Home Medicine Framingham Risk Score for the prediction of coronary artery disease in patients with chronic rheumatic heart disease
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Framingham Risk Score for the prediction of coronary artery disease in patients with chronic rheumatic heart disease

  • Yaowang Lin , Xuebiao Wei , Anping Cai , Xing Yang , Yingling Zhou and Danqing Yu EMAIL logo
Published/Copyright: June 10, 2014
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Diagnosis
From the journal Diagnosis Volume 1 Issue 3

Abstract

Background: Patients with chronic rheumatic heart disease (CRHD) have concomitant coronary artery disease (CAD), but the model to detect coexistent coronary artery stenosis prior to surgery has not been validated. Our study investigated whether the Framingham Risk Score (FRS) is a valid predictor of CAD in patients undergoing surgery for CRHD.

Methods: A total of 989 rheumatic patients were enrolled between 2005 and 2010. They were subdivided into two groups according to coronary angiography (CAG) results to identify potential factors in the development of CAD. Finally, all patients were assessed using the FRS to examine the association between the 10-year cardiovascular disease (CVD) risk and CAD.

Results: There were statistically significant inter-group differences in terms of age, sex, smoking, hypertension, bypass surgery and cardiac function in the New York Heart Association (NYHA) classification status (p<0.05). We showed that the FRS had high accuracy in predicting CAD in female and male patients with CRHD. In the male group, the area under the curve (AUC) for predicting CAD was 0.904, with a specificity of 90.3% and sensitivity of 76.1%. In the female group, the AUC for predicting CAD was 0.924, with a specificity of 77.5% and sensitivity of 90.9%, respectively. With a cut-off point of a 10-year CVD risk of 12.5 (%) in the male group and a 10-year CVD risk of 2.5 (%) in the female group, the FRS identified 746 low-risk patients, including 11 (4.3%) with CAD in the male group and 4 (0.8%) with CAD in the female group. None of the patients needed an indication for coronary artery bypass grafting (CABG).

Conclusions: The FRS model can accurately predict the prevalence of significant CAD and can reliably identify low-risk patients in whom routine pre-surgical angiography could be avoided.

Keywords: chronic rheumatic heart disease; coronary artery disease; Framingham Risk Score

Introduction

Coronary artery disease (CAD) has a detrimental effect on prognosis in patients undergoing operations for chronic rheumatic heart disease (CRHD) operations. Many factors, including a history of CAD, suspected myocardial ischemia, left ventricular systolic dysfunction, one or more cardiovascular risk factors and men aged over 40 years or female patients who are postmenopausal have been recommended for coronary angiography (CAG) prior to surgery [1]. However, it is recommended that all patients with CRHD to undergo CAG because the low positive rate varies from 10.2% to 18.8% [2–4], which increases radiation exposure, as well as the wear and tear on material in developing countries. Models have been established to predict coronary artery stenosis in patients with CRHD. Salas-Lara defined a predictive index to reliably predict cases without significant CAD in patients with RHD; however, this index could not be used to identify significant CAD [5]. Shu-Chun Li established a score system to identify low-risk patients in whom routine preoperative CAG could be avoided [6]. However, the validated approach for the evaluation of possible CAD in patients with CRHD remained uncertain. The Framingham Risk Score (FRS) has been recommended for global risk assessment in subjects who were likely to experience CAD events [7]. Therefore, we investigated the usefulness of FRS for predicting the risk of CAD in patients undergoing operations for CRHD. We also identified potential factors for CAD in patients with CRHD in an attempt to enhance the ability of FRS to assess coronary risk; the classic risk factors may not account for all coronary events.

Patients and methods

Patients

We retrospectively enrolled 989 patients from Guangdong General Hospital (Guangzhou, China) between 2005 and 2010. All of the selected patients were diagnosed as having CRHD and underwent Doppler echocardiography examination prior to surgery. Angiographically significant CAD was defined as any lesion causing at least 50% luminal stenosis in one or more major epicardial coronary arteries. The clinical and echocardiography characteristics of the enrolled participants were collected by one researcher using an electronic case report form. Exclusion criteria included patients with prior CAD or diabetes mellitus. This study was in agreement with the guidelines of the Ethics Committee of the hospital.

Framingham Risk Score

Factors including age, sex, current smoking, blood pressure, total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) levels had previously been identified and implemented in the FRS, which was recommended for global risk assessments for CAD. Moreover, the presence of CAD risk equivalents (e.g., diabetes or atherosclerotic disease in other vascular beds), which was recognized in the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATPIII) guidelines, was excluded from this FRS study.

Diagnosis of CRHD

The diagnosis of CRVD was primarily based on the Jones criteria [8]: 1) Eligible patients could be diagnosed with a previous history of a known rheumatic fever attack; 2) The clinical diagnosis was primarily based on a pathological valvular heart murmur detected during auscultation that included mitral valve incompetence, mitral stenosis, aortic regurgitation and tricuspid regurgitation; 3) The Doppler echocardiography examination was one of the most important criteria. Typically, morphological changes of the mitral valve included leaflet thickening, subvalvular apparatus thickening, shortened chordates tendineae, commissural fusion, calcification, and restricted leaflet motion. The affected aortic valve could have thickened cusps with rolled edges.

Statistical analysis

The statistical analysis was performed with the SPSS 13.0 software program. Continuous data were presented as the means±SD and compared using Student’s t-test when the data were normally distributed; the data were otherwise compared using the Wilcoxon rank-sum test. The categorical data were presented as percentages and were compared using the χ2-test. The cut-off points were analyzed using receive operative characteristics (ROC). A variable was identified as a significant independent factor when the p-value was p<0.05.

Results

Baseline characteristics of the patient population

The baseline clinical characteristics of both groups are summarized in Table 1. A total of 90 (9.1%) patients had CAD, including 89 patients (99%) aged 50 years or older and only 1 patient (1%) aged younger than 50 years. There were 268 (29.8%) males in the non-CAD group and 46 (51.1%) in the CAD group (p=0.000). There were statistically significant inter-group differences in terms of smoking, hypertension, cardiac function in the New York Heart Association (NYHA) classification and bypass surgery status (p<0.05). The laboratory findings in the non-CAD group and the CAD group were estimated glomerular filtration rate (eGFR) 88.9±25.2 vs. 78.9±26.7 mL/min·1.73 m2 (p=0.000); C-reactive protein (CRP) 2.9 (1.8, 5.0) vs. 3.6 (2.0, 8.0) mg/L (p=0.010) and total cholesterol 4.58±1.06 vs. 4.92±1.11 mmol/L (p=0.025). No statistical inter-group correlations were observed in the remaining characteristics (including Doppler echocardiography Table 2).

Table 1

Clinical characteristics of the patients.

Clinical variablesNon-CAD groupCAD groupp-Value
Total899 (90.9%)90 (9.1%)
Demograghics
 Age, year
  >60, n (%)231 (25.7%)51 (56.7%)0.000a
  50–60, n (%)588 (65.4%)38 (42.2%)
  40–50, n (%)79 (8.8%)1 (1.1%)
  <40, n (%)1 (0.1%)0
  Males, n (%)268 (29.8%)46 (51.1%)0.000
History
 Smoking, n (%)113 (12.6%)22 (24.4%)0.002
 Hypertension, n (%)92 (10.2%)23 (25.6%)0.000
 Typical chest pain, n (%)32 (3.6%)4 (4.4%)0.895
 Atrial fibrillation, n (%)562 (62.5%)51 (56.7%)0.276
Laboratory parameters
 eGFR, (mL/min·1.73 m2)88.9±25.278.9±26.70.000
 AST, U/L21 (20, 29)b23 (20, 30)b0.275
 ALT, U/L20 (16, 27)b22 (18, 30)b0.273
 BNP, pg/mL1112 (572, 1937)b1189 (611, 2543)b0.367
 CRP, g/L2.9 (1.8, 5.0)b3.6 (2.0, 8.0)b0.010
 ESR, mm/h10 (5, 17)b10 (5, 20)b0.494
Blood lipid profiles
 Total cholesterol, mmol/L4.58±1.064.92±1.110.025
 LDL cholesterol, mmol/L2.90±0.833.09±0.980.084
 HDL cholesterol, mmol/L1.13±0.321.07±0.340.110
NYHA, n (%)
 Class III317 (35.3%)43 (47.8%)0.033
 Class IV36 (4.0%)10 (11.1%)
 Bypass surgery056 (62.2%)0.000

NYHA, New York Heart Association; eGFR, estimated glomerular filtration rate; BNP, brain natriuretic peptide; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; LDL, low-density lipoprotein; HDL, high-density lipoprotein. aBy Fisher’s exact test. bInterquartile range.

Values in italics: statistically significant.

Table 2

Doppler echocardiography characteristics of the patients.

Clinical variablesNon-CAD groupCAD groupp-Value
Mitral valve insufficiency, n (%)Moderate136 (67.7%)15 (62.5%)0.611
Severe340 (84.2%)32 (78.0%)0.314
Mitral stenosis, n (%)Moderate132 (68.0%)11 (55.0%)0.238
Severe503 (89.0%)49 (84.5%)0.300
Aortic valve insufficiency, n (%)Moderate54 (9.0%)5 (8.6%)0.917
Severe220 (28.8%)24 (31.2%)0.662
Aortic stenosis, n (%)Moderate30 (5.2%)6 (11.8%)0.104
Severe89 (14.0%)13 (22.8%)0.071
Tricuspid valve insufficiency, n (%)Moderate48 (12.4%)5 (10.4%)0.687
Severe456 (57.4%)39 (47.6%)0.086
Tricuspid stenosis, n (%)Moderate1 (0.3%)00.887a
Severe11 (3.2%)00.489a
Pulmonary artery pressure (mm Hg)47.6±20.543.5±19.40.091
Left ventricular ejection fraction, %61.8±9.260.3±9.60.176

aBy Fisher’s exact test.

Multiple logistic regression analysis of FRS for CAD in patients with CRHD

Factors including age, sex, current smoking, blood pressure, TC and HDL-C levels had previously been identified and implemented into the FRS. We use these six variables in a multiple logistic regression analysis and found that age (OR 1.129; 95% CI, 1.084, 1.175; p=0.000), male (OR 2.138; 95% CI, 1.119, 3.721; p=0.007), smoking (OR 1.318; 95% CI, 0.675, 2.574; p=0.008), and hypertension (OR 2.156; 95% CI, 1.216, 3.822; p=0.009) were independently associated with CAD in patients with CRHD (Table 3).

Table 3

Multiple logistic regression analysis of FRS for CAD in patients with CRVD.

IndexB-ValueSEp-ValueOR95% CI
Age0.1210.0210.0001.1291.084, 1.175
Males0.7600.2830.0072.1381.119, 3.721
Smoking0.2760.3420.0081.3180.675, 2.574
Hypertension0.7680.2920.0092.1561.216, 3.822
Total cholesterol0.2630.2100.2101.3010.862, 1.962
LDL cholesterol0.1020.2400.6701.1070.692, 1.772
HDL cholesterol–0.7200.4410.1030.4870.205, 1.155

Values in italics: statistically significant.

FRS in predicting CAD in patients with CRHD

Table 4 demonstrates that the FRS had high accuracy in predicting CAD in male patients with CRHD as measured using the area under the curve (AUC), which was 0.904 (95% CI, 0.862–0.945) with a specificity of 90.3% and sensitivity of 76.1%, respectively. The cut-off baseline value of 10-year cardiovascular disease (CVD) risk was set to 12.5 (%). However, in the female patients, the AUC was 0.924 (95% CI, 0.891–0.957) with a specificity of 77.5% and sensitivity of 90.9%, respectively. The cut-off baseline value of 10-year CVD risk was set to 2.5 (%) (Figures 1 and 2).

Table 4

Sensitivity and specificity of FRS in detecting CAD.

VariablesSexAUCp-Value95%CICut-off, %SpecificitySensitivity
FRSMale0.9040.0000.862, 0.94512.5a90.3%76.1%
Female0.9240.0000.891, 0.9572.5a77.5%90.9%

AUC, area under the curve; CI, confidence interval. a10-year cardiovascular disease risk.

Figure 1 Diagnostic value for FRS to predict CAD (males).The area under the curve (AUC) was 0.904 (95% CI, 0.862–0.945) with a specificity of 90.3% and a sensitivity 76.1%, respectively.
Figure 1

Diagnostic value for FRS to predict CAD (males).

The area under the curve (AUC) was 0.904 (95% CI, 0.862–0.945) with a specificity of 90.3% and a sensitivity 76.1%, respectively.

Figure 2 Diagnostic value for FRS to predict CAD (females).The AUC was 0.924 (95% CI, 0.891–0.957) with a specificity of 77.5% and a sensitivity of 90.9%, respectively.
Figure 2

Diagnostic value for FRS to predict CAD (females).

The AUC was 0.924 (95% CI, 0.891–0.957) with a specificity of 77.5% and a sensitivity of 90.9%, respectively.

Discussion

In this retrospective study, we investigated whether the FRS was a valid predictor to evaluate significant coronary stenosis in patients undergoing operations for CRHD. This study was the first to utilize the FRS in accurately predicting CAD in patients with CRHD pre-surgery. Moreover, our findings also demonstrated that 1) the rate of CRHD patients with CAD was low (9.1%) in southern China and that 2) the factors older age, male, smoking, hypertension, poor cardiac function as demonstrated using the NYHA classification and a high CRP level contributed to CAD.

CAG is widely indicated for the detection of associated CAD when surgery is planned. Significant coronary stenosis contributes to risk stratification and could be used to determine if concomitant coronary revascularization is indicated. The European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS) practice guidelines for the management of valvular heart disease (version 2012) for pre-surgical CAG includes male patients 40 years of age or older, female patients who are postmenopausal or patients with one additional cardiovascular risk factor [1]. When we applied these guidelines to our study population, we determined that nearly all patients should undergo CAG screening coronary stenosis. Nevertheless, the incidence of CAD in patients with CRHD is very low (9.1%) in southern China, which results in unneeded radiation exposure and wear and tear on equipment and material. To our knowledge, this was the first simple prediction model of significant CAD in patients undergoing operations for CRHD. In this study, patients with lower scores on the FRS tended to have less CAD while patients with high scores on the FRS tended to have more CAD. In the male group, the AUC for predicting CAD was 0.904, and the cut-off baseline value for 10-year CVD risk was set at 12.5 (%) with a specificity of 90.3% and sensitivity of 76.1%. In the female group, the AUC for predicting CAD was 0.924. The cut-off baseline value of 10-year CVD risk was set to 2.5 (%) with a specificity of 77.5% and sensitivity of 90.9%. Several prior studies concentrated on purely predictive clinical models of significant CAD. Gilbert J and colleagues developed a Bayesian model for perioperative cardiac risk assessment that retained its prognostic accuracy when applied to the validation sets and could be used to reliably estimate risk in this group [9]. However, it did not reliably stratify risk in vascular surgery candidates referred for CRHD. Based on established risk factor profiles including age, sex, chest pain and electrocardiographic criteria, several logistic regression and simple additive models for the prediction of possible coronary artery stenosis in pre-surgical patients have been developed [5, 6, 10, 11]. These models included different factors, and the application of these models to our patient population could be hindered due to a lack of standard baseline characteristics.

The FRS came from the Framingham Heart Study, which was calculated with the serum cholesterol level or without the serum cholesterol level [12]. In this study, age, sex, current smoking, blood pressure, TC and HDL-C levels were identified to correlate with FRS, which was recommended for the global risk assessment of CAD. In the male group, with a probability of 12.5 (%) as a cut-off, the FRS was used to identify 253 low-risk patients, including 11 (4.3%) with CAD, but none of which required an indication for Coronary Artery Bypass Grafting (CABG). However, the FRS was used to identify 61 high-risk patients, including 35 (57.4%) with CAD. Among the 35 CAD patients, 20 needed CABG. Moreover, in the female group, the FRS identified 493 low-risk patients (with a probability of 2.5% as a cut-off), including 4 (0.8%) with CAD, and 182 high-risk patients, including 40 (22.0%) with CAD. Of the 40 CAD patients, 25 needed CABG (Table 5). Thus, patients with a low risk could potentially avoid undergoing CAG prior to surgery. This finding is in agreement with Tao Yan’s model [13], which identified low-risk patients in which routine preoperative angiography could be safely avoided. Only 4 (1.2%) patients had single-vessel disease, and none had high risks for CAD. Lin’s model [14] identified 210 low-risk patients, including four (1.8%) with CAD, none of whom were indicated for CABG. In comparison, our models were significantly simpler and could more easily be adapted by clinics with standard baseline characteristics.

Table 5

Negative predictive value and positive predictive value of FRS in detecting CAD.

Risk categoryMales, n=314Female, n=675
<12.5 (%)a>12.5 (%)<2.5 (%)a>2.5 (%)
CAD11 (4.3%)35 (57.4%)4 (0.8%)40 (22.0%)
Non-CAD242 (95.7%)26 (42.6%)489 (99.2%)142 (78.0%)

a10-year cardiovascular disease risk.

Finally, we determined that male sex, older age, smoking, hypertension, poor cardiac function via NYHA classification and high CRP levels contributed to CAD. These conclusions were in agreement with recent findings by the ESC and EACTS practice guidelines on the management of valvular heart disease [1], as well as those of Enver Atalar’s study [15].

We would like to acknowledge that our study has several limitations. First, this study was performed at a single center for CRHD. The size of our study population was limited, and the results should be confirmed in a RCT study. Patients with CAD risk equivalents such as diabetes mellitus and peripheral vascular diseases were excluded from this Framingham score study. CAD in patients with type 2 diabetes mellitus requires further studies. Finally, a follow-up study would be necessary to confirm the predictive power of the FRS for CAD in patients with CRHD.

Conclusions

In conclusion, for the prediction of significant CAD in patients undergoing operations for CRHD, FRS models could be used to reliably identify low-risk patients in whom routine pre-surgical angiography could be avoided; additionally, these models proved to be significantly simpler for clinical use due to standard baseline characteristics. Nevertheless, well-designed randomized clinical trials are necessary to assess the predictive power of FRS for CAD in patients with CRHD.

Corresponding author: Professor Danqing Yu, Department of Cardiology, Guangdong General Hospital (Guangdong Academy of Medical Sciences), Guangzhou, P.R. China, No. 106, the 2nd Zhongshan Road, 510080, Guangzhou, China, Phone: + 86-20-83827812, Fax: + 86-20-83827812, E-mail:
aCo-first authors.

  1. Conflict of interest statement

  2. Authors’ conflict of interest disclosure: The authors stated that there are no conflicts of interest regarding the publication of this article.

  3. Research funding: None declared.

  4. Employment or leadership: None declared.

  5. Honorarium: None declared.

  6. Authors’ contribution: Yaowang Lin collected, analyzed and wrote this manuscript, Xuebiao Wei collected and analyzed the data, Anping Cai, Xing Yang, Yingling Zhou assisted in this study conduction, Danqing Yu was the principal investigator.

References

1. Vahanian A, Alfieri O, Andreotti F, Antunes MJ, Baron-Esquivias G, Baumgartner H, et al. [Guidelines on the management of valvular heart disease (version 2012). The Joint Task Force on the Management of Valvular Heart Disease of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS)]. G Ital Cardiol (Rome) 2013;14:167–214.Search in Google Scholar

2. Kruczan DD, Silva NA, Pereira BB, Romao VA, Correa FW, Morales FE. Coronary artery disease in patients with rheumatic and non-rheumatic valvular heart disease treated at a public hospital in Rio de Janeiro. Arq Bras Cardiol 2008;90:197–203.Search in Google Scholar

3. Jose VJ, Gupta SN, Joseph G, Chandy ST, George OK, Pati PK, et al. Prevalence of coronary artery disease in patients with rheumatic heart disease in the current era. Indian Heart J 2004;56:129–31.Search in Google Scholar

4. Guray Y, Guray U, Yilmaz MB, Mecit B, Kisacik H, Korkmaz S. Prevalence of angiographically significant coronary artery disease in patients with rheumatic mitral stenosis. Acta Cardiol 2004;59:305–9.10.2143/AC.59.3.2005186Search in Google Scholar

5. Salas-Lara VM, Rangel-Abundis A, Solorio-Meza S, Albarran-Lopez H. Assessment of a predictive index for coronary artery disease in patients with rheumatic valvular disease. Cir Cir 2005;73:85–9.Search in Google Scholar

6. Li SC, Liao XW, Li L, Zhang LM, Xu ZY. Prediction of significant coronary artery disease in patients undergoing operations for rheumatic mitral valve disease. Eur J Cardiothorac Surg 2012;41:82–6.Search in Google Scholar

7. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837–47.10.1161/01.CIR.97.18.1837Search in Google Scholar

8. Guidelines for the diagnosis of rheumatic fever. Jones Criteria, 1992 update. Special Writing Group of the Committee on Rheumatic Fever, Endocarditis, and Kawasaki Disease of the Council on Cardiovascular Disease in the Young of the American Heart Association. J Am Med Assoc 1992;268:2069–73.10.1001/jama.268.15.2069Search in Google Scholar

9. L’Italien GJ, Paul SD, Handle RC, Leppo JA, Cohen MC, Fleisher LA, et al. Development and validation of a Bayesian model for perioperative cardiac risk assessment in a cohort of 1,081 vascular surgical candidates. J Am Coll Cardiol 1996;27:779–86.10.1016/0735-1097(95)00566-8Search in Google Scholar

10. Pryor DB, Harrell FJ, Lee KL, Califf RM, Rosati RA. Estimating the likelihood of significant coronary artery disease. Am J Med 1983;75:771–80.10.1016/0002-9343(83)90406-0Search in Google Scholar

11. Lim E, Ali ZA, Barlow CW, Jackson CH, Hosseinpour AR, Halstead JC, et al. A simple model to predict coronary disease in patients undergoing operation for mitral regurgitation. Ann Thorac Surg 2003;75:1820–5.10.1016/S0003-4975(03)00171-1Search in Google Scholar

12. Brindle P, Emberson J, Lampe F, Walker M, Whincup P, Fahey T, et al. Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study. Br Med J 2003;327:1267.10.1136/bmj.327.7426.1267Search in Google Scholar PubMed PubMed Central

13. Yan T, Zhang GX, Li BL, Han L, Zang JJ, Li L, et al. Prediction of coronary artery disease in patients undergoing operations for rheumatic aortic valve disease. Clin Cardiol 2012;35:707–11.10.1002/clc.22033Search in Google Scholar PubMed PubMed Central

14. Lin SS, Lauer MS, Asher CR, Cosgrove DM, Blackstone E, Thomas JD, et al. Prediction of coronary artery disease in patients undergoing operations for mitral valve degeneration. J Thorac Cardiovasc Surg 2001;121:894–901.10.1067/mtc.2001.112463Search in Google Scholar PubMed

15. Atalar E, Yorgun H, Canpolat U, Sunman H, Kepez A, Kocabas U, et al. Prevalence of coronary artery disease before valvular surgery in patients with rheumatic valvular disease. Coron Artery Dis 2012;23:533–7.10.1097/MCA.0b013e32835aab26Search in Google Scholar PubMed

Received: 2014-2-15
Accepted: 2014-5-15
Published Online: 2014-6-10
Published in Print: 2014-9-1

©2014, Danqing Yu et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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https://doi.org/10.1515/dx-2014-0010
Keywords for this article
chronic rheumatic heart disease; coronary artery disease; Framingham Risk Score
Creative Commons
BY-NC-ND 3.0

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Less is more, but do not throw out the baby with the bathwater either!
  4. Reviews
  5. Uncovering the hidden villain within the human respiratory microbiome
  6. The role of leukotriene receptor antagonists in exercise induced bronchoconstriction in children
  7. Original Articles
  8. Developing checklists to prevent diagnostic error in Emergency Room settings
  9. Framingham Risk Score for the prediction of coronary artery disease in patients with chronic rheumatic heart disease
  10. A survey of doctors reveals that few laboratory tests are of primary importance at the Emergency Department
  11. Case Report
  12. The bias of the question posed: a diagnostic “invisible gorilla”
  13. Letter to the Editor
  14. Thrombophilia testing and diagnostic dilemma – a tertiary centre experience
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