Improving Prediction of Postoperative Atrial Fibrillation After Cardiac Surgery Using Multiple Pathophysiological Biomarkers: A Prospective Double-Centre Study

医学 内科学 心房颤动 心脏病学 前瞻性队列研究 生物标志物 逻辑回归 心脏外科 生物化学 化学
作者
Peter G. Noordzij,Maaike S. Y. Thio,Ted Reniers,Ineke M. Dijkstra,Gabriele Mondelli,Marloes Langelaan,Henk J.T. Ruven,Thijs C.D. Rettig
出处
期刊:Journal of Clinical Medicine [Multidisciplinary Digital Publishing Institute]
卷期号:14 (11): 3737-3737
标识
DOI:10.3390/jcm14113737
摘要

Background: 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. Methods: 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. Results: 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; p < 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. Conclusions: 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.
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