Prognostic relevance of baseline pro- and anti-inflammatory markers in STEMI : An APEX AMI substudy

• Background: Plaque rupture, acute ischemia, and necrosis in acute coronary syndromes are accompanied by concurrent pro-and anti-inflammatory cascades. Whether STEMI clinical prediction models can be improved with the addition of baseline inflammatory biomarkers remains unknown. Methods: In an APEX-AMI trial substudy, 772 patients had a panel of 9 inflammatory serum biomarkers, high sensitivity C reactive protein (hsCRP), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) measured at baseline after randomization. Baseline biomarkers were incorporated into a clinical prediction model for a composite of 90-day death, shock, or heart failure. Incremental prognostic value was assessed using Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Results: Individually, several biomarkers were independent predictors of clinical outcome: hsCRP (hazard ratio [HR] 1.12; 95% confidence interval [CI], 1.03-1.21; p=0.007, per doubling), NT-proBNP (HR 1.14; 95% CI, 1.06-1.23; p<0.001, per doubling), interleukin (IL)-6 (HR 1.26; 95% CI, 1.12-1.41; p<0.001, per doubling), and inducible protein-10 (IP-10) (HR 0.86; 95% CI, 0.76-0.98; p<0.025, per doubling). The addition of baseline NT-proBNP (NRI 8.6%, p=0.028; IDI 0.030, p<0.001) and IL-6 (NRI 8.8%, p=0.012; IDI 0.036, p<0.001) improved the clinical risk prediction model and the addition of hsCRP (NRI 6.5%, p=0.069; IDI 0.018, p=0.004) yielded minimal improvement. After incorporating NT-proBNP into the model, the remaining biomarkers added little additional predictive value. Conclusions: Multiple inflammatory biomarkers independently predicted 90-day death, shock or heart failure; however, they added little value to a clinical prediction model that included NT-proBNP. Future studies of inflammatory biomarkers in STEMI should report incremental value in a prediction model that includes NT-proBNP.

Nyckelord

Myocardial infarction

Inflammation

Prediction models

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