Non-HG 1783 (71%) HG 728 (29%) p Hospital procedures Dialysis 119 (4.7) 79 (4.4) 40 (5.5) 0.255 Ventilation 282 (11.2) 186 (10.4) 96 (13.2) 0.047 Intraaortic balloon pumps 84 (3.35) 54 (3) 30 (4.1) 0.167 Pacing 35 (1.4) 20 (1.1) 15 (2.1) 0.069 Hospital ICD 145 (5.8) 127 (7.1) 18 (2.5) <0.001 Hospital CRT 65 (2.6) 56 (3.1) 9 (1.2) 0.006 Hospital complications Recurrent CHF 796 (31.7) 591 (33.1) 205 (28.2) 0.015 Sepsis 186 (7.4) 124 (6.9) 62 (8.5) 0.175 Shock 218 (8.7) 147 (8.2) 71 (9.7) 0.223 Cardiogenic 162 (74.3) 113 (76.9) 49 (69) 0.271 Noncardiogenic 21 (9.6) 11 (7.5) 10 (14.1) Both 35 (16.1) 23 (15.6) 12 (16.9) AF requiring therapy 150 (6) 113 (6.3) 37 (5.1) 0.229 VT/VF 106 (4.2) 67 (3.8) 39 (5.4) 0.071 Major bleeding 35 (1.4) 26 (1.5) 9 (1.2) 0.667 TIA/stroke 45 (1.8) 29 (1.6) 16 (2.2) 0.328 All-cause mortality In-hospital mortality 163 (6.5) 99 (5.5) 64 (8.8) 0.003 30-d mortality 204 (8.1) 128 (7.2) 76 (10.4) 0.007 1 y mortality 487 (19.4) 328 (18.4) 159 (21.8) 0.049 2 y mortality 589 (23.5) 406 (22.8) 183 (25.1) 0.209 3 y mortality 607 (24.2) 416 (23.4) 191 (26.2) 0.127 Data are presented as n (%). AF = atrial fibrillation; CHF = congestive heart failure; CRT = cardiac resynchronization therapy; HG = hyperglycemia; ICD = implantable cardioverter defibrillator; TIA = transient ischemic attack; VF = ventricular fibrillation. VT = ventricular tachycardia. The observed hospital, 30-day, and 1-year mortality rates were significantly higher in patients with HG (8.8% vs. 5.5%; p = 0.003, 10.4% vs. 7.2%; p = 0.007, and 21.8 vs. 18.4; p = 0.049, respectively). There were no differences in the 2- and 3-year mortality rates between the two groups. After adjusting for important confounders, HG remained an independent predictor for hospital and 30-day mortality (OR = 1.61; 95% CI 1.07–2.42, p = 0.022, and OR = 1.55; 95% CI 1.07–2.25, p = 0.021, respectively), Table 5. A Kaplan–Meier plot comparing survival rates between the groups showed that patients with HG had significantly lower survival rates compared with patients with non-HG (log-rank test p = 0.038), Fig. 2.Figure 2 Survival analysis using Kaplan–Meier plots in hyperglycemics versus nonhyperglycemics. Table 5 Crude and adjusted odds ratios and 95% confidence intervals for short- and long-term mortality in acute heart failure. All-cause mortality Crude OR (95% CI) p Adjusted OR (95% CI) p Hospital mortality 1.64 (1.18–2.27) 0.003 1.61 (1.07–2.42) 0.022 30-d mortality 1.50 (1.11–2.02) 0.008 1.55 (1.07–2.25) 0.021 1 y mortality 1.24 (1.00–1.53) 0.049 1.25 (0.96–1.63) 0.100 2 y mortality 1.14 (0.93–1.39) 0.209 1.13 (0.88–1.45) 0.354 3 y mortality 1.17 (0.96–1.42) 0.127 1.17 (0.91–1.50) 0.209 CI = confidence interval; OR = odds ratio. The interaction between HG and mortality was assessed in several patient subgroups. Subgroups assessed included patients stratified by age (≥70 years vs. <70 years), sex (males vs. females), prior diagnosis of DM, use of insulin, HF etiology (ischemic vs. nonischemic), type of AHF (de novo vs. acute on chronic), eGFR (≥60 mL/min/1.73 m2 vs. <60 mL/min/1.73 m2), EF (≥40% vs. <40%), SBP (≥90 mmHg vs. <90 mmHg), and history of anemia. A significant interaction between HG and EF was observed, where the negative impact of HG on 30-day mortality was worse in patients with an EF < 40% (EF < 40%, OR = 1.69; 95% CI 1.18–2.42, p = 0.003, vs. EF ≥ 40%, OR = 0.72; 95% CI 0.37–1.39, p = 0.331, p value for interaction = 0.025). This interaction between HG and EF was not seen in hospital or 1-year mortality. Additionally, a strong interaction was observed between HG and anemia. Anemic patients with HG had a higher hospital mortality compared with nonanemic patients (anemia present, OR = 2.69; 95% CI 1.62–4.46, p < 0.001 vs. anemia absent, OR = 1.10; 95% CI 0.71–1.73, p = 0.66, p for interaction = 0.01). This interaction between anemia and HG also impacted short- and long-term mortality (data not shown). 4 Discussion To our knowledge, this is the first report from the Arab Middle East examining the impact of glycemic status on the outcomes of patients with AHF. We found that almost 30% of our patients had HG upon hospital admission. Irrespective of their diabetic status and other comorbidities, these patients had a worse prognosis. Data on the impact of HG on AHF outcomes are inconsistent [12], [13], [14], [15], [16], [17], [18], [19], [20]. Some reports have suggested that HG is independently associated with hospital [12], [13], [14], [15], [16], 30- [17], [18], and 60-day [14] mortality. However, this association with mortality was less robust in the long term [12], [13], [19], [20]. Conversely, other reports did not show an association between HG and short-term mortality [19], [20] but rather an association with long-term mortality [18]. Our data agree with the general pattern of these reports where HG is more likely to be related to short-term mortality. The discrepancy in the findings of these studies could be explained by the diverse methods and inclusion and exclusion criteria that were used, such as the exclusion of diabetic patients [14], [16], [19], using different blood sugar measurements (random and/or fasting) and cutoffs, or selecting patients under special circumstances such as AHF patients admitted to the intensive care only [15], [19]. Our subgroup analysis suggests an interaction between HG and an anemic status as well as with EF. The test of interaction is hypothesis generating and may suggest colinearity between HG and anemia on one hand, and HG and an EF < 40% on the other hand. Alternatively, anemia and a low EF such as HG simply reflect disease severity. Therefore, a risk score for AHF that combine all potential risk factors for worse prognosis is essential for targeted therapy and hospital disposition. In addition, the high readmission and mortality rates in AHF patients further necessitate conducting trials focusing on risk score designing and validation [30]. Indeed, there have been many proposed risk scores that correlate with hospital and postdischarge mortality [31], [32], [33]. However, none of them is implemented as a standard-of-care in current clinical practice. Whether HG in AHF serves as a marker of disease severity or a direct cause for adverse outcomes remains unclear. Some have suggested that chronic elevation of blood sugar as evident by an elevated HbA1c could cause direct injury to the myocardium [34]. In addition, persistent hyperglycemia (e.g., Type I DM) may lead to an insulin-resistant state [35] and impaired glucose uptake by the myocardium shifting the energy generation pathway towards utilization and oxidization of free fatty acids by the myocardium [36], [37] which in turn may promote arrhythmogenesis [38]. Finally, HG may impair the cardiac function through various mechanisms such as oxidative stress [39], [40], endothelial atherogenesis, and vascular inflammation [41]. However, HG in AHF can simply be stress-induced. The normal physiological response to stress insults leads to high glucose levels as a result of sympathetic nervous system activation and/or excessive release of stress hormones such as cortisol [42], [43]. The fact that HG seemed to be an independent predictor of short- rather than long-term mortality might support the premise that HG is merely a marker of severity rather than a direct cause of mortality. The clinical implications of our findings are numerous. Firstly, the measurement of RBS in the Emergency Department is simple and provides very useful information in predicting the hospital course and prognosis of AHF. Therefore, it can potentially be used as a tool amongst other tools for risk stratification in AHF patients. Secondly, HG in the context of AHF was found to be predictive of the development of new-onset DM [18]. Similar findings were observed in critically ill patients [44], and patients with ACS [45]. This should encourage treating physicians to screen patients with abnormal glucose levels for DM following the acute phase of HF. Finally, as HG is an independent predictor of short-term adverse outcomes in the context of AHF, this should raise interest in studies examining the efficacy of aggressive glycemic control on the outcomes of AHF patients. Despite the general recommendation by the American Diabetes Association to aim for strict glycemic control in any hospital admission regardless of the primary diagnosis [46], the evidence for this practice in AHF is weak. This study suffered from several limitations. Data on hospital readmission rates were not collected in the HEARTS registry, therefore, the impact of HG on AHF readmissions rates and postdischarge disease deterioration could not be assessed. In addition, the registry only recorded all-cause mortality, and thus we are unable to comment on the rates of cardiovascular mortality. Finally, HbA1c data were not collected systematically. Hence, we could not determine if HG is a new event or simply a reflection of an undiagnosed DM. 5 Conclusion Our study highlights the deleterious short-term prognostic impact of HG in AHF patients. Our findings should prompt the design of clinical trials addressing the impact of tight glycemic control in AHF on clinical outcomes. Acknowledgments HEARTS was financially cosponsored by Servier, AstraZeneca, and the deanship of scientific research at King Saud University, Riyadh, Saudi Arabia (Research group number: RG-1436-013). The sponsors had no role in data extraction or analyses, writing the manuscript, or the decision to submit the manuscript for publication. The registry was created under the auspices of the Saudi Heart Association. Peer review under responsibility of King Saud University. ==== Refs References 1 Yancy C.W. Jessup M. Bozkurt B. Butler J. Casey D.E. 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