Use of a Polygenic Risk Score Improves Prediction of Myocardial Injury After Non-Cardiac Surgery

Abstract

Background: While postoperative myocardial injury remains a major driver of morbidity and mortality, the ability to accurately identify patients at risk remains limited despite decades of clinical research. The role of genetic information in predicting myocardial injury after noncardiac surgery (MINS) remains unknown and requires large scale electronic health record and genomic data sets.

Methods: In this retrospective observational study of adult patients undergoing noncardiac surgery, we defined MINS as new troponin elevation within 30 days following surgery. To determine the incremental value of polygenic risk score (PRS) for coronary artery disease, we added the score to 3 models of MINS risk: revised cardiac risk index, a model comprised entirely of preoperative variables, and a model with combined preoperative plus intraoperative variables. We assessed performance without and with PRSs via area under the receiver operating characteristic curve and net reclassification index.

Results: Among 90 053 procedures across 40 498 genotyped individuals, we observed 429 cases with MINS (0.5%). PRS for coronary artery disease was independently associated with MINS for each multivariable model created (odds ratio=1.12 [95% CI, 1.02-1.24], P=0.023 in the revised cardiac risk index-based model; odds ratio, 1.19 [95% CI, 1.07-1.31], P=0.001 in the preoperative model; and odds ratio, 1.17 [95% CI, 1.06-1.30], P=0.003 in the preoperative plus intraoperative model). The addition of clinical risk factors improved model discrimination. When PRS was included with preoperative and preoperative plus intraoperative models, up to 3.6% of procedures were shifted into a new outcome classification.

Conclusions: The addition of a PRS does not significantly improve discrimination but remains independently associated with MINS and improves goodness of fit. As genetic analysis becomes more common, clinicians will have an opportunity to use polygenic risk to predict perioperative complications. Further studies are necessary to determine if PRSs can inform MINS surveillance.

Keywords: coronary artery disease; genomics; myocardial infarction; precision medicine; troponin.

Figure.

Density Distributions and Risk for Developing MINS as a Function of Genome-wide polygenic risk scores for coronary artery disease (PRSCAD) A. Distribution of PRS_CAD in patients developing MINS compared to those without MINS. For MINS group, mean was 0.14 (SD=1.03) and median was 0.18 [95% CI: −0.54 to 0.81]. For non-MINS group mean was −0.01 (SD=0.99) and median was −0.03 [95% CI: −0.64 to 0.65], p=0.004. B. Risk for MINS according to PRS_CAD in MGI population. Patients with PRS_CAD > 1 standard deviation above the mean have a 1.36 fold greater risk of developing MINS [95% CI=1.06–2.40, p=0.016]. Patients with PRS_CAD > 2 standard deviations above the mean have a 2.11 fold greater risk of developing MINS [95% CI=1.26–3.55, p=0.005]. MINS, Myocardial Injury after Noncardiac Surgery; PRS_CAD, Polygenic Risk Score for Coronary Artery Disease; SD, Standard Deviation, CI, Confidence Interval; MGI, Michigan Genomics Initiative.