Kvaran Rúnar Bragi, Skagervik Andreas, Pernbro Fredrik, Romlin Birgitta, Molin Mattias, Wåhlander Håkan, Aass Hans Christian D, Vistnes Maria, Ojala Tiina, Thorlacius Elin M, Castellheim Albert Gyllencreutz
Department of Anesthesiology and Intensive Care Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Region Västra Götaland, Sahlgrenska University Hospital, Queen Silvia Children's Hospital, Gothenburg, Sweden.
Acta Anaesthesiol Scand. 2025 Jul;69(6):e70073. doi: 10.1111/aas.70073.
Cardiac surgery in infants often triggers a severe inflammatory response. The role of biomarkers in predicting clinical outcomes in this group of patients has been debated in the literature. This study aimed to investigate the predictive value of 20 inflammatory biomarkers, in combination with clinical data, for acute kidney injury, ventilator support duration, and inotropic score following infant cardiac surgery by developing and comparing three models: Clinical-Data-Only, Biomarker-Only, and Combined.
This secondary analysis of the MiLe-1 study included infants undergoing surgery with cardiopulmonary bypass. Biomarkers were measured before and after CPB. Using BIC-guided logistic regression, we developed and compared three multivariable models-Clinical-Data-Only, Biomarker-Only, and Combined-for each outcome. Model performance was assessed using c-statistics and p-contrast tests.
Regarding AKI risk prediction, the c-statistics for Biomarker-Only, Clinical-Data-Only, and Combined Model were 0.79, 0.60, and 0.78 respectively. The difference in performance between the Combined and Clinical-Data-Only Models was statistically significant (p < 0.001). Concerning ventilator support time prediction, the c-statistics were 0.80, 0.72, and 0.77 for the models respectively (p-contrast = 0.10). As for inotropic score prediction, the c-statistics were 0.83, 0.77, and 0.85 for the models (p-contrast = 0.007).
Inflammatory biomarkers may enhance risk stratification for postoperative outcomes in infant cardiac surgery. However, given the exploratory nature of this study, further validation in larger and more diverse cohorts is needed.
婴儿心脏手术常引发严重的炎症反应。生物标志物在预测该组患者临床结局中的作用在文献中一直存在争议。本研究旨在通过开发和比较三种模型：仅临床数据模型、仅生物标志物模型和联合模型，来研究20种炎症生物标志物结合临床数据对婴儿心脏手术后急性肾损伤、呼吸机支持时间和血管活性药物评分的预测价值。
对MiLe-1研究进行二次分析，纳入接受体外循环手术的婴儿。在体外循环前后测量生物标志物。使用贝叶斯信息准则（BIC）指导的逻辑回归，我们针对每个结局开发并比较了三种多变量模型——仅临床数据模型、仅生物标志物模型和联合模型。使用c统计量和p对比检验评估模型性能。
关于急性肾损伤风险预测，仅生物标志物模型、仅临床数据模型和联合模型的c统计量分别为0.79、0.60和0.78。联合模型和仅临床数据模型在性能上的差异具有统计学意义（p < 0.001）。关于呼吸机支持时间预测，各模型的c统计量分别为0.80、0.72和0.77（p对比 = 0.10）。至于血管活性药物评分预测，各模型的c统计量分别为0.83、0.77和0.85（p对比 = 0.007）。
炎症生物标志物可能会增强婴儿心脏手术术后结局的风险分层。然而，鉴于本研究的探索性质，需要在更大且更多样化的队列中进行进一步验证。
