Steltzer H, Trummer B, Höltermann W, Kolousek G, Fridrich P, Lewandowski K, Adlassnig K P, Hammerle A F
Universitätsklinik für Anaesthesiologie und Allgemeine Intensivmedizin, AKH Wien.
Anasthesiol Intensivmed Notfallmed Schmerzther. 1999 Apr;34(4):218-23. doi: 10.1055/s-1999-181.
Since the treatment of patients with severe ARDS using the extracorporal lung assist (ECLA) methods remains a cost intensive and speculative procedure, a knowledge based computer system should be created and evaluated in order to support clinical decisions.
The model was based on the fuzzy set theory and therefore able to give decisions between yes and no, that means that a criterion could also be fulfilled to 35% or 80% for example. The development of this computer program consists of two steps: first, the entry criteria for the ECLA therapy were established within a framework of an international evaluation of clinical data from 3 centres (Berlin, Marburg, Vienna). Here, inherent vagueness, uncertainty of the occurrence and limited availability of medical data are to be considered to establish a useful tool. Secondly, this was done by grouping and weighting of parameters by the system and the status of each patient or patient group was assigned by the percentage of fulfillment of the criterion.
By using a mixed sample of patients from these three centres, the fulfillment of entry criteria according either to definitions of Berlin or to definition of Marburg was different (68% versus 36%). Other differences (36% vs. 22% and 68% vs. 60%) were found between the fuzzy based score and the crisp score which represents the usually performed method.
This now preevaluated minimal data set to describe severe ARDS patients based on the fuzzy set theory may be useful to evaluate patients for ECLA therapy or for another controlled ARDS-therapy.
由于使用体外肺辅助（ECLA）方法治疗重症急性呼吸窘迫综合征（ARDS）患者仍然是一种成本高昂且具有投机性的程序，因此应创建并评估一个基于知识的计算机系统，以支持临床决策。
该模型基于模糊集理论，因此能够在“是”与“否”之间做出决策，这意味着例如一个标准也可以以35%或80%的程度得到满足。这个计算机程序的开发包括两个步骤：首先，在对来自3个中心（柏林、马尔堡、维也纳）的临床数据进行国际评估的框架内，确定ECLA治疗的入选标准。在此，要考虑医学数据的内在模糊性、发生的不确定性和有限的可获得性，以建立一个有用的工具。其次，通过系统对参数进行分组和加权来完成这一过程，并根据标准的满足百分比为每个患者或患者组确定状态。
通过使用来自这三个中心的患者混合样本，根据柏林定义或马尔堡定义，入选标准的满足情况有所不同（分别为68%和36%）。在基于模糊的评分与代表通常执行方法的清晰评分之间还发现了其他差异（分别为36%对22%和68%对60%）。
这个基于模糊集理论预先评估的用于描述重症ARDS患者的最小数据集，可能有助于评估ECLA治疗或其他可控ARDS治疗的患者。
