Dataset Information

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

ABSTRACT: Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We first used Bootstrap Lasso Regression (BLR) - a Machine Learning method for selecting important variables - to identify a prognostic set of features that identify patients at high risk of death 6-months after presenting with an Acute Coronary Syndrome. Using data derived from the Global Registry of Acute Coronary Events (GRACE) we trained a logistic regression model using these features and evaluated its performance on a development set (N?=?43,063) containing patients who have values for all features, and a separate dataset (N?=?6,363) that contains patients who have missing feature values. The final model, Ridge Logistic Regression with Variable Inputs (RLRVI), uses imputation to estimate values for missing features. BLR identified 19 features, 8 of which appear in the GRACE score. RLRVI had modest, yet statistically significant, improvement over the standard GRACE score on both datasets. Moreover, for patients who are relatively low-risk (GRACE?87), RLRVI had an AUC and Hazard Ratio of 0.754 and 6.27, respectively, vs. 0.688 and 2.46 for GRACE, (p?

SUBMITTER: Myers PD

PROVIDER: S-EPMC6787006 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Myers Paul D PD Huang Wei W Anderson Fred F Stultz Collin M CM

Scientific reports 20191010 1

Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We first used Bootstrap Lasso Regression (BLR) - a Machine Learning method for selecting important variables - to identify a prognostic set of features that identify patients at high risk of death 6-months after presenting with an Acute Coronary Syndrome. U ...[more]

PMID: 31601916

Similar Datasets

Project description:Background Cystatin C (Cys-C) is a marker of renal function that has shown prognostic value for cardiovascular risk stratification across different patient populations. The incremental value of Cys-C beyond established cardiac and renal biomarkers remains incompletely explored. Methods and Results SOLID - TIMI 52 (Stabilization of Plaques Using Darapladib-Thrombolysis in Myocardial Infarction 52; www.clinicaltrials.gov , NCT01000727) randomized patients ≤30 days post-acute coronary syndrome were treated with darapladib or placebo. The association between Cys-C and long-term risk (median follow-up 2.5 years) was assessed in 4965 individuals with adjustments made for clinical variables and other risk markers (eg, estimated glomerular filtration rate, high-sensitivity troponin I, brain-type natriuretic peptide, and fibroblast growth factor-23). The prespecified outcome of interest was cardiovascular death (CVD) or heart failure hospitalization. Cys-C was strongly correlated with creatinine ( r=0.60) and estimated glomerular filtration rate ( r=-0.68), moderately correlated with fibroblast growth factor-23 ( r=0.39), and weakly correlated with brain-type natriuretic peptide ( r=0.28) and high-sensitivity troponin I ( r=0.06) (all P<0.0001). After multivariate adjustment, increasing concentration of Cys-C (per SD of log-transformed Cys-C) was significantly associated with a 28% higher hazard of CVD or heart failure hospitalization (hazard ratio [ HR ] 1.28, 95% confidence interval [ CI ] 1.12-1.46, P<0.001), including CVD ( HR 1.24, 95% CI 1.04-1.47, P=0.01) and heart failure hospitalization ( HR 1.42, 95% CI 1.19-1.69, P<0.001). Cys-C was also associated with a higher hazard of CVD, myocardial infarction, or stroke ( HR 1.15, 95% CI 1.04-1.28, P<0.01), including myocardial infarction ( HR 1.17, 95% CI 1.02-1.33, P=0.02). The addition of Cys-C to a fully adjusted model without estimated glomerular filtration rate improved the C-statistic from 0.80 to 0.81 ( P=0.03) for CVD or heart failure hospitalization. In contrast, the addition of estimated glomerular filtration rate to a fully adjusted model without Cys-C failed to improve model discrimination ( P=0.17). Conclusions Cys-C is associated with the risk of adverse outcomes in patients after acute coronary syndrome. This relationship is independent of established and novel biomarkers of the cardiorenal axis.

Dataset Information

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Publications

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets