Personalized Evaluation of Susptected Myocardial Ischemia

Ischemic heart disease poses a significant burden on society and affects many Danes annually. With more than 300,000 citizens in Denmark living with cardiovascular disease, and more than 17,500 cardiovascular deaths annually, the need for effective diagnosis and treatment is crucial. Cardiac Computed Tomography Angiography (CCTA) has become an important tool in diagnosing arteriosclerotic obstructive coronary artery disease (CAD) in patients with typical or atypical chest pain, but the increasing use of this method requires a more efficient selection of patients before initial testing. Early and accurate risk stratification could improve patient management, reduce morbidity, and improve patient outcomes, highlighting the importance of optimizing diagnostic pathways.

At Gødstrup Hospital, novel clinical likelihood (CL) models have been developed to assess the pre-test probability of obstructive CAD. Based on sex, age, and symptom characteristics, and including traditional cardiovascular risk factors, the risk factor-weighted clinical likelihood (RF-CL) model improves discrimination of obstructive CAD and prognosis compared to traditional models. Additionally, the utilization of a coronary artery calcium score (CACS) in conjunction with the RF-CL model, i.e., the CACS-weighted clinical likelihood (CACS-CL) model, further enhances patient management in external validation cohorts. Recently, both CL models have been implemented in the European guidelines on CAD management. However, the CL models have only been applied in observational studies, and no randomized trials substantiate their use in clinical practice.

It is hypothesized that a diagnostic strategy based on an assessment including the CL models is non-inferior to the current standard strategy, as measured by the number of asymptomatic patients during follow-up. Secondly, it is assumed that the CL-based strategy reduces unnecessary diagnostic tests and improve resource utilization without compromising patient safety.

Emerging alongside these developments is Laser Speckle Contrast Imaging (LSCI), a promising non-invasive technique for assessing microvascular function. Several studies have suggested a link between reduced microcirculation in the skin and heart among patients with angina and non-significant calcification, compared to healthy controls. LSCI measures red blood cell movement to quantify blood flow, making it an effective, fast, and cost-efficient tool already in use in other medical fields. If a correlation between peripheral and cardiac microcirculation is established, LSCI could address a diagnostic gap in detecting microvascular dysfunction, particularly for angina patients without significant coronary calcification. Integrating LSCI into the diagnostic process offers potential to further refine patient selection for testing and provide more targeted diagnostic pathways.

This study will increase the evidence for utilizing the RF-CL and CACS-CL models in clinical practice. Currently, the use of pre-test likelihood models is only recommended with a IB recommendation and deferral of diagnostic testing in individuals with CL\>=5% with IIa B recommendation due to a lack of randomized studies. The study will focus on symptomatic endpoints and investigate quality of life measurements in patients deferred for testing based on the CL estimation. Secondary endpoints include both effectiveness and safety metrics.

This project is an ambitious endeavor that builds on previous work performed within our research group. The supervisors are experienced researchers with substantial expertise in this area and conducting randomized studies. The findings from this study have the potential to significantly impact clinical practice by providing evidence-based recommendations (Level/Class of evidence 1A) for the use of CL models in the diagnostic pathway of ischemic heart disease.

By demonstrating that using the CL model in the management of patients with new-onset chest pain substantially and safely reduces the necessity for cardiac CT and other advanced diagnostic procedures, resource utilization could improve and costs be lowered for the healthcare system. Additionally, as tests could be deferred without compromising safety, patient-related quality of life could improve. Finally, the findings are expected to contribute to clinical guidelines and practices, benefiting the broader field of cardiology. By validating the CL models in a large, diverse patient population, this study could provide strong evidence for their broader implementation in clinical practice.

The incorporation of LSCI into this framework also presents an exciting avenue for further improving diagnostic precision. If LSCI can reliably identify microvascular dysfunction, it could serve as a complementary tool in optimizing diagnostic strategies, particularly for patients in whom obstructive CAD has been ruled out but who still experience angina-like symptoms.

If CL model utilization proves capable of safely reducing the necessity for CCTA and other advanced diagnostic procedures in patients with obstructive CAD, resource utilization could improve, lowering costs for the healthcare system while maintaining or enhancing patient quality of life.

The present research addresses a significant gap in current diagnostic strategies and has the potential to shift clinical practices towards more personalized and efficient care pathways for stable chest pain.

• Sensitivity and Specificity for Detecting Obstructive Coronary Artery Disease (CAD) [Срок оценки: From enrollment to 12 months after patient inclusion.]
• Proportion of Patients Undergoing Cardiac CT Imaging [Срок оценки: From enrollment to 12 months after patient inclusion]
• Seattle Angina Questionnaire (SAQ) Scores for Quality of Life [Срок оценки: 3, 6, and 12 months after enrollment]
• Dyspnea Assessment: Rose Dyspnea Score for Symptom Evaluation [Срок оценки: 3, 6 and 12 months after enrollment]
• Correlation Between Peripheral and Cardiac Microvascular Function via LSCI [Срок оценки: At baseline and 6 months after enrollement]
• Long-term prognosis [Срок оценки: 10 years]