Predictors of fatal and non-fatal outcomes in the Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA): incremental value of apolipoprotein A-1, high-sensitivity C-reactive peptide and N-terminal pro B-type natriuretic peptide.
OBJECTIVE
Few prognostic models in heart failure have been developed in typically elderly patients treated with modern pharmacological therapy and even fewer included simple biochemical tests (such as creatinine), new biomarkers (such as natriuretic peptides), or, especially, both. In addition, most models have been developed for the single outcome of all-cause mortality.


RESULTS
We built a series of models for nine different fatal and non-fatal outcomes. For each outcome, a model was first built using demographic and clinical variables (Step 1), then with the addition of biochemical measures (serum creatinine, alanine aminotransferase, creatine kinase, thyrotrophin, apolipoproteins A-1 and B, and triglycerides) (Step 2) and finally with the incorporation of high-sensitivity C-reactive protein (hsCRP) and N-terminal pro B-type natriuretic peptide (NT-proBNP). Ranked according to the Wald chi(2) value, age (56), ejection fraction (44), and body mass index (42) were most predictive of all-cause mortality in Step 1 (total model chi(2) 343). Creatinine was the most powerful predictor at Step 2 (48) and ApoA-1 ranked fifth (25), with the overall chi(2) increasing to 440. Log NT-proBNP (167) was the most powerful of the 14 independently predictive variables identified at Step 3 and the overall chi(2) increased to 600. NT-proBNP was the most powerful predictor of each other outcome. hsCRP was not a predictor of all-cause mortality but did predict the composite atherothrombotic outcome.


CONCLUSIONS
Of the two new biomarkers studied in prognostic models in heart failure, NT-proBNP, but not hsCRP, added substantial and independent predictive information, for a range of clinical outcomes, to that provided by simple demographic, clinical, and biochemical measures. ApoA-1 was more predictive than LDL or HDL.
Wedel
Hans
H
Nordic School of Public Health, Göteborg, Sweden. hans.wedel@biostat.se
McMurray
John J V
JJ
Lindberg
Magnus
M
Wikstrand
John
J
Cleland
John G F
JG
Cornel
Jan H
JH
Dunselman
Peter
P
Hjalmarson
Ake
A
Kjekshus
John
J
Komajda
Michel
M
Kuusi
Timo
T
Vanhaecke
Johan
J
Waagstein
Finn
F
D000284:Administration, Oral; D000368:Aged; D016632:Apolipoprotein A-I / Q000097:blood*; D015415:Biomarkers / Q000097:blood; D002097:C-Reactive Protein / Q000378:metabolism*; D002423:Cause of Death / Q000639:trends; D004305:Dose-Response Relationship, Drug; D004334:Drug Administration Schedule; D005260:Female; D005464:Fluorobenzenes / Q000008:administration & dosage / Q000627:therapeutic use*; D006333:Heart Failure / Q000097:blood / Q000188:drug therapy / Q000401:mortality*; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000008:administration & dosage / Q000627:therapeutic use*; D008297:Male; D020097:Natriuretic Peptide, Brain / Q000097:blood*; D009391:Nephelometry and Turbidimetry; D010446:Peptide Fragments / Q000097:blood*; D011379:Prognosis; D011498:Protein Precursors; D011743:Pyrimidines / Q000008:administration & dosage / Q000627:therapeutic use*; D012307:Risk Factors; D000068718:Rosuvastatin Calcium; D013449:Sulfonamides / Q000008:administration & dosage / Q000627:therapeutic use*; D015996:Survival Rate / Q000639:trends
D016632:Apolipoprotein A-I; D015415:Biomarkers; D005464:Fluorobenzenes; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D010446:Peptide Fragments; D011498:Protein Precursors; D011743:Pyrimidines; D013449:Sulfonamides; C109794:pro-brain natriuretic peptide (1-76); D020097:Natriuretic Peptide, Brain; D000068718:Rosuvastatin Calcium; D002097:C-Reactive Protein