Assessing the psychosocial risk factors for coronary artery disease: an investigation of predictive validity for the psychosocial inventory for cardiovascular illness

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Baker, Maria Kathryn

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This dissertation investigated the psychometric properties and clinical applications of the Psychosocial Inventory for Cardiovascular Illness (PICI). The PICI is an inventory developed to measure the psychosocial risk factors for heart disease including anxiety, depression, stress, social isolation, and anger. The inventory was developed to measure the ways that each psychosocial risk factor contributes to the coronary artery disease process through the lifestyle behaviors and pathophysiological mechanisms with which they are associated. The primary purpose of the study was to examine predictive validity for the PICI. With support for predictive validity, the inventory may aid in early identification of individuals at increased risk for coronary artery disease (CAD) so that behavioral, psychosocial, and medical interventions can be implemented. Both healthy and cardiac samples were used in the inventory development and validation process. The PICI was administered in conjunction with similar inventories and physiological markers of CAD were collected including percent of coronary artery blockage and history of heart attacks. Item analysis and factor analysis were used to yield a 20-item PICI comprised of three subscales to include Negative Affect, Social Isolation, and Anger. It was hypothesized that the PICI subscales would predict group membership; whether or not a participant carried a diagnosis of CAD, and would be have a strong relationship to the physiological markers of CAD that were measured. Analysis revealed that the PICI was unable to predict diagnostic status and did not have a strong relationship with the physiological markers of CAD. Results suggest that the PICI has acceptable reliability and construct validity as demonstrated in the current sample, yet further investigation must be conducted to gain support for the instrument’s predictive abilities.



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