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Abstract：
BACKGROUND: The detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models in the context of the pandemic environment. This study aimed to elucidate the adaptability of prediction models for 30-day mortality in patients with ACS during the pandemic periods.
METHODS: A total of 2041 consecutive patients with ACS were included from 32 institutions between December 2020 and April 2023. The dataset comprised patients who were admitted for ACS and underwent coronary angiography for the diagnosis during hospitalisation. The prediction accuracy of the Global Registry of Acute Coronary Events (GRACE) and a machine learning model, KOTOMI, was evaluated for 30-day mortality in patients with ST-elevation acute myocardial infarction (STEMI) and non-ST-elevation acute coronary syndrome (NSTE-ACS).
RESULTS: The area under the receiver operating characteristics curve (AUROC) was 0.85 (95% CI 0.81 to 0.89) in the GRACE and 0.87 (95% CI 0.82 to 0.91) in the KOTOMI for STEMI. The difference of 0.020 (95% CI -0.098-0.13) was not significant. For NSTE-ACS, the respective AUROCs were 0.82 (95% CI 0.73 to 0.91) in the GRACE and 0.83 (95% CI 0.74 to 0.91) in the KOTOMI, also demonstrating insignificant difference of 0.010 (95% CI -0.023 to 0.25). The prediction accuracy of both models had consistency in patients with STEMI and insignificant variation in patients with NSTE-ACS between the pandemic periods.
CONCLUSIONS: The prediction models maintained high accuracy for 30-day mortality of patients with ACS even in the pandemic periods, despite marginal variation observed.
METHODS: A total of 2041 consecutive patients with ACS were included from 32 institutions between December 2020 and April 2023. The dataset comprised patients who were admitted for ACS and underwent coronary angiography for the diagnosis during hospitalisation. The prediction accuracy of the Global Registry of Acute Coronary Events (GRACE) and a machine learning model, KOTOMI, was evaluated for 30-day mortality in patients with ST-elevation acute myocardial infarction (STEMI) and non-ST-elevation acute coronary syndrome (NSTE-ACS).
RESULTS: The area under the receiver operating characteristics curve (AUROC) was 0.85 (95% CI 0.81 to 0.89) in the GRACE and 0.87 (95% CI 0.82 to 0.91) in the KOTOMI for STEMI. The difference of 0.020 (95% CI -0.098-0.13) was not significant. For NSTE-ACS, the respective AUROCs were 0.82 (95% CI 0.73 to 0.91) in the GRACE and 0.83 (95% CI 0.74 to 0.91) in the KOTOMI, also demonstrating insignificant difference of 0.010 (95% CI -0.023 to 0.25). The prediction accuracy of both models had consistency in patients with STEMI and insignificant variation in patients with NSTE-ACS between the pandemic periods.
CONCLUSIONS: The prediction models maintained high accuracy for 30-day mortality of patients with ACS even in the pandemic periods, despite marginal variation observed.
摘要：
背景：COVID-19大流行对急性冠状动脉综合征（ACS）患者的护理质量和临床结局的有害影响，因此需要在大流行环境下对预后预测模型进行严格的重新评估。本研究旨在阐明大流行期间ACS患者30天死亡率预测模型的适应性。
方法：纳入了2020年12月至2023年4月期间来自32个机构的2041例连续ACS患者。数据集包括因ACS入院并在住院期间接受冠状动脉造影诊断的患者。全球急性冠状动脉事件注册(GRACE)和机器学习模型的预测准确性，KOTOMI,对ST段抬高型急性心肌梗死(STEMI)和非ST段抬高型急性冠脉综合征(NSTE-ACS)患者的30天死亡率进行了评估.
结果：STEMI的受试者工作特征曲线下面积（AUROC）在GRACE中为0.85（95％CI0.81至0.89），在KOTOMI中为0.87（95％CI0.82至0.91）。0.020(95%CI-0.098-0.13)差异不显著。对于NSTE-ACS，GRACE中各自的AUROC为0.82(95%CI0.73至0.91)，KOTOMI中的AUROC为0.83(95%CI0.74至0.91)，也显示差异不显著0.010（95%CI-0.023至0.25）。两种模型的预测准确性在STEMI患者中具有一致性，而在大流行期之间，NSTE-ACS患者的差异不大。
结论：即使在大流行时期，预测模型也能保持ACS患者30天死亡率的高准确性。尽管观察到边际变化。
方法：纳入了2020年12月至2023年4月期间来自32个机构的2041例连续ACS患者。数据集包括因ACS入院并在住院期间接受冠状动脉造影诊断的患者。全球急性冠状动脉事件注册(GRACE)和机器学习模型的预测准确性，KOTOMI,对ST段抬高型急性心肌梗死(STEMI)和非ST段抬高型急性冠脉综合征(NSTE-ACS)患者的30天死亡率进行了评估.
结果：STEMI的受试者工作特征曲线下面积（AUROC）在GRACE中为0.85（95％CI0.81至0.89），在KOTOMI中为0.87（95％CI0.82至0.91）。0.020(95%CI-0.098-0.13)差异不显著。对于NSTE-ACS，GRACE中各自的AUROC为0.82(95%CI0.73至0.91)，KOTOMI中的AUROC为0.83(95%CI0.74至0.91)，也显示差异不显著0.010（95%CI-0.023至0.25）。两种模型的预测准确性在STEMI患者中具有一致性，而在大流行期之间，NSTE-ACS患者的差异不大。
结论：即使在大流行时期，预测模型也能保持ACS患者30天死亡率的高准确性。尽管观察到边际变化。
