Predicting risk of future events in individuals with chronic coronary syndromes
Background
- Chronic coronary syndrome (CCS) is a dynamic process of atherosclerotic plaque accumulation and functional alterations of coronary circulation that can be modified by lifestyle, pharmacological therapies, and revascularization which result in disease stabilization or regression.
- Investigation of suspicion of CCS is often focused on the diagnosis of local anatomical or functional disease. However, this approach is limited in its ability to detect functionally relevant changes and predict systemic risks for future cardiovascular events.
- The easy, accurate, and safe prognostic risk-stratification of CCS patients is an unmet clinical need.
Objective
Evaluate whether a previously validated 27-protein prognostic model for four-year cardiovascular event risk can be used to stratify patients with suspected chronic coronary syndrome (CCS).
Methods
- BASEL VIII is a large prognostic and diagnostic study in participants with suspected CCS designed to evaluate and advance the non-invasive diagnosis of functionally relevant coronary artery disease (fCAD) (as adjudicated by independent cardiologists) using myocardial perfusion scanning and coronary angiography.
- Measured ~5000 proteins in plasma samples (taken at rest) utilizing SomaScan® Platform from 4106 evaluable participants
- 1688 participants without prior cardiovascular disease (primary population)
- 2418 participants with known prior cardiovascular disease (secondary population)
- Evaluated 27-protein model of cardiovascular event risk in primary and secondary populations
- Continuous output of protein risk score was calculated for each population.
- Low risk bin was evaluated in participants with low risk for an event as a potential rule-out from additional assessments.
- High risk bin was evaluated for added utility with imaging.
- Prognostic performance of 27-protein model was compared to final fCAD diagnosis.
Results: Primary Population
Low category for proteomic risk prediction as a rule-out in primary participants
- 41% of primary participants were categorized as low risk and had an observed event rate of 2%.
- Observed event rates were not significantly different between fCAD- and fCAD+ participants.
High categorical risk scores to identify imaging negative participants at high risk of an event
- High risk categorical scores were present in 48% of primary patients negative for fCAD.
Results: Secondary Population
Low category for proteomic risk prediction as a rule-out in secondary participants
- 27% of secondary participants were categorized as low risk and had an observed event rate of 10%.
- Observed event rates were not significantly different between fCAD- and fCAD+ participants.
High categorical risk scores to identify false negatives of imaging negative participants
- High risk categorical scores were present in 35% of secondary patients negative for fCAD.
Conclusions
- The low-risk category of the proteomic model could effectively rule-out 41% and 27% of the primary and secondary populations from further assessment with negative predictive values of 98% and 92%.
- In subjects with a negative fCAD diagnosis, the proteomic model identified subjects with elevated cardiovascular risk who would have otherwise been overlooked by perfusion imaging and angiography with event rates of 15% for the primary and 26% for the secondary cohorts.
- CVD absolute risk score outperformed fCAD diagnosis alone and in combination at predicting 4-year events.
- These findings suggest that the 27-protein prognostic model could be useful in addition to imaging – by ruling out the need for imaging in a substantive fraction and exposing unresolved risks in imaging negative patients.
Authors
Stephen A. Williams1
Jessica Chadwick1
David Astling1
Michael Hinterberg1
Rachel Ostroff1
Emma Troth1
Joan E. Walter2
Christian Mueller2
1SomaLogic Operating Co., Inc.
2University of Basel
More posters
PosterThe Plasma Proteome as a Cardiovascular Disease Risk Assessment Tool in Cancer Survivors
Cardiovascular disease (CVD) is the most common non-cancer cause of death in cancer survivors and there is an unmet clinical need for easy, accurate, and safe CVD prognostic risk-stratification in adult cancer survivors. This study investigated whether a previously validated 27-plasma protein prognostic model for four-year cardiovascular (CV) events could have such a utility.
PosterEfficient development of prognostic tests for detecting cancer risk using proteomic technology
Prognostic models for assessing future health outcomes can be developed using time-to-event (also known as “survival”) data. This methodology is ubiquitous in statistical literature and in the analysis of cancer outcomes, but its use in high-dimensional analyses tends to be limited as the methods are difficult to implement in a machine learning environment. Additionally, development of certified prognostic clinical tests using proteomic biomarkers for detecting future cancer risk can be time-consuming, prone to overfitting issues, and difficult to navigate. We demonstrate the utility of combining SomaScan® proteomic data with pipeline machine learning tools and survival analysis methodology to identify powerful and robust LDT-certifiable prognostic tests for assessing future risk of cancer.
PosterUtility of proteomic trajectories of cardiovascular risk and cardiorespiratory fitness to monitor adverse health states throughout post-COVID-19 illness
Cardiovascular involvement is a prominent observation in patients during the acute phase of COVID-19 infection, as well as in convalescence. However, the etiology, trajectory, and underlying biology of cardiac dysfunction across the spectrum of COVID-19 illness is not fully understood. To address this, the CISCO-19 study (NCT04403607) was formed to investigate the multisystem effects of COVID-19 from hospitalized patients