Real-World Data Validate ESC Risk Model in NSTE-ACS
A real-world study of more than 12,000 cases over 7 years has validated the predictive ability of the proposed guidelines for stratifying thrombotic risks at 1 year for patients with non–ST-elevated acute coronary syndrome (NSTE-ACS) undergoing percutaneous coronary intervention (PCI).
Dr George Dangas
In research presented at the Society for Cardiovascular Angiography & Interventions annual scientific sessions, George Dangas, MD, PhD, current SCAI president and professor of cardiology and vascular surgery at the Icahn School of Medicine at Mount Sinai, New York, reported that the European Society of Cardiology risk stratification criteria appropriately predicted risk in 12,538 patients treated from 2012 to 2019.
Despite these proposed guidelines put forward by the ESC in 2020, no consensus exists on criteria for ischemic or thrombotic risk in NSTE-ACS patients, Dr. Dangas noted.
The new study shows that the 1-year major adverse cardiovascular events (MACE) risk was four times greater in patients classified as medium risk (hazard ratio, 4.31; 95% confidence interval, 2.47-7.52) and six times greater in high-risk patients (HR, 6.16; 95% CI, 3.52-10.8), compared with the low-risk group, mostly because of higher rates of all-cause death and myocardial infarction, Dr. Dangas said in an interview.
“Indeed, we found some good correlation between the three risk categories and gradation of risk that validates essentially, but with the statistical testing that we need, that this classification is meaningful if not perfect,” Dr. Dangas said. “In the future we may perform calibrations to enhance its performance.”
The study used data on consecutive patients from the Angioplasty and Stent Procedures Database of Mount Sinai, grouping them into low, medium, and high thrombotic risk based on the proposed ESC guidelines for the management of NSTE-ACS.
The guidelines included a subset of criteria to identify patients with increased thrombotic risk who may benefit from extended treatment with a second antithrombotic agent.
This study aimed to evaluate the value of the criteria to identify patients at higher risk of ischemic events. “That’s why we went to our database to see how this might work,” Dr. Dangas said.
The researchers also found that high-risk patients had about a 40% greater risk of major bleeding (HR, 1.39; 95% CI, 1.06-1.84). Bleeding risks were similar between the low- and moderate-risk groups.
The risk categories reflected the rates of all-cause death, myocardial infarction, or stroke: 5.4%, 4.1%, and 1.6% in the high-, moderate-, and low-risks groups, respectively (P < .001).
“This identification of ischemic risks worked very well for all-cause mortality,” Dr. Dangas said. “I feel this is a strength because mortality is a leader of outcomes. And of course, we’ve had some associations with all events like mortality, myocardial infarction, repeat revascularization, which are interesting and valid, but I think a study result that indicates the mortality itself is known to be unidirectional and a very good correlation makes the result more robust.”
Risk prediction models such as the proposed ESC guidelines will play a critical role as individualized medicine continues to evolve, Somjot Brar, MD, MPH, director of the regional department of cardiac catheterization at Kaiser Permanente, Los Angeles Medical Center, and associate clinical professor at the University of California, Los Angeles, said in an interview.
Dr Somjot Brar
“This study highlights again the importance of the value for predictive and precision medicine,” Dr. Brar said. “Everything is moving in this direction where we make decisions that are more appropriate for a given patient as opposed to a population of patients.”
Study strengths are the large sample size in a real-world setting and thorough 1-year follow-up, Dr. Brar said.
A limitation is the three risk categories the guidelines proposed. “These are still pretty big boxes,” he said. “The low-, moderate- and high-risk categorization is still very, very broad and can be very vague.”
The relatively low percentage of low-risk patients – 12% versus 56% and 32% for the moderate- and high-risk groups – in this data set may also skew results, Dr. Brar said.
“As we move toward predictive analytics and medicine, we want to make these boxes smaller and smaller and smaller to be able to better understand which treatments should be administered to which patients to maximize the benefit against the risk,” he said. That would be a focus for future analyses, Dr. Brar said.
Dr. Dangas and Dr. Brar have no relevant financial disclosures.
This article originally appeared on MDedge.com, part of the Medscape Professional Network.
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