Barbini Paolo, Barbini Emanuela, Furini Simone, Cevenini Gabriele
Department of Medical Biotechnologies, University of Siena, Viale Bracci 53100, Siena, Italy.
BMC Med Inform Decis Mak. 2014 Oct 13;14:89. doi: 10.1186/1472-6947-14-89.
Length-of-stay prediction for cardiac surgery patients is a key point for medical management issues, such as optimization of resources in intensive care units and operating room scheduling. Scoring systems are a very attractive family of predictive models, but their retraining and updating are generally critical. The present approach to designing a scoring system for predicting length of stay in intensive care aims to overcome these difficulties, so that a model designed in a given scenario can easily be adjusted over time or for internal purposes.
A naïve Bayes approach was used to develop a simple scoring system. A set of 36 preoperative, intraoperative and postoperative variables collected in a sample of 3256 consecutive adult patients undergoing heart surgery were considered as likely risk predictors. The number of variables was reduced by selecting an optimal subset of features. Scoring system performance was assessed by cross-validation.
After the selection process, seven variables were entered in the prediction model, which showed excellent discrimination, good generalization power and suitable sensitivity and specificity. No significant difference was found between AUC of the training and testing sets. The 95% confidence interval for AUC estimated by the BCa bootstrap method was [0.841, 0.883] and [0.837, 0.880] in the training and testing sets, respectively. Chronic dialysis, low postoperative cardiac output and acute myocardial infarction proved to be the major risk factors.
The proposed approach produced a simple and trustworthy scoring system, which is easy to update regularly and to customize for other centers. This is a crucial point when scoring systems are used as predictive models in clinical practice.
心脏手术患者的住院时间预测是医疗管理问题的关键，例如重症监护病房资源的优化和手术室排班。评分系统是一类非常有吸引力的预测模型，但它们的重新训练和更新通常至关重要。目前设计用于预测重症监护住院时间的评分系统的方法旨在克服这些困难，以便在给定场景中设计的模型能够随着时间的推移或出于内部目的轻松调整。
采用朴素贝叶斯方法开发一个简单的评分系统。在连续接受心脏手术的3256例成年患者样本中收集的一组36个术前、术中和术后变量被视为可能的风险预测因素。通过选择特征的最优子集来减少变量数量。通过交叉验证评估评分系统的性能。
经过选择过程，七个变量被纳入预测模型，该模型显示出出色的辨别力、良好的泛化能力以及合适的敏感性和特异性。训练集和测试集的AUC之间未发现显著差异。通过BCa自助法估计的训练集和测试集AUC的95%置信区间分别为[0.841, 0.883]和[0.837, 0.880]。慢性透析、术后低心输出量和急性心肌梗死被证明是主要风险因素。
所提出的方法产生了一个简单且可靠的评分系统，该系统易于定期更新并可针对其他中心进行定制。当评分系统在临床实践中用作预测模型时，这是一个关键点。
