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The use of echocardiographic and clinical data recorded on admission to simplify decision making for elective
Rabah M Al Abdi1, Hussam Alshraideh2, Heba H Hijazi3
1Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid, Jordan. rmalabdi@just.edu.jo.
Insights
Predicting health-related quality of life (HRQoL) after elective percutaneous coronary interventions (ePCIs) is possible using admission data. These models can help physicians identify ePCI efficacy for individual patients, reducing costs and complications.
Area of Science:
- Cardiology
- Health Services Research
- Medical Informatics
Background:
- Coronary artery disease (CAD) significantly impacts patients' health-related quality of life (HRQoL).
- Elective percutaneous coronary interventions (ePCIs) are commonly performed to improve HRQoL in CAD patients.
- Predictive models for HRQoL outcomes post-ePCI are crucial for personalized treatment decisions.
Purpose of the Study:
- To develop predictive models for HRQoL domains (physical, emotional, social) following ePCIs.
- To utilize routinely collected admission data for HRQoL outcome prediction.
- To assess the potential of these models in guiding clinical decision-making and optimizing ePCI utilization.
Main Methods:
- Prospective cohort study of 239 CAD patients undergoing ePCIs.
- HRQoL assessment using the improved MacNew (QLMI-2) questionnaire 6 months post-procedure.
- Multivariate linear regression analysis to build predictive models using admission clinical and echocardiographic data.
Main Results:
- Models successfully predicted physical, emotional, and social HRQoL domains with significant coefficients of determination (0.630, 0.604, 0.534).
- Admission basophil levels positively correlated with all HRQoL domains; aortic root diameter showed a negative correlation.
- Lower HRQoL scores were observed in women, hypertensive, and diabetic patients compared to their counterparts.
Conclusions:
- HRQoL outcomes 6 months post-ePCI can be predicted using admission data.
- Developed models serve as decision-making tools for physicians to identify ePCI efficacy.
- These predictive models can potentially reduce inappropriate ePCIs, associated costs, and complications.
Background:
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 aim of this study was to design models using admission data to predict the outcomes of the ePCI treatments on the patients' HRQoL.
Methods:
This 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.
Results:
The 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.
Conclusion:
The 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.
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