This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone. Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants. AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%–41.8%) vs. 29.3% (27.0%–31.5%) in UK Biobank, 36.5% (34.4%–38.5%) vs. 32.5% (30.4%–34.5%) in MGB, 41.8% (37.7%–45.9%) vs. 33.0% (28.9%–37.2%) in FHS, and 34.9% (28.8%–41.0%) vs. 28.9% (22.9%–35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049). A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%–41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors. A clinical model to estimate ascending aortic diameter was developed in 44 420 UK Biobank participants with cardiovascular magnetic resonance imaging, with the arrows depicting interaction terms between the clinical risk factors. A polygenic score derived from a genome-wide association study of 39 524 participants was incorporated based on data from 4896 participants to produce the AORTA Gene model. The AORTA Gene model was used to estimate ascending aortic diameter in UK Biobank, All of Us, Framingham Heart Study, and Mass General Brigham and to predict adverse thoracic aortic outcomes, including thoracic aortic dissection, in UK Biobank and All of Us. The aortic drawings are derived from Servier Medical Art, CC BY 4.0. Height image by Fengquan Li, aging image by Adrien Coquet, blood pressure cuff image by Luis Prado, scale image by Gacem Tachfin, and DNA image by TkBt, from thenounproject.com, CC BY 3.0. FHS, Framingham Heart Study; GWAS, genome-wide association study; MGB, Mass General Brigham.

Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this retrospective cohort study, we evaluated 77,428 operative cases at a single academic center between 2016 and 2022. A total of 11,212 cases with serum creatinine (sCr) data were included in the analysis. Then, 8519 cases were randomly assigned to the training set and the remainder to the validation set. Fourteen preoperative and twenty intraoperative variables were evaluated using elastic net followed by hierarchical group least absolute shrinkage and selection operator (LASSO) regression. The training set was 56% male and had a median [IQR] age of 62 (51–72) and a 6% AKI rate. Retained model variables were preoperative sCr values, the number of minutes meeting cutoffs for urine output, heart rate, perfusion index intraoperatively, and the total estimated blood loss. The area under the receiver operator characteristic curve was 0.81 (95% CI, 0.77–0.85). At a score threshold of 0.767, specificity was 77% and sensitivity was 74%. A web application that calculates the model score is available online. Our findings demonstrate the utility of intraoperative time series data for prediction problems, including a new potential use of the perfusion index. Further research is needed to evaluate the model in clinical settings.

Diameter-based risk stratification for elective repair of ascending aortic aneurysm fails to prevent type A dissection in many patients. Aneurysm wall stresses may contribute to risk prediction; however, rates of wall stress change over time are poorly understood. Our objective was to examine aneurysm wall stress changes over 3–5 years and subsequent all-cause mortality. Male veterans with <5.5 cm ascending aortic aneurysms and computed tomography at baseline and 3- to 5-year follow-up underwent three-dimensional aneurysm model construction. Peak circumferential and longitudinal wall stresses at systole were calculated using finite element analysis. Temporal trends were assessed by mixed-effects modelling. Changes in aortic wall stresses, diameter and length over time were evaluated as predictors of subsequent 3-year all-cause mortality by Cox proportional hazards modelling. Sixty-two male veterans were included in the study. Yearly changes in geometric and biomechanical measures were 0.12 mm/year (95% confidence interval, 0.04–0.20) for aortic diameter, 0.41 mm/year (0.12–0.71) for aortic length, 1.19 kPa/year −5.94 to 8.33) for peak circumferential stress, and 0.48 kPa/year (−3.89 to 4.84) for peak longitudinal stress. Yearly change in peak circumferential stress was significantly associated with hazard of death—hazard ratio for peak circumferential stress growth per 10 kPa/year, 1.27 (95% CI, 1.02–1.60; P = 0.037); hazard ratio for peak circumferential stress growth ≥ 32 kPa/year, 8.47 (95% CI, 2.42–30; P < 0.001). In this population of nonsurgical aneurysm patients, large temporal changes in peak circumferential stress, but not aortic diameter or length, was associated with all-cause mortality. Biomechanical stress and stress changes over time may be beneficial as additional risk factors for elective surgery in small aneurysms.

Background: Thoracic aortic disease is an important cause of morbidity and mortality in the US, and aortic diameter is a heritable contributor to risk. Could a polygenic prediction of ascending aortic diameter improve detection of aortic aneurysm? Methods: Deep learning was used to measure ascending thoracic aortic diameter in 49,939 UK Biobank participants. A genome-wide association study (GWAS) was conducted in 39,524 participants and leveraged to build a 1.1 million-variant polygenic score with PRScs-auto . Aortic diameter prediction models were built with the polygenic score (“AORTA Gene”) and without it. The models were tested in a held-out set of 4,962 UK Biobank participants and externally validated in 5,469 participants from Mass General Brigham Biobank (MGB), 1,298 from the Framingham Heart Study (FHS), and 610 participants from All of Us . Results: In each test set, the AORTA Gene model explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.9% (95% CI 37.8-42.0%) vs 29.2% (95% CI 27.1-31.4%) in UK Biobank, 36.5% (95% CI 34.4-38.5%) vs 32.5% (95% CI 30.4-34.5%) in MGB, 41.8% (95% CI 37.7-45.9%) vs 33.0% (95% CI 28.9-37.2%) in FHS, and 34.9% (95% CI 28.8-41.0%) vs 28.9% (95% CI 22.9-35.0%) in All of Us . AORTA Gene had a greater AUROC for identifying diameter ≥4cm in each test set: 0.834 vs 0.765 (P=7.3E-10) in UK Biobank, 0.808 vs 0.767 in MGB (P=4.5E-12), 0.856 vs 0.818 in FHS (P=8.5E-05), and 0.827 vs 0.791 (P=7.8E-03) in All of Us . Conclusions: Genetic information improved estimation of thoracic aortic diameter when added to clinical risk factors. Larger and more diverse cohorts will be needed to develop more powerful and equitable scores.

This report presents the case of a 66-year-old man with acute torrential aortic insufficiency after a Ross procedure 20 years earlier, a biologic aortic valve replacement 16 years earlier, and a transcatheter valve-in-valve 4 years earlier. He underwent third-time sternotomy, revealing that the pulmonary autograft was heavily calcified and frozen to the homograft. The previous transcatheter valve-in-valve was explanted. The previous bioprosthetic valve was adhered in place, necessitating emergency deployment of a replacement transcatheter valve-in-valve. The patient recovered well, with normal aortic valve function. Techniques from cardiac surgery and interventional cardiology are rapidly converging in invasiveness, thus making hybrid strategies increasingly important in complex cases.