Dataset Information

CRAX: A simple cardiovascular risk assessment tool to predict risk of acute myocardial infarction or death.

ABSTRACT:

Background

Determining the risk of cardiovascular events is essential to optimize patient management.

Methods and results

5842 individuals underwent SPECT myocardial perfusion imaging (MPI) with 4.4?±?1.2 years of follow-up. Models (the CRAX tool) were derived to predict the cumulative risk of death and acute myocardial infarction (AMI) at 1, 3, and 5 years using clinical and MPI variables. Predictors of AMI and death included age, number of hospitalizations in the 3 years preceding MPI, and left ventricular ejection fraction (LVEF). Additional predictors of death were the use of pharmacological stress, and global stress total perfusion deficit (sTPD), while transient ischemic dilation (TID), and ischemic total perfusion deficit (iTPD) change were predictive of AMI. CRAX predictions were significantly (P?ConclusionsCRAX is a risk assessment tool that predicts the risk of AMI, death, or MACE, and improves prediction compared to clinical variables or MPI results alone.

SUBMITTER: Martineau P

PROVIDER: S-EPMC6565497 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

CRAX: A simple cardiovascular risk assessment tool to predict risk of acute myocardial infarction or death.

Martineau Patrick P Slomka Piotr P Goertzen Andrew A Leslie William D WD

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology 20181210 6

<h4>Background</h4>Determining the risk of cardiovascular events is essential to optimize patient management.<h4>Methods and results</h4>5842 individuals underwent SPECT myocardial perfusion imaging (MPI) with 4.4 ± 1.2 years of follow-up. Models (the CRAX tool) were derived to predict the cumulative risk of death and acute myocardial infarction (AMI) at 1, 3, and 5 years using clinical and MPI variables. Predictors of AMI and death included age, number of hospitalizations in the 3 years precedi ...[more]

PMID: 30535920

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Project description:BackgroundGenetic risk scores have been developed for coronary artery disease and atherosclerosis, but are not predictive of adverse cardiovascular events. We asked whether peripheral blood expression profiles may be predictive of acute myocardial infarction (AMI) and/or cardiovascular death.MethodsPeripheral blood samples from 338 subjects aged 62 ± 11 years with coronary artery disease (CAD) were analyzed in two phases (discovery N = 175, and replication N = 163), and followed for a mean 2.4 years for cardiovascular death. Gene expression was measured on Illumina HT-12 microarrays with two different normalization procedures to control technical and biological covariates. Whole genome genotyping was used to support comparative genome-wide association studies of gene expression. Analysis of variance was combined with receiver operating curve and survival analysis to define a transcriptional signature of cardiovascular death.ResultsIn both phases, there was significant differential expression between healthy and AMI groups with overall down-regulation of genes involved in T-lymphocyte signaling and up-regulation of inflammatory genes. Expression quantitative trait loci analysis provided evidence for altered local genetic regulation of transcript abundance in AMI samples. On follow-up there were 31 cardiovascular deaths. A principal component (PC1) score capturing covariance of 238 genes that were differentially expressed between deceased and survivors in the discovery phase significantly predicted risk of cardiovascular death in the replication and combined samples (hazard ratio = 8.5, P < 0.0001) and improved the C-statistic (area under the curve 0.82 to 0.91, P = 0.03) after adjustment for traditional covariates.ConclusionsA specific blood gene expression profile is associated with a significant risk of death in Caucasian subjects with CAD. This comprises a subset of transcripts that are also altered in expression during acute myocardial infarction.

Project description:ImportanceAccurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.ObjectiveTo evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.Design, setting, and participantsThis cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020.Main outcomes and measuresThree machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample.ResultsA total of 755 402 patients (mean [SD] age, 65 [13] years; 495 202 [65.5%] male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates.Conclusions and relevanceIn this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.

Dataset Information

CRAX: A simple cardiovascular risk assessment tool to predict risk of acute myocardial infarction or death.

Background

Methods and results

Publications

CRAX: A simple cardiovascular risk assessment tool to predict risk of acute myocardial infarction or death.

Similar Datasets

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