Abstract

BackgroundCoronary artery disease (CAD), a leading cause of mortality, affects patient health-related quality of life (HRQoL). Elective percutaneous coronary interventions (ePCIs) are usually performed to improve HRQoL of CAD patients. The aim of this study was to design models using admission data to predict the outcomes of the ePCI treatments on the patients’ HRQoL.MethodsThis prospective cohort study was conducted with CAD patients who underwent ePCIs at the King Abdullah University Hospital in Jordan from January 2014 through May 2015. Six months after their ePCI procedures, the participants completed the improved MacNew (QLMI-2) questionnaire, which was used for evaluating three domains (physical, emotional and social) of HRQoL. Multivariate linear regression was used to design models to predict the three domains of HRQoL from echocardiographic findings and clinical data that are routinely measured on admission.ResultsThe study included 239 patients who underwent ePCIs and responded to the QLMI-2 questionnaire. The mean age (± standard deviation) of the participants was 55.74 ± 11.84 years, 54.58 ± 11.37 years for males (n = 174) and 59.11 ± 12.49 years for females (n = 65). The average scores for physical, emotional and social HRQoL were 4.38 ± 1.27, 4.4 ± 1.11, and 4.37 ± 1.32, respectively. Out of the 42 factors inputted to the models to predict HRQoL scores, 10, 9, and 9 factors were found to be significant determinants for physical, emotional and social domains, respectively, with adjusted coefficients of determination of 0.630, 0.604 and 0.534, respectively. Basophil levels on admission showed a significant positive correlation with the three domains of HRQoL, while aortic root diameter showed a negative correlation. Scores for the three domains were significantly lower in women than in men. Hypertensive and diabetic patients had significantly lower HRQoL scores than patients without hypertension and diabetes.ConclusionThe prediction of HRQoL scores 6 months after an ePCI is possible based on data acquired on admission. The models developed here can be used as decision-making tools to guide physicians in identifying the efficacy of ePCIs for individual patients, hence decreasing the rate of inappropriate ePCIs and reducing costs and complications.

Highlights

  • Coronary artery disease (CAD), a leading cause of mortality, affects patient health-related quality of life (HRQoL)

  • While primary Percutaneous coronary intervention (PCI) is very urgent in patients with acute myocardial infarction (MI), elective PCI is used for CAD patients to relieve symptoms that may be difficult to control with medication and to improve their health-related quality of life (HRQoL)

  • We developed and validated models based on multivariate linear regression (MLR) analyses for predicting the HRQoL of elective PCI (ePCI) patients from the data routinely acquired on admission

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Summary

Introduction

Coronary artery disease (CAD), a leading cause of mortality, affects patient health-related quality of life (HRQoL). Elective percutaneous coronary interventions (ePCIs) are usually performed to improve HRQoL of CAD patients. The leading cause of morbidity and mortality worldwide is CAD [1]. In 2010, more than 75% of deaths due to CAD were attributed to low- and middle-income countries [2, 3]. Percutaneous coronary intervention (PCI) is usually performed to treat CAD. While primary PCI is very urgent in patients with acute MI (heart attack), elective PCI (ePCI) is used for CAD patients to relieve symptoms that may be difficult to control with medication and to improve their health-related quality of life (HRQoL). Despite the survival advantages of PCI over drug therapy [7], multiple studies have shown that there was no mortality advantage of PCI to stable CAD [8]

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