Noordzij Peter G, Thio Maaike S Y, Reniers Ted, Dijkstra Ineke, Mondelli Gabriele, Langelaan Marloes, Ruven Henk J T, Rettig Thijs C D
Department of Anaesthesiology and Intensive Care, University Medical Centre Utrecht, 3584 CX Utrecht, The Netherlands.
Department of Anaesthesiology, Intensive Care and Pain Medicine, St. Antonius Hospital, Koekoekslaan 1, 3435 CM Nieuwegein, The Netherlands.
J Clin Med. 2025 May 27;14(11):3737. doi: 10.3390/jcm14113737.
Postoperative atrial fibrillation (POAF) is a common and serious complication after cardiac surgery. Existing clinical prediction models show limited discriminative ability. We hypothesize that incorporating biomarkers that reflect key pathophysiological pathways of POAF can enhance preoperative risk stratification. Adult cardiac surgery patients without a history of atrial fibrillation from the BIGPROMISE cohort-a prospective, observational, two-centre perioperative biobank study-were included to investigate whether biomarkers of myocardial injury, systemic inflammation, haematological status, and metabolic and neuroendocrine dysregulation improved prediction of new-onset POAF when compared with an established clinical model, the POAF Score. We evaluated the incremental value of a 13-biomarker panel added to the POAF Score using multivariable logistic regression with shrinkage (lasso), assessing model discrimination, calibration, reclassification, and net clinical benefit. Among 959 cardiac surgery patients, POAF occurred in 35% (n = 339). Inflammatory, metabolic, and neuro-endocrine biomarkers remained independently associated with POAF after applying lasso regression. Adding these biomarkers to the POAF Score improved discrimination, with the C-statistic increasing from 0.60 (95% CI: 0.60-0.60) to 0.63 (95% CI: 0.63-0.64; < 0.01). Calibration was good in both models. At a threshold of 40% for high risk of POAF, the addition of biomarkers correctly reclassified 16% of patients with POAF as high risk. However, only 2% of the patients without POAF were reclassified as low risk, while 13% were incorrectly reclassified as high risk, resulting in a net reclassification index of 0.05. The addition of pathophysiological biomarkers significantly improves the performance of an established risk model for POAF after cardiac surgery, although the incremental clinical benefit is small.
术后心房颤动(POAF)是心脏手术后常见且严重的并发症。现有的临床预测模型显示出有限的判别能力。我们假设纳入反映POAF关键病理生理途径的生物标志物可以增强术前风险分层。纳入了来自BIGPROMISE队列(一项前瞻性、观察性、两中心围手术期生物样本库研究)的无房颤病史的成年心脏手术患者,以研究与既定临床模型POAF评分相比,心肌损伤、全身炎症、血液学状态以及代谢和神经内分泌失调的生物标志物是否能改善新发POAF的预测。我们使用带收缩(套索)的多变量逻辑回归评估添加到POAF评分中的13种生物标志物组合的增量价值,评估模型的判别能力、校准、重新分类和净临床获益。在959例心脏手术患者中,35%(n = 339)发生了POAF。应用套索回归后,炎症、代谢和神经内分泌生物标志物仍与POAF独立相关。将这些生物标志物添加到POAF评分中可改善判别能力,C统计量从0.60(95%CI:0.60 - 0.60)增加到0.63(95%CI:0.63 - 0.64;P < 0.01)。两个模型的校准效果均良好。在POAF高风险阈值为40%时,添加生物标志物可将16%的POAF患者正确重新分类为高风险。然而,只有2%无POAF的患者被重新分类为低风险,而13%被错误地重新分类为高风险,导致净重新分类指数为0.05。添加病理生理生物标志物显著改善了心脏手术后POAF既定风险模型的性能,尽管增量临床获益较小。