Modified CHA 2 DS 2-VASc score predicts in-hospital mortality and procedural complications in acute coronary syndrome treated with percutaneous coronary intervention
Abugroun A, Hassan A, Gaznabi S, et al. Modified CHADS-VASc score predicts in-hospital mortality and procedural complications in acute coronary syndrome treated with percutaneous coronary intervention. Int J Cardiol Heart Vasc. 2020;28:100532.
Background: Current risk prediction models in acute coronary syndrome (ACS) patients undergoing PCI are mathematically complex. This study was undertaken to assess the accuracy of a modified CHA2DS2-VASc score, comprised of easily accessible clinical factors in predicting adverse events.
Methods: The National Inpatient Sample (NIS) was queried for ACS patients who underwent PCI between 2010 and 2014. We developed a modified CHA2DS2-VASc score for risk prediction in ACS patients. Multivariate mixed effect logistic regression was utilized to study the adjusted risk for adverse outcomes based on the score. The primary outcome evaluated was in-hospital mortality. Secondary outcomes assessed were stroke, respiratory failure, acute kidney injury, all-cause bleeding, pacemaker insertion, vascular complications, length of stay and cost.
Results: There were 252,443 patients admitted with ACS included. Mean age was 62 ± 12 years. The mean CH3A2DS-VASc score was 1.6 ± 1.6. The in-hospital mortality rate was 2.5%. CH3A2DS-VASc score was highly correlated with increased rate of mortality and all secondary outcomes. ROC curve analysis for association of CH3A2DS-VASc score with mortality demonstrates that area under the curve (AUC) = 0.83 (95%C: 0.82-0.84). Stepwise increases in CH3A2DS-VASc score correlated with incremental risk, and total score was an independent predictor of mortality (adjusted OR: 1.99 (95%CI: 1.96-2.03) p < 0.001) and all secondary outcomes.
Conclusion: This study supports the applicability of the CH3A2DS-VASc score as an accurate risk prediction model for ACS patients undergoing PCI and could supplant more complicated models for quality assurance.