Risk scores for predicting outcomes in valvular heart disease: how useful?

Area of Science:

• Cardiology
• Medical Informatics
• Surgical Outcomes Research

Background:

• Risk scoring tools are crucial for predicting patient mortality in cardiac surgery.
• Several risk models exist, derived from clinical databases, to guide surgical decisions.
• These tools are increasingly used to identify suitable candidates for transcatheter aortic valve implantation (TAVI).

Purpose of the Study:

• To evaluate the accuracy of existing risk models, specifically logistic EuroSCORE (LES) and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), for selecting patients undergoing aortic valve replacement (AVR).
• To identify limitations of current risk algorithms in predicting mortality for high-risk patients considered for TAVI.
• To highlight the need for a specialized risk algorithm for TAVI procedures.

Main Methods:

• Review and comparison of established cardiac surgery risk models, including LES and STS-PROM.
• Analysis of model performance in predicting mortality, particularly in high-risk patient populations undergoing AVR.
• Identification of clinical factors impacting mortality that are not incorporated into current risk algorithms.

Main Results:

• Both LES and STS-PROM predict mortality in cardiac surgery, but LES overpredicts mortality by threefold in high-risk AVR candidates.
• STS-PROM demonstrates greater accuracy in predicting mortality for high-risk patients compared to LES.
• Factors such as liver disease, frailty, porcelain aorta, and prior radiation are not included in current models but affect mortality.

Conclusions:

• Existing risk algorithms like LES and STS-PROM are valuable but have limitations, especially for high-risk patients undergoing TAVI.
• STS-PROM is currently more accurate for high-risk patients than LES.
• Development of a novel risk algorithm tailored specifically for TAVI patient selection is essential as TAVI databases mature.