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Role of Coronary Angiography in Pre-Liver Transplantation Cardiac Evaluation: Experience From an Asian Transplant
N Q Pang1, W C A Kow1, J H Law2
1Department of Surgery, National University Health System, Singapore.
Insights
Predictors for abnormal coronary angiography in liver transplant candidates were identified. A model using diabetes, dyslipidemia, ischemic heart disease, age over 65, and LVH may reduce unnecessary procedures.
Area of Science:
- Cardiology
- Hepatology
- Transplant Surgery
Background:
- Liver transplant (LT) patients with significant coronary artery disease (CAD) face poorer outcomes.
- Pre-LT coronary angiography (CA) carries significant complication risks in cirrhotic patients.
Purpose of the Study:
- Identify predictors of abnormal CA in pre-LT cardiac assessment.
- Develop a predictive model to minimize unnecessary CA procedures.
Main Methods:
- Retrospective analysis of 122 patients undergoing CA from January 2006 to June 2013.
- Univariate and multivariable logistic regression to identify predictors of abnormal CA.
- Development of a predictive model incorporating key clinical factors.
Main Results:
- 33.6% of patients had abnormal CA.
- Significant predictors included age ≥65, cryptogenic cirrhosis, cardiac comorbidities, IHD, LVH, hypertension, diabetes, dyslipidemia, metabolic syndrome, ≥2 CAD risk factors, and high Framingham risk score.
- A predictive model (diabetes, dyslipidemia, IHD, age ≥65, LVH) achieved 90.0% sensitivity and 55.1% specificity, potentially halving unnecessary CA.
Conclusions:
- Diabetes, dyslipidemia, IHD, age ≥65, and LVH are key predictors of abnormal CA in pre-LT patients.
- The developed predictive model shows promise for optimizing CA selection in LT candidates.
- Further validation of the predictive model is recommended.
Background:
Liver transplant (LT) patients with significant coronary artery disease (CAD) have poorer outcomes. Pre-LT coronary angiography (CA) is associated with significant complications in cirrhotic patients.
Methods:
This study aimed to identify predictors of abnormal CA in pre-LT cardiac assessment and to develop a predictive model to reduce unnecessary CA. From January 2006 to June 2013, 122 patients underwent CA based on the current institutional protocol.
Results:
Forty-one (33.6%) patients had abnormal CA. Univariate analysis showed age ≥65 years (P = .001), cryptogenic cirrhosis (P = .046), cardiac comorbidities (P = .027), ischemic heart disease (IHD; P = .002), left ventricular hypertrophy (LVH; P = .004), hypertension (P = .002), diabetes mellitus (P = .017), dyslipidemia (P < .001), metabolic syndrome (P = .003), ≥2 CAD risk factors (P = .001), and high Framingham risk score (hard CAD risk, P = .018; cardiovascular disease: lipids, P = .002; body mass index, P < .001) to be significant predictors of abnormal CA. A predictive model was developed with the use of multivariable logistic regression and included diabetes, dyslipidemia, IHD, age ≥65 years, and LVH, achieving a specificity of 55.1% and sensitivity of 90.0%. This would reduce unnecessary CA by up to one-half in our study population (from 81 to 35) while maintaining a false negative rate of only 8.5%.
Conclusions:
Diabetes, dyslipidemia, IHD, age ≥65 years, and LVH appear to be predictors of abnormal CA in pre-LT patients. Our predictive model may help to better select patients for CA, although further validation is required.
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