Hematological Parameters Improve Prediction of Mortality and Secondary Adverse Events in Coronary Angiography Patients: A Longitudinal Cohort Study

Abstract

Prediction of primary cardiovascular events has been thoroughly investigated since the landmark Framingham risk score was introduced. However, prediction of secondary events after initial events of coronary artery disease (CAD) poses a new challenge. In a cohort of coronary angiography patients (n = 1760), we examined readily available hematological parameters from the UPOD (Utrecht Patient Oriented Database) and their addition to prediction of secondary cardiovascular events. Backward stepwise multivariable Cox regression analysis was used to test their ability to predict death and major adverse cardiovascular events (MACE). Continuous net reclassification improvement (cNRI) and integrated discrimination improvement (IDI) measures were calculated for the hematological parameters on top of traditional risk factors to assess prediction improvement. Panels of 3 to 8 hematological parameters significantly improved prediction of death and adverse events. The IDIs ranged from 0.02 to 0.07 (all P < 0.001) among outcome measures and the cNRIs from 0.11 to 0.40 (P < 0.001 in 5 of 6 outcome measures). In the hematological panels red cell distribution width (RDW) appeared most often. The multivariable adjusted hazard ratio of RDW per 1 standard deviation (SD) increase for MACE was 1.19 [1.08-1.32], P < 0.001. Routinely measured hematological parameters significantly improved prediction of mortality and adverse events in coronary angiography patients. Accurately indicating high-risk patients is of paramount importance in clinical decision-making.

FIGURE 1

. ROC curves of models for a clinical model with and without hematological parameters P values are given for difference between the area under the curve (AUC) of the clinical model plus hematological parameters (black line) as compared with the clinical model only (gray line). The hematological parameters added to the model are as stated in Table 3.

FIGURE 2

Multivariable adjusted MACE-free survival by RDW quartiles MACE-free survival plot by RDW quartiles. The results are derived from Cox regression analysis adjusting for age, sex, diabetes, smoking, indication for coronary angiography, angiographic severity of CAD, history of PCI, history of ACS, history of PAD, kidney failure, treatment of CAD, and diuretic use.