A New Risk Score to Predict In-Hospital
Mortality in Elderly Patients With Acute
Heart Failure: On Behalf of the Journey
HF-TR Study Investigators
Gu¨lay Go¨k, MD
1, Mehmet Karadag˘, PhD
2,
U
¨ mit Yas¸ar Sinan, MD
3, and Mehdi Zoghi, MD
4Abstract
We aimed to predict in-hospital mortality of elderly patients with heart failure (HF) by using a risk score model which could be easily applied in routine clinical practice without using an electronic calculator. The study population (n¼ 1034) recruited from the Journey HF-TR (Patient Journey in Hospital with Heart Failure in Turkish Population) study was divided into a derivation and a validation cohort. The parameters related to in-hospital mortality were first analyzed by univariate analysis, then the variables found to be significant in that analysis were entered into a stepwise multivariate logistic regression (LR) analysis. Patients were classified as low, intermediate, and high risk. A risk score obtained by taking into account the regression coefficients of the significant variables as a result of the LR analysis was tested in the validation cohort using receiver operating characteristic curve analysis. In total, 6 independent variables (age, blood urea nitrogen, previous history of hemodialysis/hemofiltration, inotropic agent use, and length of intensive care stay) associated with in-hospital mortality were included in the analysis. The risk score had a good discrimination in both the derivation and validation cohorts. A new validated risk score to determine the risk of in-hospital mortality of elderly hospitalized patients with HF was developed by including 6 independent predictors.
Keywords
elderly, heart failure, risk score, in-hospital mortality
Introduction
Heart failure (HF) is related to a higher mortality rate in elderly patients.1A large population–based observational study found that the incidence of HF approaches 10/1000 population after the age of 65 years.2A previous study found that the cumulative 3-year mortality rates may reach up to 57.2% in patients aged 80 years.3
Despite the presence of high mortality and hospita-lization rates among these patients, there is no specific risk model that could be used in clinical practice to help objective clinical decision-making in this growing elderly population.4,5 Predicting mortality rates and identifying high-risk groups are important for selecting treatment strategies.
In this study, we aimed to identify predictors of in-hospital mortality for elderly patients with HF and to produce a validated risk score which is easy to apply at the bedside.
Methods
Journey HF-TR (Patient Journey in Hospital with Heart Failure in Turkish Population) was a multicenter and observational study, which recruited patients from 37 centers from Turkey between September 2015 and September 2016.6In this study,
the in-hospital outcomes of 1034 patients (aged >65 years) were evaluated. Demographic characteristics, medical history, med-ication usages as well as clinical findings on admission, treat-ments received in the emergency department, procedures performed during hospital admission, echocardiographic findings, laboratory parameters, length of hospital stay, and in-hospital outcomes were recorded. The study included hospi-talized patients with acute HF. Acute decompensation of chronic heart failure (ADCHF) was defined as the worsening of HF in patients with a previous diagnosis or hospitalization for HF. De novo AHF was defined as AHF in patients with no history
1Department of Cardiology, Faculty of Medicine, Medipol University, Istanbul, Turkey
2Department of Biostatistics, Faculty of Medicine, Hatay Mustafa Kemal University, Hatay, Turkey
3
Department of Cardiology, Institute of Cardiology, _Istanbul University-Cerrahpasa, Istanbul, Turkey
4
Department of Cardiology, Faculty of Medicine, Ege University, Izmir, Turkey Corresponding Author:
Gu¨lay Go¨k, Department of Cardiology, Faculty of Medicine, Medipol Univer-sity, Hatay, Turkey.
Email: glygk84@gmail.com
Angiology
2020, Vol. 71(10) 948-954 ªThe Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0003319720941758 journals.sagepub.com/home/ang
of HF. Patients were classified as decompensated HF, cardio-genic shock, pulmonary edema, hypertensive HF, and right ven-tricular HF as described in the latest European Society of Cardiology (ESC) guidelines on HF.7,8 According to World Health Organization, the standardized definitions were used for patients’ past medical history including hypertension (HT), dia-betes mellitus (DM), coronary artery disease (CAD), atrial fibrillation (AF), hyperlipidemia (HL), cerebrovascular disease (CVD), chronic renal failure (CRF), and anemia. The estimated glomerular filtration rate was calculated by using the Modifica-tion of Diet in Renal Disease formula.9Clinical presentations were determined and classified as proposed by the ESC HF guideline.10The study was approved by the ethic committee of the Istanbul Haydarpasa Numune Training and Research Hospital.
Statistical Analysis
Patients over 65 years old were randomly divided into deriva-tion and validaderiva-tion cohorts. Of 1048 patients, 702 (67%) were included in the derivation cohort and 346 (33%) into the vali-dation cohort. Demographics, clinical findings, treatment stra-tegies as well as laboratory parameters in the derivation data set were firstly analyzed by univariate logistic regression (LR) analysis and then the variables found to be significant were analyzed by stepwise multivariate LR analysis to determine independent predictors of in-hospital mortality. Risk scores obtained by taking the regression coefficients (b) were ana-lyzed by stepwise multivariate LR analysis.
The parameters found to be significant in the stepwise multi-variate LR model are primarily determined by the receiver oper-ating characteristic method. The cutoff values were obtained, then reclassification success (sensitivity, specificity, and area under the curve) was calculated by using the validation data set. Patients were classified as low-, intermediate-, and high-risk
group in both the derivation and validation data sets. Compari-son of mortality rates in the derivation and validation samples were analyzed by the Pearson w2test. Descriptive statistics were expressed as mean + SD for numerical variables, and number and % for categorical variables. SPSS Windows version 24.0 package program was used for statistical analysis and a 2-sided P < .05 was considered significant.
Results
Patient Characteristics
The mean age of 1034 elderly patients was 75.2 + 7 years and 51.3% were males. In-hospital mortality rate was 7.4%. In the present study, 70.7% of patients had a left ventricle ejection fraction (LVEF) 40%. The prevalence for HT, DM, CAD, AF, and CRF were 71.4%, 40.2%, 61.5%, 42.9%, and 30.5%, respectively. Diuretics and b-blockers were the most common medications. The majority of patients were admitted with a diagnosis of ADCHF (80.9%). The most common precipitating factor for hospitalization was an infection, which accounted for 30.2% of all cases. The other precipitating factors for hospita-lization were arrhythmias (26.7%), acute coronary syndrome (21.4%), and acute renal failure (24.4%). The majority of patients were admitted to intensive care units (ICUs) and received an intravenous bolus of loop diuretics in the emer-gency department. In the study, 16.1% of patients received inotrope infusion, 8% of patients were mechanically ventilated, 18.7% of patients were noninvasively ventilated in the ICUs, and 5.4% of patients received hemodialysis (HD)/ultra-filtration hemodialysis (UF-HD). The mean length of stay in ICU was 4.2 + 4.5 days. Table 1 shows the baseline demo-graphic characteristics, medications, laboratory values as well as the clinical and echocardiographic findings of both the deri-vation and validation cohorts.
Table 1. Demographic and Clinical Characteristics of all Patients.a
Derivation (n¼ 702) Validation (n¼ 346) Age, years 75.41 + 7.00 74.93 + 6.94 Male n (%) 349 (49.7) 190 (45.1) Smoking, n (%) 161 (22.9) 64 (18.5) Hypertension, n (%) 501 (71.4) 253 (73.1) Diabetes mellitus, n (%) 282 (40.2) 152 (43.9)
Coronary artery disease, n (%) 432 (61.5) 215 (62.3)
Atrial fibrillation, n (%) 301 (42.9) 158 (45.7)
Hyperlipidemia, n (%) 187 (26.6) 92 (26.6)
Cerebrovascular disease, n (%) 86 (12.3) 42 (12.1)
Peripheral artery disease, n (%) 48 (6.8) 19 (5.5)
Chronic renal failure 214 (30.5) 106 (30.6)
Depression (%) 111 (15.9) 53 (15.3)
Device implantation, n (%) 105 (15) 34 (9.8)
Medications
Acetyl salicylic acid, n (%) 436 (62.4) 216 (62.6)
ACE inh./ARB, n (%) 262 (37.5) 150 (43.4)
Aldosterone antagonist, n (%) 243 (34.7) 121 (35.2)
b-blocker, n (%) 484 (69.1) 248 (71.7)
Table 1. (continued)
Derivation (n¼ 702) Validation (n¼ 346)
Digoxin n (%) 137 (19.6) 72 (20.8)
Diuretic, n (%) 487 (69.6) 250 (72.5)
Clinical findings on admission
Body mass index, kg/m2 27.4 + 5.0 28.8 + 15.6
Systolic blood pressure, mm Hg 130 + 32 129 + 30
Heart rate, beat per minute 94 + 24 95 + 25
SO2 90.0 + 7.1 89.3 + 11.3 NYHA classification (3-4), n (%) 539 76.7 274 79.2 Clinical Presentations Cardiogenic shock, n (% 24 (3.4) 9 (2.6) Pulmonary edema, n (%) 228 (32.5) 113 (32.7) AD-HF, n (%) 567 (80.9) 279 (80.6) Hypertensive HF, n (%) 109 (15.5) 57 (16.5) Right HF, n (%) 186 (26.5) 105 (30.4)
Acute coronary syndrome, n (%) 103 (14.7) 50 (14.5)
De nova (new-onset HF), n (%) 116 (16.5) 48 (13.9) Laboratory data BUN, mg/dL 48.5 + 39.0 46.5 + 37.1 Creatinine, mg/dL 1.36 + 0.77 1.3 + 0.69 eGFR, mL/min/1.73 m2 47.1 + 27.07 47.94 + 27.84 Hemoglobin, mg/dL 11.9 + 2.1 12.2 + 2.0 ALT, U/L b22 (2-895) b22 (4-905) AST, U/L b25 (4-1020) b25 (3-980) LDH, U/L b266 (1-3193) b259 (0-3220) Albumin, mg/dL 3.49+0.60 3.49 + 0.62 Anemia, n (%) 283 (40.4) 190 (57.1) BNP/pro-BNP, ng/mL b4189 (51-35 000) b3419 (231-35 000) Uric acid, mg/dL 7.7 + 2.9 7.6 + 2.9 Electrocardiography findings LBBB, n (%) 135 (19.2) 88 (25.5) QRS>120 ms, n (%) 209 (29.8) 117 (33.8) Atrial fibrillation, n (%) 301 (42.9) 158 (45.7)
In-hospital medical treatments
Continuous infusion of diuretic, n (%) 320 (46.2) 167 (49.0)
Intermittent bolus of diuretic, n (%) 403 (58.5) 192 (56.6)
Inotrope use, n (%) 113 (16.1) 61 (17.6)
Mechanic ventilation, n (%) 56 (8.0) 29 (8.4)
Noninvasive ventilation, n (%) 128 (18.7) 49 (14.5)
HD/UF-HD, n (%) 38 (5.4) 11 (3.2)
Vasodilator use, n (%) 212 (30.2) 110 (31.8)
Length of ICU stay, day 4.2 + 4.1 4.2 + 5.0
Length of cardiology ward stay, day 3.1 + 4.8 3.3 + 5.6
Echocardiography findings
Left atrium diameter, mm 37.7 + 17.7 36.9 + 17.9
Moderate-severe MR, n (%) 367 (54.1) 176 (52.2)
Moderate-severe TR, n (%) 343 (51.0) 154 (45.7)
Moderate-severe AS, n (%) 53 (7.8) 21 (6.2)
Moderate-severe AR, n (%) 60 (8.9) 29 (8.6)
LVEF40, n (%) 17 (2.4) 12 (3.5)
Pulmonary systolic hypertension, n (%) 385 (57.1) 185 (55.1)
Precipitant factors
Acute decompensation, n (%) 448 (66.9) 216 (66.1)
Acute coronary syndrome, n (%) 150 (21.4) 70 (20.2)
Infection, n (%) 212 (30.2) 99 (28.6)
Arrhythmia, n (%) 187 (26.7) 94 (27.2)
Uncontrolled hypertension, n (%) 152 (21.7) 63 (18.2)
Acute renal failure, n (%) 171 (24.4) 87 (25.1)
Non-compliance, n (%) 193 (27.5) 95 (27.5)
Exitus, n (%) 54 (7.7) 28 (8.1)
a
Mean + SD are given for quantitative variables. bMedian (min-max) (%) are given for qualitative variables.
Abbreviations: ACE, angiotensinogen converting enzyme; AD, acute decompensated; ALT, alanine aminotransferase; AR, aortic regurgitation; ARB, angiotensino-gen receptor blocker; AS, aortic stenosis; AST, aspartate aminotransferase; BNP, brain natriuretic peptide; BUN, blood urea nitroangiotensino-gen; eGFR, estimated glomerular filtration rate; HD, hemodialysis; HF, heart failure; ICU, intensive care unit; IQR Interquartile range; LBBB, left bundle branch block; LDH, lactate dehydrogenase; LVEF, left ventricle ejection fraction; MR, mitral regurgitation; NYHA, New York Heart Association Classification; SO2, saturation; TR, tricuspid regurgitation; UF, ultra-filtration.
Predictors of Mortality
In the derivation cohort, variables associated with in-hospital mortality were determined using univariate and stepwise multi-variate LR analysis (Table 2). After stepwise multimulti-variate LR analysis, 6 variables were significant: age, anemia, inotropic agent use, HD/UF-HD, uncontrolled HT, and length of ICU stay were significant risk factors in the model (P < .05). The cut-off value was determined for quantitative risk factors (age ¼ 77.5 years and the length of ICU stay >6.5 days).
Risk Score
Age >77.5 years, anemia, inotropic agent use, HD/UF-HD, uncontrolled HT, and the length of ICU stay >6.5 days were included in the risk scoring according to multivariate LR anal-ysis. Scoring was performed using the previous studies11-14; regression coefficients (b) from the stepwise multivariate LR model were used for scoring. Scoring was done as follows: 1 for the variable with a regression coefficient between 0 and 1, 2 for the variable with a regression coefficient between 1 and 1.5, 3 for variables with a regression coefficient of 1.5 and above. The optimal cutoff point of each prognostic factor, its predictive
ability, and risk score model for predicting in-hospital mortality on the validation data set is shown in Table 3.
The new risk score was calculated for each patient and dis-played in Table 4. When we divide the sample size into 3 groups; the number of patients at low risk (0-1) was 459, those at mod-erate risk (2-4) was 475, and those at high-risk levels (>5) was 114.
Table 2. Univariate and Stepwise Multivariate Logistic Regression Analysis of Predictors of in-Hospital Mortality Obtained From the Derivation Data Set.
Univariate Stepwise multivariate
Odds ratio (95% CI) P value Odds ratio (95% CI) b P value Age 1.021 (1.002-1.062) .040 1.048 (1.001-1.098) 0.047 .044 Male 0.975 (0.557-1.707) .929
Smoking 1.024 (0.525-1.999) .944 Hypertension 2.016 (1.140-3.564) .016 Coronary artery disease 1.641 (0.885-3.046) .116 Atrial fibrillation 1.424 (0.813-2.495) .217 Cerebrovascular disease 2.542 (1.299-4.975) .006 Chronic renal failure 2.192 (1.245-3.858) .007 NYHA classification 1.136 (0.145-8.948) .902 Cardiogenic shock 8.591 (3.559-20.731) .001 Systolic blood pressure 0.978 (0.967-0.988) .001 Pulmonary edema 2.242 (0.953-5.275) .064 Hypertensive HF 0.097 (0.013-0.707) .021 BUN mg/dl 1.013 (1.007-1.018) .001 eGFR < 30% 1.956 (1.102-3.471) .022 Anemia 2.221 (1.165-4.234) .015 2.219 (1.097-4.487) 0.797 .026 Inotrope use 6.176 (3.444-11.075) .001 5.095 (2.687-9.659) 1.628 .001 HD/UF-HD 9.724 (4.644-20.359) .001 4.752 (1.972-11.451) 1.559 .001 LVEF of40 0.649 (0.365-1.154) .141 IV continuous diuretic 1.386 (0.725 2.649) .323
Length of ICU stay 1.109 (1.059-1.161) .001 1.064 (1.007-1.125) 0.062 .027 Acute decompensation 1.895 (0.952-3.768) .069
Acute coronary syndrome 1.379 (0.726-2.618) .326 Infection 2.120 (1.197-3.753) .010 Arrhythmia 1.244 (0.673-2.300) .485
Uncontrolled Hypertension 0.204 (0.063-0.665) .008 4.206 (1.240-14.256) 1.436 .021 Acute renal failure 2.962 (1.665-5.267) .001
Abbreviations: BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; HD, hemodialysis; HF, heart failure; ICU, intensive care unit; LVEF, left ventricle ejection fraction; NYHA, New York Heart Association; UF, ultra-filtration.
Table 3. Optimal Cutoff Point of Each Prognostic Factor, Its Predic-tive Ability, and Risk Score Model for Predicting In-Hospital Mortality on the Validation Data Set.
Sensitivity Specificity AUC [%95 CI] Score Age > 77.50 56.3 68.4 0.62 [0.50-0.74] 1 Anemia 54.3 68.4 0.56 [0.44 0.68] 1 Inotrope use 40.1 84.2 0.62 [0.50 0.75] 3 HD/UF-HD 22.0 90.4 0.55 [0.42 0.67] 3 Length of ICU stay >6.5 44.0 87.3 0.58 [0.43 0.74] 1 Uncontrolled Hypertension 23.4 81.6 0.49 [0.35 0.59] 2
Abbreviations: AUC, area under the receiver-operator characteristic curve; BUN, blood urea nitrogen; HD, hemodialysis; ICU, intensive coronary care unit; UF, ultra-filtration.
There was a significant relationship between the groups and mortality rates (P < .001). As the risk rose, the non-survivor rate significantly increased. Survivor and non-survivor rates were similar between the validation and derivation samples (P¼ .785; Table 5).
The mortality rate of low, moderate, and high groups were 3.8 versus 3.7%; 8.9 versus 7.4%; 30.4 versus 32.9%, respectively, in the derivation and validation samples (Figure 1).
Discussion
This study included detailed hospitalization characteristics of a large population of elderly patients with HF. We developed a
simple risk score using this observational database, which can be used in clinical practice at the bedside to predict in-hospital mortality for hospitalized elderly patients with HF.
Age was a strong predictor of mortality in our study as shown in most of the studies, such as the Acute Decompensated Heart Failure National Registry (ADHERE) database and in the in the Organized Program to Initiate Lifesaving Treatment in Hospita-lized Patients with Heart Failure (OPTIMIZE-HF) study.11-13In the OPTIMIZE-HF study, serum creatinine, systolic blood pres-sure, and age were observed as the strongest risk factors associ-ated with mortality. In our study, age >77.5 years was an independent discriminatory parameter for in-hospital mortality, which is consistent with the OPTIMIZE-HF and ADHERE study. The use of inotrope infusion in the ICUs was an independent discriminative factor for in-hospital mortality. Receiving ino-trope infusion was analyzed in the EuroHeart Failure Survey II (EHFS II) study where inotropes were more frequently used in cardiogenic shock patients. However, in contrast to our study, this was not considered as a prognostic factor in EHFS II.15
Some studies16have demonstrated that patients with ADCHF who received extracorporeal ultrafiltration had poor survival outcomes. It was thought that this finding might be due to diure-tic resistance. Nevertheless, HD/UF-HD has not been consid-ered as a candidate variable in predicting in-hospital mortality in most of the survival models.12,13In the present study, HD/UF-HD requirement was a strong predictor of in-hospital mortality. The effect of LVEF on short-term mortality is controversial. Some studies suggest that LVEF might not be a predictive of short-term mortality.17,18In our study, we classified patients according to LVEF, but the LVEF was not different between the survival and non-survival group, which might support those suggestions.
The mean length of stay in the ICU in our study was inde-pendently associated with in-hospital mortality. Prolonged ICU stay was associated with worse outcomes. In-hospital mortality was higher in our study (7.4%) compared with the OPTIMIZE-HF13(3.8%) and the ADHERE12 studies (4%). Also, in our study, the length of hospital stay was associated with higher mortality.19
In general, previous studies did not specifically evaluate in-hospital mortality of elderly populations. So, there are not many study specific for elder patients to compare our results. Elderly patients have different clinical characteristics, comorbidities, risk factors, and in-hospital mortality rates compared with younger patients. In the present research, patients were stratified according to risk groups by using 6 statistically significant para-meters (age, anemia, inotropic agent use, HD/UF-HD, uncon-trolled HT, and the length of ICU stay). These parameters were found to have a discriminative ability to identify the mortality risk of elderly patients. We observed that in-hospital mortality was greater in the high-risk score group. Although it was not possible to validate this new score externally, internal validation demonstrated similar in-hospital mortality rates both in the deri-vation and validation cohorts. The main advantage of our risk score is that it is a practical, simple, and can be applied at the bedside. Also, our model can be used in routine clinical practice without using an electronic calculator.
Table 4. Evaluation of Each Patient According to the New Risk score.a
Risk score Number % Cumulative (%)
0 152 14.5 14.7 1 307 29.3 44,5 2 212 20.2 65 3 159 15.2 80.4 4 104 9.9 90.5 5 47 4.5 95.1 6 19 1.8 96.9 7 19 1.8 98.7 8 9 0.9 99.6 9 4 0.4 100
aAccording to this table, the number of patients was gradually declining as the risk score increased. The percentage of high-risk patients were less than the percentage of low-risk patients. As the risk score gradually increased, the number of patients decreased.
Table 5. Mortality Rate of Patients Classified as Low, Moderate, and High Risk.a
Survival Non-survival
n % n %
Risk group Low 597 97.2 17 2.8 Moderate 347 89.0 43 11.0 High 14 46.7 16 53.3 aw2 ¼ 119.811; P .001. 3.8 8.9 30.4 3.7 7.4 32.9 0 5 10 15 20 25 30 35
Low Moderate High
M o rt a lit y (% ) derivation validation
Figure 1. The mortality rate of low, moderate, and high groups both in the derivation and validation data set.
Study Limitations
Although this was a multicenter study and it included a large group of elderly patients, the major limitation was that it only evaluated in-hospital mortality and did not include results after discharge from the hospital. Second, the data were dependent on the accuracy of medical records. So, there may be unmeasured or undocumented variables which may have influenced our find-ings. Finally, the data were not validated in a separate group of patients, thus further investigation is necessary.
Conclusion
In this study, a simple and practical risk score model was devel-oped that can be easily applied at the bedside to predict in-hospital mortality of elderly patients with HF.
Appendix A
Collaborators: Dogac Caglar Gurbuz, MD1, Oguzhan Celik, MD2, Huseyin Altug Cakmak, MD3, Salih Kilic MD4, Sinan Inci, MD5, Gulay Gok, MD6, Mehmet Erturk, MD7, Erkan Yil-dirim, MD8, Duygu Kocyigit, MD9, Ilgın Karaca, MD10, Faruk Ertas¸, MD11, Ahmet C¸ elik, MD12, Fatih Aksoy, MD13, Hasan Ali Gumrukcuoglu, MD14, Umit Yuksek, MD15, Mahir Cengiz, MD16, Emre Arugaslan, MD17, Mustafa Kursun, MD18, Ali Coner, MD19, Ozlem Ozcan Celebi, MD20, Cengiz Ozturk, MD21, Onur Dalgic, MD22, Nurullah Cetin, MD22, Ebru Ipek Turkoglu, MD23, Hatice Kemal, MD24, Emine Gazi, MD25, Cihan Altin, MD26, Servet Altay, MD27, Murat Meric, MD28, Ozgen Safak, MD29, Murathan Kucuk, MD30, Alper Kepez, MD31, Ozcan Vuran, MD32, Hakki Kaya, MD33, Mehmet Serdar Kucukoglu, MD34, Ahmet Ekmekc¸i35, Benay O¨ zbay36, Filiz Akyıldız Akc¸ay37, Lu¨tfu¨ Bekar38, Yavuzer Koza39, Ismail Bolat40, and Umut Kocabas¸41
1
Department of Cardiology, Izmir Katip Celebi University Ataturk Training and Research Hospital, Izmir, Turkey.
2
Department of Cardiology, Hitit University School of Medicine, Corum, Turkey.
3
Department of Cardiology, Mustafakemalpasa State Hospital, Bursa, Turkey.
4
Department of Cardiology, Nizip State Hospital, Gazian-tep, Turkey.
5
Department of Cardiology, Aksaray State Hospital, Aksaray, Turkey.
6
Department of Cardiology, Mardin State Hospital, Mardin, Turkey.
7
Departsment of Cardiology, Istanbul Mehmet Akif Ersoy Thoracic, Cardiac and Vascular Surgery Training and Research Hospital, Istanbul, Turkey.
8
Department of Cardiology, Erzurum Regional Training and Research Hospital, Erzurum, Turkey.
9
Department of Cardiology, Hacettepe University School of Medicine, Ankara, Turkey.
10
Department of Cardiology, Firat University School of Medicine, Elazig, Turkey.
11
Department of Cardiology, Dicle University School of Medicine, Diyarbakir, Turkey.
12
Department of Cardiology, Mersin University School of Medicine, Mersin, Turkey.
13
Department of Cardiology, Dinar State Hospital, Afyon-karahisar, Turkey.
14
Department of Cardiology, Lokman Hekim Hospital, Van, Turkey.
15
Department of Cardiology, Izmir Odemis State Hospital, Izmir, Turkey.
16
Department of Cardiology, Istanbul University Cerrah-pasa School of Medicine, Istanbul, Turkey.
17
Department of Cardiology, Sinop Ataturk State Hospital, Sinop, Turkey.
18
Department of Cardiology, Izmir Tepecik Training and Research Hospital, Izmir, Turkey.
19
Department of Cardiology, Baskent University Alanya Hospital, Antalya, Turkey.
20
Department of Cardiology, Turkey Yuksek Ihtisas Train-ing and Research Hospital, Ankara, Turkey.
21
Department of Cardiology, Gulhane Training and Research Hospital, Ankara, Turkey.
22
Department of Cardiology, Izmir Karsiyaka State Hospi-tal, Izmir, Turkey.
23
Department of Cardiology, Izmir Kemalpasa State Hospi-tal, Izmir, Turkey.
24
Department of Cardiology, Near East University, School of Medicine, Girne, Cyprus.
25
Department of Cardiology, Canakkale Onsekiz Mart University, School of Medicine, Canakkale, Turkey.
26
Department of Cardiology, Baskent University Izmir Hospital, Izmir, Turkey.
27
Department of Cardiology, Trakya University School of Medicine, Edirne, Turkey.
28
Department of Cardiology, Ondokuz Mayıs University, School of Medicine, Samsun, Turkey.
29
Department of Cardiology, Burdur State Hospital, Burdur, Turkey.
30
Department of Cardiology, Akdeniz University School of Medicine, Antalya, Turkey.
31
Department of Cardiology, Marmara University School of Medicine, Istanbul, Turkey
32
Department of Cardiology, Alasehir State Hospital, Man-isa, Turkey.
33
Department of Cardiology, Sivas Cumhuriyet University, School of Medicine, Sivas, Turkey.
34
Department of Cardiology, Istanbul University Institute of Cardiology, Istanbul, Turkey.
35
Department of Cardiology, Dr. Siyami Ersek Thoracic, Cardiac and Vascular Surgery Training and Research Hospital, _Istanbul-Turkey.
36
Department of Cardiology, Faculty of Medicine, Ege Uni-versity, _Izmir-Turkey.
37
Department of Cardiology, Faculty of Medicine, Izmir Katip C¸ elebi University, Atatu¨rk Training and Research Hos-pital, _Izmir-Turkey.
38
Department of Cardiology, Faculty of Medicine, Hitit University, C¸ orum-Turkey.
39
Department of Cardiology, Faculty of Medicine, Atatu¨rk University, Erzurum-Turkey.
40
Department of Cardiology, Fethiye State Hospital, Muˇgla-Turkey.
41
Department of Cardiology, Soma State Hospital, Manisa-Turkey.
Authors’ Note
All authors contributed to (1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and (3) final approval of the version to be published.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-ship, and/or publication of this article.
ORCID iD
Gu¨lay Go¨k https://orcid.org/0000-0003-0205-1138
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