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<p>We aimed to compare the prognostic value of Machine learning (ML) and traditional Cox regression method for predicting adverse clinical events in patients with acute coronary syndrome (ACS). In a prospective observational cohort of 2730 patients with ACS, we analyzed several candidate variables for median follow-up of 8.1 years from the Observation of Cardiovascular Events of ACS patients (OCEA) study. External validation was performed in 542 patients with ACS for median follow-up of 2.8 years from the management of cardiovascular risk and health (MCRH). The random survival forests (RSF)-based ML model including clinical traditional risk factors and plasma biomarkers were evaluated. A multivariable Cox regression model was subsequently used to develop a nomogram. Adverse clinical events refer to cardiovascular events and non-cardiovascular death. Cardiovascular events were defined as a combined endpoint of MI, ischemic stroke, cardiovascular death and heart failure for hospital. RSF based model exhibited the highest prognostic ability, achieving a C-index of 0.960 with 95% CI (0.952–0.970) in the derivation cohort compared with nomogram model [C-index 0.806, 95% CI:0.790–0.822) and GRACE 2.0 (C-index 0.730, 95% CI:0.711–0.749) (both <i>p</i> &lt; 0.001). In the validation cohort with a median follow-up of 2.8 years, RSF model achieved C-index of 0.836 (95% CI: 0.813–0.866), without superiority compared with Cox regression model with C-index of 0.854 (95%CI: 0.785–0.922), both of which outperformed GRACE 2.0 risk score in derivation and validation cohorts (<i>p</i> &lt; 0.001). Also, our Cox model achieved significantly higher C-index than GRACE 3.0 across both derivation (0.806 vs 0.710) and external validation cohorts (0.854 vs 0.701; all <i>p</i> &lt; 0.001). RSF model and Cox regression model outperformed GRACE 2.0/GRACE 3.0 in the discrimination of adverse clinical events in ACS, with former two carrying no significant difference. RSF showed high apparent performance in the derivation cohort, but the simpler seven-variable Cox nomogram demonstrated comparable external validation performance and may be more practical for clinical use.</p>

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Machine learning and traditional regression approaches to predict risk of cardiovascular events in patients with acute coronary syndrome

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Jing Xue,
  • Xinyu Wang,
  • Xiaorong Xu,
  • Zhiyong Zhang,
  • Hongbin Liu,
  • Zhichang Zheng,
  • Tao Zhang,
  • Xin Wang,
  • Mulei Chen,
  • Li Xu,
  • Hongjie Chi,
  • Weiming Li,
  • Kuibao Li,
  • Juan Wang

摘要

We aimed to compare the prognostic value of Machine learning (ML) and traditional Cox regression method for predicting adverse clinical events in patients with acute coronary syndrome (ACS). In a prospective observational cohort of 2730 patients with ACS, we analyzed several candidate variables for median follow-up of 8.1 years from the Observation of Cardiovascular Events of ACS patients (OCEA) study. External validation was performed in 542 patients with ACS for median follow-up of 2.8 years from the management of cardiovascular risk and health (MCRH). The random survival forests (RSF)-based ML model including clinical traditional risk factors and plasma biomarkers were evaluated. A multivariable Cox regression model was subsequently used to develop a nomogram. Adverse clinical events refer to cardiovascular events and non-cardiovascular death. Cardiovascular events were defined as a combined endpoint of MI, ischemic stroke, cardiovascular death and heart failure for hospital. RSF based model exhibited the highest prognostic ability, achieving a C-index of 0.960 with 95% CI (0.952–0.970) in the derivation cohort compared with nomogram model [C-index 0.806, 95% CI:0.790–0.822) and GRACE 2.0 (C-index 0.730, 95% CI:0.711–0.749) (both p < 0.001). In the validation cohort with a median follow-up of 2.8 years, RSF model achieved C-index of 0.836 (95% CI: 0.813–0.866), without superiority compared with Cox regression model with C-index of 0.854 (95%CI: 0.785–0.922), both of which outperformed GRACE 2.0 risk score in derivation and validation cohorts (p < 0.001). Also, our Cox model achieved significantly higher C-index than GRACE 3.0 across both derivation (0.806 vs 0.710) and external validation cohorts (0.854 vs 0.701; all p < 0.001). RSF model and Cox regression model outperformed GRACE 2.0/GRACE 3.0 in the discrimination of adverse clinical events in ACS, with former two carrying no significant difference. RSF showed high apparent performance in the derivation cohort, but the simpler seven-variable Cox nomogram demonstrated comparable external validation performance and may be more practical for clinical use.