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Background Numerous invasive and noninvasive diagnostic tests with different cost and effectiveness exist for detection of coronary artery disease. This diversity leads to unnecessary utilization of health services. For this reason, this study focused on the cost-effectiveness analysis of diagnostic strategies for coronary artery disease from the perspective of the health care system with 1-year time horizon. Results Incremental cost effectiveness ratios of all strategies were less than the threshold except for the electrocardiography-computed tomography angiography-coronary angiography strategy, and cost of the cardiac magnetic resonance imaging-based strategy was higher than the cost of other strategies. Also, the number of correct diagnosis in the electrocardiography-coronary angiography strategy was higher than the other strategies, and its ICER was 15.197 dollars per additional correct diagnosis. Moreover, the sensitivity analysis found that the probability of doing MRI and sensitivity of the exercise electrocardiography had impact on the results. Conclusion The most cost-effective strategy for acute patient is ECG-CA strategy, and for chronic patient, the most cost-effective strategies are electrocardiography-single photon emission computed tomography-coronary angiography and electrocardiography-exercise electrocardiography-coronary angiography. Applying these strategies in the same clinical settings may lead to a better utilization of resources.

Introduction: Hospital beds, human resources, and medical equipment are the costliest elements in the health system and play an essential role at the time of treatment. In this paper, different phases of the NEDA 2026 project and its methodological approach were presented and its formulation process was analysed using the Kingdon model of policymaking. Methods: Iran Health Roadmap (NEDA 2026) project started in March 2016 and ended in March 2017. The main components of this project were hospital beds, clinical human resources, specialist personnel, capital medical equipment, laboratory facilities, emergency services, and service delivery model. Kingdon model of policymaking was used to evaluate NEDA 2026 development and implementation. In this study, all activities to accomplish each step in the Kingdon model was described. Results: The followings were done to accomplish the goals of each step: collecting experts' viewpoint (problem identification and definition), systematic review of the literature, analysis of previous experiences, stakeholder analysis, economic analysis, and feasibility study (solution appropriateness analysis), three-round Delphi survey (policy survey and scrutinization), and intersectoral and interasectoral agreement (policy legislation). Conclusion: In the provision of an efficient health service, various components affect each other and the desired outcome, so they need to be considered as parts of an integrated system in developing a roadmap for the health system. Thus, this study demonstrated the cooperation process at different levels of Iran's health system to formulate a roadmap to provide the necessary resources for the health sector for the next 10 years and to ensure its feasibility using the Kingdon policy framework.

Recruitment of leukocytes is one of the earliest events in the pathogenesis of ischemic heart disease (IHD) and chemokines play an important role in the migration of these cells into the inflammation sites. The aim of this study was to evaluate the CXCL10, CCL20 and CCL22 levels and the single nucleotide polymorphisms (SNPs) rs4508917, rs6749704 and rs4359426 in chemokine genes in patients with IHD to clarify any association. A total of 300 patients with IHD as having acute myocardial infarction (AMI; n=100), stable angina (SA; n=100) or unstable angina (UA; n=100) and 100 healthy subjects as a control group were enrolled to study. Serum samples from all participants were tested for the CXCL10, CCL20 and CCL22 levels by using ELISA. The SNPs were determined by polymerase chain reaction-restriction length polymorphism (PCR-RFLP) method. The mean serum concentrations of CXCL10, CCL20 and CCL22 in AMI patients (395.97±21.20Pg/mL, 108.38±10.31Pg/mL and 1852.58±205.77Pg/mL), SA patients (405.48±27.36Pg/mL, 90.20±7.69Pg/mL and 2322.04±231.23Pg/mL) and UA patients (396.69±22.79Pg/mL, 141.87±18.10Pg/mL and 2754.89±211.70Pg/mL) were significantly higher than in the healthy group (179.38±8.85Pg/mL, 51.92±4.62Pg/mL and 451.82±23.76Pg/mL, respectively; P

OBJECTIVE Much attention has recently been focused on the underlying role of circulating inflammatory biomarkers such as high-sensitivity C-reactive protein for predicting cardiovascular disease progression. We therefore set out to assess the relationship between the value of high-sensitivity C-reactive protein and (i) coronary artery disease severity, and (ii) left ventricular end diastolic pressure. DESIGN A cross-sectional study. SETTING The Shafa hospital in Kerman, Iran. PATIENTS A total of 107 consecutive patients referred for coronary angiography from January 2008 to January 2009 were prospectively studied. INTERVENTION AND MAIN OUTCOME MEASURES All patients underwent coronary angiography. They all had undergone left ventricular end diastolic pressure measurement, involving a 6-Fr pigtail catheter and a properly zeroed fluid-filled pressure transducer. For each patient, the level of high-sensitivity C-reactive protein was also determined using enzyme-linked immunosorbent assay kits. RESULTS The high-sensitivity C-reactive protein levels could strongly predict increased left ventricular end diastolic pressure (standardised beta=1.010; P=0.008), with other patient variables being confounders, but there was no significant association between these levels and Gensini scores. Multiple linear regression analysis showed that among the study parameters, systolic hypertension (standardised beta=1.611; P=0.047) and a family history of coronary artery disease (standardised beta=1.911; P=0.005) were the main predictors of high Gensini scores in study patients. CONCLUSION High-sensitivity C-reactive protein level is a clinical parameter that could predict left ventricular end diastolic pressure and left ventricular dysfunction, but was not associated with the severity of coronary artery disease.