Squiccimarro Enrico, Lorusso Roberto, Consiglio Antonio, Labriola Cataldo, Haumann Renard G, Piancone Felice, Speziale Giuseppe, Whitlock Richard P, Paparella Domenico
Division of Cardiac Surgery, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy; Cardio-Thoracic Surgery Department, Heart & Vascular Centre, Maastricht University Medical Centre, Maastricht, The Netherlands.
Cardio-Thoracic Surgery Department, Heart & Vascular Centre, Maastricht University Medical Centre, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands.
J Cardiothorac Vasc Anesth. 2025 Mar;39(3):683-691. doi: 10.1053/j.jvca.2024.12.013. Epub 2024 Dec 9.
To investigate the impact of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develop a machine learning model to predict SIRS.
Retrospective cohort study.
Single tertiary care hospital.
Patients who underwent elective or urgent cardiac surgery with cardiopulmonary bypass (CPB) from 2016 to 2020 (N = 1,908).
Mixed cardiac surgery operations were performed on CPB. Data analysis was made of preoperative, intraoperative, and postoperative variables without direct interventions.
SIRS, defined using American College of Chest Physicians/Society of Critical Care Medicine parameters, was assessed on the first postoperative day. The primary outcome was 30-day mortality. SIRS incidence was 28.7%, with SIRS-positive patients showing higher 30-day mortality (12.2% v 1.5%, p < 0.001). A multivariate logistic model identified predictors of SIRS. Propensity score matching balanced 483 patient pairs. SIRS was associated with increased mortality (OR 2.77; 95% CI 1.40-5.47, p = 0.003). Machine learning models to predict SIRS were developed. The baseline risk model achieved an area under the curve of 0.77 ± 0.04 in cross-validation and 0.73 (95% CI 0.70-0.85) on the test set, while the procedure-adjusted risk model showed improved performance with an area under the curve of 0.81 ± 0.02 in cross-validation and 0.82 (95% CI 0.76-0.85) on the test set.
SIRS is significantly associated with increased 30-day mortality following cardiac surgery. Machine learning models effectively predict SIRS, paving the way for future investigations on potential targeted interventions that may mitigate adverse outcomes.
探讨全身炎症反应综合征（SIRS）对心脏手术后30天死亡率的影响，并开发一种机器学习模型来预测SIRS。
回顾性队列研究。
单一的三级医疗中心。
2016年至2020年接受体外循环（CPB）下择期或急诊心脏手术的患者（N = 1908）。
在CPB上进行混合心脏手术操作。对术前、术中和术后变量进行数据分析，无直接干预措施。
术后第一天使用美国胸科医师学会/危重病医学会参数定义的SIRS进行评估。主要结局是30天死亡率。SIRS发生率为28.7%，SIRS阳性患者的30天死亡率更高（12.2%对1.5%，p < 0.001）。多因素逻辑模型确定了SIRS的预测因素。倾向评分匹配平衡了483对患者。SIRS与死亡率增加相关（OR 2.77；95% CI 1.40 - 5.47，p = 0.003）。开发了预测SIRS的机器学习模型。基线风险模型在交叉验证中的曲线下面积为0.77±0.04，在测试集上为0.73（95% CI 0.70 - 0.85），而程序调整风险模型表现更佳，交叉验证中的曲线下面积为0.81±0.02，在测试集上为0.82（95% CI 0.76 - 0.85）。
SIRS与心脏手术后30天死亡率增加显著相关。机器学习模型可有效预测SIRS，为未来可能减轻不良结局的潜在靶向干预研究铺平了道路。
