Hayn Dieter, Kreiner Karl, Ebner Hubert, Kastner Peter, Breznik Nada, Rzepka Angelika, Hofmann Axel, Gombotz Hans, Schreier Günter
Dieter Hayn, AIT Austrian Institute of Technology, Reininghausstr. 13, 8020 Graz, Austria, Email:
Appl Clin Inform. 2017 Jun 14;8(2):617-631. doi: 10.4338/ACI-2016-11-RA-0195.
Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for an individual patient when transfusion is indicated.
It was our aim to describe, how predictive modeling and machine learning tools applied on pre-operative data can be used to predict the amount of red blood cells to be transfused during surgery and to prospectively optimize blood ordering schedules. In addition, the data derived from the predictive models should be used to benchmark different hospitals concerning their blood transfusion patterns.
6,530 case records obtained for elective surgeries from 16 centers taking part in two studies conducted in 2004-2005 and 2009-2010 were analyzed. Transfused red blood cell volume was predicted using random forests. Separate models were trained for overall data, for each center and for each of the two studies. Important characteristics of different models were compared with one another.
Our results indicate that predictive modeling applied prior surgery can predict the transfused volume of red blood cells more accurately (correlation coefficient cc = 0.61) than state of the art algorithms (cc = 0.39). We found significantly different patterns of feature importance a) in different hospitals and b) between study 1 and study 2.
We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.
输血是住院患者中非常普遍的一项操作，在某些临床情况下具有挽救生命的潜力。然而，在大多数情况下，输血是给予血流动力学稳定的患者，并无益处，反而会增加患者出现不良后果的几率以及产生大量直接和间接费用。因此，患者血液管理的概念对于预防和减少输血以及在需要输血时确定个体患者的最佳输血量越来越重要。
我们的目的是描述如何将基于术前数据应用的预测建模和机器学习工具用于预测手术期间要输注的红细胞量，并前瞻性地优化用血订购计划。此外，预测模型得出的数据应用于对不同医院的输血模式进行基准比较。
分析了从参与2004 - 2005年和2009 - 2010年进行的两项研究的16个中心获取的6530例择期手术病例记录。使用随机森林预测输注的红细胞量。针对总体数据、每个中心以及两项研究中的每一项分别训练模型。对不同模型的重要特征进行相互比较。
我们的结果表明，术前应用预测建模比现有算法（相关系数cc = 0.39）能更准确地预测红细胞输注量（相关系数cc = 0.61）。我们发现a) 在不同医院以及b) 在研究1和研究2之间，特征重要性模式存在显著差异。
我们得出结论，预测建模可用于对不同医院数据所衍生模型上不同特征的重要性进行基准比较。这可能有助于优化特定医院的关键流程，甚至在患者血液管理之外的其他场景中也是如此。
