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Clinical decision support algorithm for prediction of postoperative atrial fibrillation following coronary artery bypass grafting.

机译：临床决策支持算法，用于预测冠状动脉搭桥术后的房颤。

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摘要

Introduction: Postoperative atrial fibrillation (POAF) is exhibited by 20-40% of patients following coronary artery bypass grafting (CABG). POAF is associated with increased long-term morbidity and mortality, as well as additional healthcare costs. I aimed to find techniques for predicting which patients are likely to develop POAF, and therefore who may benefit from prophylaxis.;Methods: Informed consent was obtained prospectively from patients attending for elective CABG. Patients were placed in the POAF group if atrial fibrillation (AF) was sustained for at least 30 seconds prior to discharge, and were placed in the 'no AF' (NOAF) group otherwise. I evaluated the performance of classifiers including binary logistic regression (BLR), k-nearest neighbors (k-NN), support vector machine (SVM), artificial neural network (ANN), decision tree, and a committee of classifiers in leave-one-out cross validation. Accuracy was calculated in terms of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and C-statistic.;Results: Consent was obtained from 200 patients. I excluded 21 patients due to postoperative administration of amiodarone, 5 due to perioperative AF ablation, and 1 due to both. Exclusions were also made for 8 patients with a history of AF, 2 patients with cardiac implantable electronic devices (CIED), and 3 patients with no CABG (valve replacement only). POAF was exhibited by 54 (34%) of patients. Factors significantly associated (P<0.05) with POAF were longer postoperative hospital stay, advanced age, larger left atrial (LA) volume, presence of valvular disease, and lower white blood cell count (WCC). Using BLR for dimensionality reduction, I created a feature vector consisting of age, presence of congestive heart failure (CHF) (P=0.06), valvular disease, WCC, and aortic valve replacement (AVR). I performed leave-one-out cross validation. In unlabeled testing data, I obtained Se=70%, Sp=56%, PPV=89%, NPV=26%, and C=58% using a committee (BLR, k-NN, and ANN).;Conclusion: My results suggest that prediction of patients likely to develop POAF is possible using established machine learning techniques, thus allowing targeting of appropriate contemporary preventative techniques in a population at risk for POAF. Studies appear warranted to discover new predictive indices that may be added to this algorithm during continued enrolment and validation.
机译：简介：冠状动脉搭桥术（CABG）后有20-40％的患者表现出术后房颤（POAF）。 POAF与长期发病率和死亡率增加以及额外的医疗保健费用有关。我的目的是找到预测哪些患者可能发展为POAF，从而使哪些患者可以受益于预防的技术。方法：前瞻性地从参加选择性CABG的患者中获得知情同意。如果出院前房颤（AF）持续至少30秒，则将患者置于POAF组，否则将患者置于'no AF'（NOAF）组。我评估了分类器的性能，这些分类器包括二进制逻辑回归（BLR），k最近邻（k-NN），支持向量机（SVM），人工神经网络（ANN），决策树，以及分类器委员会交叉验证。根据敏感性（Se），特异性（Sp），阳性预测值（PPV），阴性预测值（NPV）和C统计量计算准确性；结果：获得200例患者的同意。我排除了21例因胺碘酮的术后给药，5例因围手术期AF消融而造成的患者以及1例因两者均引起的患者。还排除了8例有AF病史的患者，2例具有心脏植入式电子设备（CIED）的患者和3例无CABG（仅用于瓣膜置换）的患者。 54名（34％）患者表现出POAF。与POAF显着相关的因素（P <0.05）为术后住院时间较长，年龄较大，左心房（LA）体积较大，瓣膜疾病的存在和白细胞计数（WCC）较低。使用BLR进行降维，我创建了一个特征向量，包括年龄，充血性心力衰竭（CHF）（P = 0.06），瓣膜疾病，WCC和主动脉瓣置换（AVR）。我进行了留一法交叉验证。在未标记的测试数据中，我使用委员会（BLR，k-NN和ANN）获得了Se = 70％，Sp = 56％，PPV = 89％，NPV = 26％和C = 58％。结果表明，使用已建立的机器学习技术可以预测可能发展为POAF的患者，因此可以将适当的当代预防技术用于有POAF风险的人群。似乎有必要进行研究以发现可以在继续注册和验证期间添加到此算法的新预测指标。

著录项

  • 作者

    Seaborn, Geoffrey E. J.;

  • 作者单位

    Queen's University (Canada).;

  • 授予单位 Queen's University (Canada).;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 156 p.
  • 总页数 156
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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