Incremental Value of Renal Dysfunction Addition to the CHA2DS2-Vasc Score for Mortality Prediction in Patients with Acute Coronary Syndrome

Link to article at PubMed

Cardiology. 2021 May 7:1-9. doi: 10.1159/000515986. Online ahead of print.


INTRODUCTION: This study analyzes the usefulness of the CHA2DS2-VASc score for mortality prediction in patients with acute coronary syndromes (ACSs) and evaluates if the addition of renal functional status could improve its predictive accuracy.

METHODS: CHA2DS2-VASc score was calculated by using both the original scoring system and adding renal functional status using 3 alternative renal dysfunction definitions (CHA2DS2-VASc-R1: eGFR <60 mL/min/1.73 mq = 1 point; CHA2DS2-VASc-R2: eGFR <60 mL/min/1.73 mq = 2 points; and CHA2DS2-VASc-R3: eGFR <60 mL/min/1.73 mq = 1 point, <30 mL/min/1.73 mq = 2 points). Inhospital mortality (IHM) and post-discharge mortality (PDM) were recorded, and discrimination of the various risk models was evaluated. Finally, the net reclassification index (NRI) was calculated to compare the mortality risk classification of the modified risk models with that of the original score.

RESULTS: Nine hundred and eight ACS patients (median age 68 years, 30% female, 51% ST-elevation) composed the study population. Of the 871 patients discharged, 865 (99%) completed a 12-month follow-up. The IHM rate was 4.1%. The CHA2DS2-VASc score demonstrated a good discriminative performance for IHM (C-statistic 0.75). Although all the eGFR-modified risk models showed higher C-statistics than the original model, a statistically significant difference was observed only for CHA2DS2-VASc-R3. The PDM rate was 4.5%. The CHA2DS2-VASc C-statistic for PDM was 0.75, and all the modified risk models showed significantly higher C-statistics values than the original model. The NRI analysis showed similar results.

CONCLUSIONS: CHA2DS2-VASc score demonstrated a good predictive accuracy for IHM and PDM in ACS patients. The addition of renal dysfunction to the original score has the potential to improve identification of patients at the risk of death.

PMID:33965936 | DOI:10.1159/000515986

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