Department of Surgery and Bioengineering, University of Siena, Siena, Italy.
J Eval Clin Pract. 2013 Feb;19(1):25-9. doi: 10.1111/j.1365-2753.2011.01762.x. Epub 2011 Aug 23.
RATIONALE, AIMS AND OBJECTIVES: Transfusion of allogeneic blood products is a key issue in cardiac surgery. Although blood conservation and standard transfusion guidelines have been published by different medical groups, actual transfusion practices after cardiac surgery vary widely among institutions. Models can be a useful support for decision making and may reduce the total cost of care. The objective of this study was to propose and evaluate a procedure to develop a simple locally customized decision-support system.
We analysed 3182 consecutive patients undergoing cardiac surgery at the University Hospital of Siena, Italy. Univariate statistical tests were performed to identify a set of preoperative and intraoperative variables as likely independent features for planning transfusion quantities. These features were utilized to design a naïve Bayes classifier. Model performance was evaluated using the leave-one-out cross-validation approach. All computations were done using spss and matlab code.
The overall correct classification percentage was not particularly high if several classes of patients were to be identified. Model performance improved appreciably when the patient sample was divided into two classes (transfused and non-transfused patients). In this case the naïve Bayes model correctly classified about three quarters of patients with 71.2% sensitivity and 78.4% specificity, thus providing useful information for recognizing patients with transfusion requirements in the specific scenario considered.
Although the classifier is customized to a particular setting and cannot be generalized to other scenarios, the simplicity of its development and the results obtained make it a promising approach for designing a simple model for different heart surgery centres needing a customized decision-support system for planning transfusion requirements in intensive care unit.
背景、目的和目标：异体输血是心脏手术中的一个关键问题。尽管不同的医学团体已经发布了血液保护和标准输血指南，但心脏手术后的实际输血实践在各机构之间差异很大。模型可以为决策提供有用的支持，并可能降低护理总成本。本研究的目的是提出并评估一种开发简单的本地定制决策支持系统的方法。
我们分析了意大利锡耶纳大学医院连续进行的 3182 例心脏手术患者。进行单变量统计检验，以确定一组术前和术中变量作为可能的独立输血量规划特征。这些特征被用于设计朴素贝叶斯分类器。使用留一交叉验证方法评估模型性能。所有计算均使用 spss 和 matlab 代码完成。
如果要识别出几类患者，整体正确分类百分比并不是特别高。当将患者样本分为两类（输血和非输血患者）时，模型性能显著提高。在这种情况下，朴素贝叶斯模型正确分类了大约四分之三的输血患者，敏感性为 71.2%，特异性为 78.4%，从而为识别特定情况下有输血需求的患者提供了有用的信息。
尽管分类器是为特定环境定制的，不能推广到其他情况，但它的开发简单性和所获得的结果使其成为设计用于不同心脏手术中心的简单模型的有前途的方法，这些中心需要在重症监护室中规划输血需求的定制决策支持系统。
