Dataset Information

Galectin 3 and Galectin 3 Binding Protein Improve the Risk Stratification after Myocardial Infarction.

ABSTRACT:

Background

Acute myocardial infarction (AMI) survivors are at risk of major adverse cardiac events and their risk stratification is a prerequisite to tailored therapeutic approaches. Biomarkers could be of great utility in this setting.

Methods

We sought to evaluate the utility of the combined assessment of Galectin 3 (Gal-3) and Galectin 3 binding protein (Gal-3bp) for post-AMI risk stratification in a large, consecutive population of AMI patients. The primary outcomes were: Recurrent angina/AMI and all-cause mortality at 12 months after the index event.

Results

In total, 469 patients were included. The median Gal-3bp was 9.1 ?g/mL (IQR 5.8-13.5 ?g/mL), while median Gal-3 was 9.8 ng/mL (IQR 7.8-12.8 ng/mL). During the 12 month follow-up, 34 patients died and 41 had angina pectoris/reinfarction. Gal-3 was associated with all-cause mortality, while Gal-3bp correlated with the risk of angina/myocardial infarction even when corrected for other significant covariates. The final multivariable model for mortality prediction included patients' age, left ventricular ejection fraction (LVEF), Gal-3, and renal function. The ROC curve estimated for this model has an area under the curve (AUC) of 0.84 (95%CI 0.78-0.9), which was similar to the area under the ROC curve obtained using the GRACE score 1-year mortality.

Conclusions

The integrated assessment of Gal-3 and Gal-3bp could be helpful in risk stratification after AMI.

SUBMITTER: Gagno G

PROVIDER: S-EPMC6571589 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Galectin 3 and Galectin 3 Binding Protein Improve the Risk Stratification after Myocardial Infarction.

Gagno Giulia G Padoan Laura L Stenner Elisabetta E Beleù Alessandro A Ziberna Fabiana F Hiche Cristina C Paldino Alessia A Barbati Giulia G Biolo Gianni G Fiotti Nicola N Not Tarcisio T Beltrami Antonio Paolo AP Sinagra Gianfranco G Aleksova Aneta A

Journal of clinical medicine 20190426 5

<h4>Background</h4>Acute myocardial infarction (AMI) survivors are at risk of major adverse cardiac events and their risk stratification is a prerequisite to tailored therapeutic approaches. Biomarkers could be of great utility in this setting.<h4>Methods</h4>We sought to evaluate the utility of the combined assessment of Galectin 3 (Gal-3) and Galectin 3 binding protein (Gal-3bp) for post-AMI risk stratification in a large, consecutive population of AMI patients. The primary outcomes were: Recu ...[more]

PMID: 31035456

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Project description:BackgroundSeveral biomarkers have individually been shown to be useful for risk stratification in patients with acute myocardial infarction (MI). The optimal multimarker strategy remains undefined.Methods and resultsBiomarkers representing different pathobiological axes were studied, including myocardial stress/structural changes (NT-pro B-type natriuretic peptide [NT-proBNP], midregional proatrial natriuretic peptide [MR-proANP], suppression of tumorigenicity 2 [ST2], galectin-3, midregional proadrenomedullin [MR-proADM], and copeptin), myonecrosis (troponin T), and inflammation (myeloperoxidase [MPO], high sensitivity C-reactive protein [hsCRP], pregnancy-associated plasma protein A [PAPP-A], and growth-differentiation factor-15 [GDF-15]), in up to 1258 patients from Clopidogrel as Adjunctive Reperfusion Therapy-Thrombolysis in Myocardial Infarction 28 (CLARITY-TIMI 28), a randomized trial of clopidogrel in ST-elevation MI (STEMI). Patients were followed for 30 days. Biomarker analyses were adjusted for traditional clinical variables. Forward step-wise selection was used to assess a multimarker strategy. After adjustment for clinical variables and using a dichotomous cutpoint, 7 biomarkers were each significantly associated with a higher odds of cardiovascular death or heart failure (HF) through 30 days, including NT-proBNP (adjusted odds ratio [ORadj], 2.54; 95% CI, 1.47-4.37), MR-proANP (2.18; 1.27-3.76), ST2 (2.88; 1.72-4.81), troponin T (4.13; 1.85-9.20), MPO (2.75; 1.20-6.27), hsCRP (1.96, 1.17-3.30), and PAPP-A (3.04; 1.17-7.88). In a multimarker model, 3 biomarkers emerged as significant and complementary predictors of cardiovascular death or HF: ST2 (ORadj, 2.87; 1.61-5.12), troponin T (2.34; 1.09-5.01 and 4.13, 1.85-9.20, respectively for intermediate and high levels), and MPO (2.49; 1.04-5.96). When added to the TIMI STEMI Risk Score alone, the multimarker risk score significantly improved the C-statistic (area under the curve, 0.75 [95% CI, 0.69-0.81] to 0.82 [0.78-0.87]; P=0.001), net reclassification index (0.93; P<0.001), and integrated discrimination index (0.09; P<0.001).ConclusionsIn patients with STEMI, a multimarker strategy that combines biomarkers across pathobiological axes of myocardial stress, myocyte necrosis, and inflammation provides incremental prognostic information for prediction of cardiovascular death or HF.

Project description:BackgroundMany mortality risk scoring tools exist among patients with ST-elevation Myocardial Infarction (STEMI). A risk stratification model that evaluates STEMI prognosis more simply and rapidly is preferred in clinical practice.Methods and findingsWe developed a simple stratification model for blood examination by using the STEMI data of AMI-Kyoto registry in the derivation set (n = 1,060) and assessed its utility for mortality prediction in the validation set (n = 521). We selected five variables that significantly worsen in-hospital mortality: white blood cell count, hemoglobin, C-reactive protein, creatinine, and blood sugar levels at >10,000/μL, <10 g/dL, >1.0 mg/dL, >1.0 mg/dL, and >200 mg/dL, respectively. In the derivation set, each of the five variables significantly worsened in-hospital mortality (p < 0.01). We developed the risk stratification model by combining laboratory variables that were scored based on each beta coefficient obtained using multivariate analysis and divided three laboratory groups. We also found a significant trend in the in-hospital mortality rate for three laboratory groups. Therefore, we assessed the utility of this model in the validation set. The prognostic discriminatory capacity of our laboratory stratification model was comparable to that of the full multivariable model (c-statistic: derivation set vs validation set, 0.81 vs 0.74). In addition, we divided all cases (n = 1,581) into three thrombolysis in myocardial infarction (TIMI) risk index groups based on an In TIME II substudy; the cases were further subdivided based on this laboratory model. The high laboratory group had significantly high in-hospital mortality rate in each TIMI risk index group (trend of in-hospital mortality; p < 0.01).ConclusionsThis laboratory stratification model can predict in-hospital mortality of STEMI simply and rapidly and might be useful for predicting in-hospital mortality of STEMI by further subdividing the TIMI risk index.

Dataset Information

Galectin 3 and Galectin 3 Binding Protein Improve the Risk Stratification after Myocardial Infarction.

Background

Methods

Results

Conclusions

Publications

Galectin 3 and Galectin 3 Binding Protein Improve the Risk Stratification after Myocardial Infarction.

Similar Datasets

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