Cost-Effectiveness of Magnetocardiography in Diagnosis of Coronary Artery Disease in Patients with Chest Pain

Research Article

Austin Cardiol. 2016; 1(1): 1003.

Cost-Effectiveness of Magnetocardiography in Diagnosis of Coronary Artery Disease in Patients with Chest Pains

Chaikovsky IA¹*, Pryimak VM², Verba AV³, Lutay MI5, Budnyk MM¹, Mjasnikov GV4, Kazmirchyk AP4, Kovalenko AS¹, Bae JSH6 and Ji Wenming7

¹Glushkov Institute of Cybernetics, Ukraine

²Shevchenko National University of Kyiv, Ukraine

³Military Medical Department, Ministry of Defense, Ukraine

4National Military Medical Clinical Center, Ukraine

5National Research Center, Strazheshesko Institute of Cardiology, Ukraine

6University of Oxford, UK

7Cardiomox, UK

*Corresponding author: Illya Chaikovsky, Glushkov Institute of Cybernetics, 40 Glushkov Ave., 03680, Kyiv- 187, Ukraine

Received: September 16, 2016; Accepted: December 09, 2016; Published: December 13, 2016

Abstract

Coronary Artery Disease (CAD) is a leading cause of death worldwide. Early detection has been shown to be critical in preventing CAD-related deaths. Magnetocardiography (MCG) is often favoured for its non-invasiveness and high sensitivity in the current diagnosis of CAD. Despite the popularity of MCG, an analysis of its cost-effectiveness in comparison with other non-invasive methods has not yet been performed. To estimate the potential cost effectiveness of MCG in CAD patients, specifically in those with chest pain, cost-effectiveness analyses of selected non-invasive methods (Stress-ECG, Stress-Scintigraphy and Stress-EchoCG) were performed and compared. The analysis revealed that MCG shows the lowest cost-effectiveness ratios, indicating it is the most efficient diagnostic method amongst non-invasive cardiographs. Furthermore, our analysis revealed that MCG is the most cost efficient method even for patients with symptomatic indication of CAD (e.g. chest pain), either on its own or in combination with coronary angiographs. These results suggest MCG is a highly most economical non-invasive diagnostic method, and can improve the quality of CAD diagnosis.

Keywords: Magnetocardiography; Cost-effectiveness analysis; Coronary artery disease; Non-invasive diagnostic methods

Abbreviations

MCG: Magnetocardiography; CAD: Coronary Artery Disease; CEA: Cost-Effectiveness Analysis; Stress-ECG: Stress Electrocardiography; Stress-scintigraphy: Stress Scintigraphy Test with Tallium; Stress-EchoCG: 2D Echocardiography and Load Test with Treadmill; RISK: Risk of Essential Cardiovascular Failures Provoked by Given Diagnostic Method; CA: Coronary Angiography; CER: Cost-Effectiveness Ratio; PREV: Prevalence; SENS: Sensitivity; SPEC: Specificity

Introduction

Cardiovascular complications represent one of the leading cause of death worldwide, and they are estimated to cause 23.3 million deaths by 2030 [1]. Coronary Artery Disease (CAD) is the most common cause of death among cardiovascular complications, and indeed it accounted for more than 16.8% of all deaths worldwide in 2013 [2]. CAD has also been associated with important morbidity and mortality related to stroke, ischemia, embolism and heart failure [3]. The number of cases of CAD is especially high in developed countries. According to the Global Burden of Disease Study 2010, ischemic heart disease and stroke are the most prevalent diseases in Ukraine. In the United States, CAD is the most common cause of death in men and women over 20 years of age, contributing to 370,000 deaths annually [4].

Especially concerning is the fact that the prevalence of CAD is increasing [5]. Moreover, the identification of the mechanisms by which CAD results in untimely deaths, as well as the development of safe and effective therapies to combat it, remain elusive. Developing innovative therapeutics targeting CAD is a priority, and much effort has been expended in identifying prophylactic measures and pharmacological approaches for disease management. While the methods of early CAD diagnosis have been significantly improved, stress echocardiography, followed by Coronary Angiograph (CA), remain the most favourable methods of diagnosis in symptomatic patients. However, the recent development of Magnetocardiography (MCG) - a non-invasive cardiac-activity mapping technique - has led to increased detection sensitivity via increased numbers of recording sites as compared to other non-invasive cardiographs. MCG can detect even slight changes in the electrophysiology of the myocardium, and allows for the visualization of cardiac electrophysiological processes without any external influence [6]. MCG also provides information on the magnetic signature produced by the vortex currents in the myocardium, which cannot be registered by Electrocardiography (ECG) [7]. These unique advantages of MCG make it an attractive technique for CAD detection and it has contributed to the current understanding of the generation, localization, and dynamic behaviours of cardiac currents in CAD patients.

Common non-invasive techniques to diagnose CAD include Stress Induced Electrocardiography (stress-ECG), Echocardiography (stress-EchoCG) and Scintigraphy (stress-scintigraphy). The choice of one method over another depends on cost-effectiveness and resource consideration. Generating a generic model that can estimate the comparative cost effectiveness of a screening technique would thus provide a valuable tool to assess the opportunity cost of a medical intervention on the health care system [8]. In order to make a comparison amongst the current non-invasive cardiographs, this study aimed to perform a Cost Effectiveness Analysis (CEA) for several methods (Stress-ECG, Stress-EchoCG, and Stress-Scintigraphy) and to compare them with the MCG, the most modern non-invasive CAD diagnostic technique.

Methods

Statistical definitions

The research was conducted under the following terms:

Total probability of false diagnosis for CAD

Health economic evaluations include uncertainty for both positive and false parameters of observable variables. In order to determine the total probability of establishing a false diagnosis, we have built a generalized model to combine both sensitivity and specificity to analyze the likelihood of an error [10]:

p error (Diagnosis) =α.PREV+β.( 1PREV )   (1) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabaqaamaabaabaaGcbaaeaaaaaaaaa8qacaWGWbWdamaaBaaaleaapeGaamyzaiaadkhacaWGYbGaam4Baiaadkhaa8aabeaakmaaBaaaleaapeGaaiikaiaadseacaWGPbGaamyyaiaadEgacaWGUbGaam4BaiaadohacaWGPbGaam4CaiaacMcaa8aabeaak8qacqGH9aqpcqaHXoqycaGGUaGaamiuaiaadkfacaWGfbGaamOvaiabgUcaRiabek7aIjaac6cadaqadaWdaeaapeGaaGymaiabgkHiTiaadcfacaWGsbGaamyraiaa[email protected][email protected]

Total probability of false diagnosis for CAD in patients with symptomatic indications

The most common symptom of CAD is chest pain, described as chest discomfort, aching, and heaviness in the chest [11]. Since the presence of disease symptoms can bias the selection of a method of diagnosis, prevalence – which measures the probability of a randomized occurrence of CAD - is not an accurate measurement in our model. Pretest Probability (PP), determined by the Mayo Clinic Index (MCI) from 2002, was used instead to calculate the total probability of false diagnosis of CAD in patients with chest pain [12].

p error (Chest Pain) =α.pp+β.(1pp)   (2) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabaqaamaabaabaaGcbaaeaaaaaaaaa8qacaWGWbWdamaaBaaaleaapeGaamyzaiaadkhacaWGYbGaam4Baiaadkhaa8aabeaakmaaBaaaleaapeGaaiikaiaadoeacaWGObGaamyzaiaadohacaWG0bGaaeiiaiaadcfacaWGHbGaamyAaiaad6gacaGGPaaapaqabaGcpeGaeyypa0JaeqySdeMaaiOlaiaadchacaWGWbGaey4kaSIaeqOSdiMaaiOlaiaacIcacaaIXaGaeyOeI0I[email protected][email protected]

Cost-effectiveness ratio

Diagnostic accuracy incorporates parameters of Specificity (SPEC), Sensitivity (SENS) and Prevalence (PRV) when measuring the effectiveness of a method. To determine the diagnostic accuracy of non-invasive methods used in medical practice for the diagnosis of CAD, we calculated predictive indexes using values of sensitivity and specificity derived from the literature [13,14]:

NPV= SPEC [SPEC+α. PREV ( 1PREV )    (3) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabaqaamaabaabaaGcbaaeaaaaaaaaa8qacaWGobGaamiuaiaadAfacqGH9aqpdaWcaaWdaeaapeGaam4uaiaadcfacaWGfbGaam4qaaWdaeaapeGaai4waiaadofacaWGqbGaamyraiaadoeacqGHRaWkcqaHXoqycaGGUaWaaSaaa8aabaWdbiaadcfacaWGsbGaamyraiaadAfaa8aabaWdbmaabmaapaqaa8qacaaIXaGaeyOeI0IaamiuaiaadkfacaWGfbGaamOvaaG[email protected][email protected]

PPV= SENS [SENS+β. PREV ( 1PREV )    (4) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabaqaamaabaabaaGcbaaeaaaaaaaaa8qacaWGqbGaamiuaiaadAfacqGH9aqpdaWcaaWdaeaapeGaam4uaiaadweacaWGobGaam4uaaWdaeaapeGaai4waiaadofacaWGfbGaamOtaiaadofacqGHRaWkcqaHYoGycaGGUaWaaSaaa8aabaWdbiaadcfacaWGsbGaamyraiaadAfaa8aabaWdbmaabmaapaqaa8qacaaIXaGaeyOeI0IaamiuaiaadkfacaWGfbGaamOvaaG[email protected][email protected]

where NPV is a negative predictive value (rate of coincidence of negative test results under the absence of CAD); PPV is a positive predictive value (rate of coincidence of positive test results under the presence of CAD); SPEC and SENS - specificity and sensitivity, respectively; PREV - the prevalence of CAD; α and β -probability of the diagnostic method correctly detecting patients with or without CAD, respectively.

Based on formulas 3 and 4, the average of the diagnostic effectiveness was calculated [13,14]:

EFFECT=(NPV+PPV)/2    (5) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabaqaamaabaabaaGcbaaeaaaaaaaaa8qacaWGfbGaamOraiaadAeacaWGfbGaam4qaiaadsfacqGH9aqpcaGGOaGaamOtaiaadcfacaWGwbGaey4kaSIaamiuaiaadcfacaWGwbGaaiyka[email protected][email protected]

The Cost-Effectiveness Ratio (CER) of each diagnostic method was also calculated:

CER=COST/EFFECT   (6) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabaqaamaabaabaaGcbaaeaaaaaaaaa8qacaWGdbGaamyraiaadkfacqGH9aqpcaWGdbGaam4taiaadofacaWGubGaai4laiaadweacaWGgbGaamOra[email protected][email protected]

Substituting the calculated value of effect (5) above, we can reformulate the CER as the following:

CER=2 COST (NPV+PPV)    (7) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabaqaamaabaabaaGcbaaeaaaaaaaaa8qacaWGdbGaamyraiaadkfacqGH9aqpcaaIYaWaaSaaa8aabaWdbiaadoeacaWGpbGaam4uaiaadsfaa8aabaWdbiaacIcacaWGobGaamiuaiaadAfacqGHRaWkcaWGqbGaa[email protected][email protected]

Ratio for cost-effectiveness increments

The Incremental Cost-Effective Ratio (ICER) provides the summarized cost-effectiveness of a health care intervention by comparing the CER of two diagnostic methods. For this analysis, we have calculated the coefficient ratio between the MCG and other noninvasive cardiographs using the following equation:

ICER= [Cos t (MCG) Cos t (NIM) ] [Effec t (MCG) Effec t (NIM) ]    (8) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=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[email protected][email protected]

where ICER is the incremental cost-effectiveness ratio; Cost(MCG) is the relative cost of MCG; Cost(NIM) is the relative cost of another non-invasive method; Effect(MCG) is the diagnostic accuracy of MCG; Effect(NIM) is the diagnostic accuracy of another non-invasive method.

Relative cost of non-invasive diagnosis followed by coronary angiography

The accuracy of a non-invasive method to diagnose CAD can be uncertain due to the sensitivity and specificity of the method, as well as the severity of the disease symptoms. In most cases, 70% to 90% of diagnoses using non-invasive methods still require a CA to fully confirm the presence of CAD. Therefore, we have further modified the generated formula to calculate the relative probable cost for patients with or without CAD when undergoing both invasive and non-invasive diagnostic methods.

The relative cost of a false diagnosis for patients without symptomatic implications was calculated using:

Cos t (no symptom) =(1PREV).[β.Cos t (MoI) +β. β (CA) .Cos t (CA) ]    (9) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=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[email protected][email protected]

Cos t (CAD) =PREV.[α.Cos t (MoI) +α. α (CA) .Cos t (CA) ]    (10) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=xb9adbaqaaeGaciGaaiaabaqaamaabaabaaGcbaaeaaaaaaaaa8qacaWGdbGaam4BaiaadohacaWG0bWdamaaBaaaleaapeGaaiikaiaadoeacaWGbbGaamiraiaacMcaa8aabeaak8qacqGH9aqpcaWGqbGaamOuaiaadweacaWGwbGaaiOlaiaacUfacqaHXoqycaGGUaGaam4qaiaad+gacaWGZbGaamiDa8aadaWgaaWcbaWdbiaacIcacaWGnbGaam4BaiaadMeacaGGPaaapaqabaGcpeGaey4kaSIaeqySdeMaaiOlaiabeg7aH9aadaWgaaWcbaWdbiaacIcacaWGdbGaamyqaiaacMcaa8aabeaakiaac6capeGaam4qaiaad+gacaWGZbGaamiDa8aadaWgaaWcbaWdbiaacIcacaWGdbGaamyqaiaacMcaa8aabeaak8qa[email protected][email protected]

The relative cost of a false diagnosis for patients with symptomatic implications was calculated using:

Cos t (Chest Pain) =(1PP).[β.Cos t ( MoI ) +β. β ( CA ) .Cos t ( CA ) ]   (11) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=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[email protected][email protected]

Cos t (CAD with Chest Pain) =PP.[α.Cos t ( MoI ) +α. α ( CA ) .Cos t ( CA ) ]   (12) [email protected]@[email protected]@+=feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=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[email protected][email protected]

where PREV is the prevalence of CAD; PP is the pretest probability of CAD based on symptomatic indication; α and β are the probability of the diagnostic method correctly identifying patients with or without CAD, respectively; α(CA) and β(CA) (both = 0.001) are the probability of false positive and negative diagnosis, respectively [15]; Cost (CA) is the cost of the coronary angiograph.

Results

Total probability of false diagnosis for CAD using selected non-invasive methods

To establish the overall effectiveness of selected medical interventions, both diagnostic accuracy and cost were evaluated for the purpose of this study. The cost-effectiveness of MCG was compared with that of other non-invasive methods, namely Stress- ECG, Stress-scintigraphy and Stress-EchoCG, to determine which method is the most accurate with the lowest cost. Sensitivity (SENS) and Specificity (SPEC), derived from previously reported analyses [13,14], and the relative costs of examinations were compared in Table 1.