External validation of the BE-ALIVE score for predicting 30-day mortality in patients presenting with acute coronary syndromes

Int J Cardiol. 2024 Dec 15:417:132560. doi: 10.1016/j.ijcard.2024.132560. Epub 2024 Sep 12.

Abstract

Introduction: The BE-ALIVE score is an additive scoring system for estimating 30-day mortality in patients presenting with an acute coronary syndrome (ACS) [1]. However, it had only previously been tested on an internal validation cohort. The aim was to assess the scoring system on an external validation cohort.

Methods: The scoring system comprises six domains: (1) Base Excess (1 point for < -2 mmols/L), (2) Age (<65 years: 0 points, 65-74: 1 point, 75-84: 2 points, ≥ 85: 3 points), (3) Lactate (<2 mmols/L: 0 points, 2-4.9: 1 point, 5-9.9: 3 points, ≥ 10: 6 points), (4) Intubated & Ventilated (2 points), (5) Left Ventricular function (normal or mildly impaired: -1 point, moderately impaired: 1 point, severely impaired: 3 points) and (6) External / out of hospital cardiac arrest (1 point). We applied the BE-ALIVE score was applied to 205 consecutive patients at a different institution.

Results: Calibration was strong, with an observed to expected ratio of 1.01, a calibration slope of 1.26 and calibration in the large of -0.03. The Spiegelhalter's Z-statistic was -0.95 (p = 0.34). The AUC was 0.95 (0.92-0.98) in the external validation cohort versus 0.90 (0.85-0.95) during internal validation. Overall performance was excellent with a Brier score of 0.07 versus 0.06 during internal validation. The negative predictive value for 30-day mortality of a BE-ALIVE score < 4 was 98 %, with a positive predicted value of a score ≥ 10 of 95 %.

Conclusions: The BE-ALIVE score remains a robust predictor of 30-day mortality in an external validation cohort.

Keywords: Acute coronary syndromes; Prediction; Risk models; Risk scores.

Publication types

  • Validation Study

MeSH terms

  • Acute Coronary Syndrome* / diagnosis
  • Acute Coronary Syndrome* / mortality
  • Aged
  • Aged, 80 and over
  • Cohort Studies
  • Female
  • Humans
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Risk Assessment / methods
  • Time Factors