Derivation and Validation of a 10-Year Risk Score for Symptomatic Abdominal Aortic Aneurysm: Cohort Study of Nearly 500 000 Individuals

Abstract

Background: Abdominal aortic aneurysm (AAA) can occur in patients who are ineligible for routine ultrasound screening. A simple AAA risk score was derived and compared to current guidelines used for ultrasound screening of AAA. Methods: UK Biobank participants without previous AAA were split into a derivation cohort (n=401,820, 54.6% women, mean age 56.4 years, 95.5% white race) and validation cohort (n=83,816). Incident AAA was defined as first hospital inpatient diagnosis of AAA, death from AAA, or an AAA-related surgical procedure. A multivariable Cox model was developed in the derivation cohort into an AAA risk score that did not require blood biomarkers. To illustrate the sensitivity and specificity of the risk score for AAA, a theoretical threshold to refer patients for ultrasound at 0.25% 10-year risk was modelled. Discrimination of the risk score was compared to a model of US Preventive Services Task Force (USPSTF) AAA screening guidelines. Results: In the derivation cohort there were 1,570 (0.40%) cases of AAA over a median 11.3 years of follow-up. Components of the AAA risk score were age (stratified by smoking status), weight (stratified by smoking status), antihypertensive and cholesterol lowering medication use, height, diastolic blood pressure, baseline cardiovascular disease, and diabetes. In the validation cohort, over ten years of follow-up, the C-index for the model of the USPSTF guidelines was 0.705 (95% CI 0.678, 0.733). The C-index of the risk score as a continuous variable was 0.856 (95%CI 0.837-0.878). In the validation cohort, the USPSTF model yielded sensitivity 63.9% and specificity 71.3%. At the 0.25% 10-year risk threshold, the risk score yielded sensitivity 82.1% and specificity 70.7%, while also improving the net reclassification index (NRI) compared to the USPSTF model +0.176 (95%CI 0.120, 0.232). A combined model, whereby risk scoring was combined with the USPSTF model, also improved prediction compared to USPSTF alone (NRI +0.101, 95%CI 0.055, 0.147). Conclusions: In an asymptomatic general population, a risk score based on patient age, height, weight and medical history may improve identification of asymptomatic patients at risk for clinical events from AAA. Further development and validation of risk scores to detect asymptomatic AAA is needed.

Predictive models for abdominal aortic aneurysms using polygenic scores and PheWAS-derived risk factors

“…Our study suggests an enhanced disease screening program of asymptomatic individuals who would otherwise be considered lower risk by USPTF guidelines would substantially improve AAA detection in the US population. Even covariates alone perform substantially better than the USPTF guidelines, similar to what has been shown in a recent UK Biobank study with a simple predictive model that lacked variables for genetics, sex, or race 38 . This demonstrates the principle that opportunities exist to substantially improve the public health impact of AAA.…”

“…Clinical decision support tools for identifying patients for AAA screening based on USPTF guidelines have existed for over a decade 39 – 42 , however, recent reports indicate that even those fitting USPTF criteria remain unlikely to receive screening (only 13% of eligible patients within ≥ two years) 43 . Importantly, these studies focused on male patients, while in both BioVU and eMERGE, females made up 23–25% of the AAA cases, higher than the 17% observed in the UK Biobank risk prediction study 38 .…”

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

“…The existing aneurysm screening guidelines only consider a few biomarkers [ 12 , 39 ] and are generalized for ‘average patients’. In contrast, PPPM aims to improve the outcomes of medical interventions for each individual by developing new medical policies for clear target patients on the basis of population heterogeneity [ 40 ]; however, this strategy relies on advances in biomarker discovery [ 41 , 42 ].…”

“…According to previous prediction models for aneurysm [ 12 , 37 ], modifiable clinical risk factors, demographic information and social economic status were considered as characteristics that can be used to predict aneurysm events. Baseline characters included age, sex, systolic (SBP) and diastolic (DBP) blood pressure, blood lipid, glycated haemoglobin, smoking status, body mass index (BMI), baseline cardiovascular disease (defined by International Classification of Diseases [ICD-10] codes I20–I25 and I60–I69 excluding I67.0 and I67.1), baseline diabetes (self-reported type 1 or type 2 diabetes), self-reported hyperlipidaemia and the use of blood pressure- or cholesterol-lowering medications or anti-diabetic medications.…”

“…However, these biomarkers neglect the heterogeneous characteristics of the disease [ 7 – 11 ] and are not precise enough to benefit individuals who do not meet the current screening criteria. A recent study proposed an effective and accurate screening approach for abdominal aortic aneurysm (AAA) based on a broader spectrum of clinical biomarkers [ 12 ], thereby providing a foundation for developing and testing new biomarker-based screening approaches for aneurysm. Therefore, the integration of numerous biological data, such as genetics and medical images, and the identification of non-invasive and convenient biomarkers, could facilitate the prediction of aneurysm and provide options for personalized medical prevention strategies, which is an essential step in the context of PPPM [ 4 ].…”