Gopalan P D, Pershad S
Discipline of Anaesthesiology and Critical Care, School of Clinical Medicine, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa.
Intensive Care Unit, King Edward VIII Hospital, Durban, South Africa.
South Afr J Crit Care. 2021 Mar 17;37(1). doi: 10.7196/SAJCC.2021.v37i1.473. eCollection 2021.
The complex intensive care unit (ICU) admission decision process has numerous non-linear relationships involving multiple factors. To better describe and analyse this process, exploration of novel techniques to clearly delineate the importance and interrelationships of factors is warranted. Network analysis (NA), based on graph theory, attempts to identify patterns of connections within a network and may be useful in this regard.
To identify patterns of ICU decision-making pertaining to patients referred for admission to ICU and to identify key factors, their distribution, connection and relative importance. The secondary aim was to compare subgroups as per decision outcomes and case labels.
NA was performed using Gephi software package as a secondary analysis on a dataset generated from a previous study on ICU admission decision-making process using a 20-questions game approach. The data were standardised and coded up to a quaternary level for this analysis.
The coding process generated 31 nodes and 964 edges. Regardless of the measure used (centrality, prestige, authority and hubs), properties of the acute illness, progress of the acute illness and properties of comorbidities emerged consistently as among the most important factors and their relative rankings differed. Using different measures allowed important factors to emerge differentially. The six subgroups that emerged from the modularity measure bore little resemblance to traditional factor subgroups. Differences were noted in the subgroup comparisons of decision outcomes and case prognoses.
The use of NA with its various measures has facilitated a more comprehensive exploration of the ICU admission decision, allowing us to reflect on the process. Further studies with larger datasets are needed to elucidate the exact role of NA in decision-making processes.
We performed a novel analysis of a complex decision-making process that allowed for comparison with traditional analytic methods. It allowed for identification of key factors, their distribution, connection and relative importance. This may subsequently allow for reflection on difficult decision-making processes, thereby leading to more appropriate outcomes. Moreover, this may lead to new considerations in developing decision support systems such as the formulation of pro-forma data-capture tools (e.g. referral forms). Further, the way factors have been traditionally subgrouped may need to be reconsidered, with different subgroups being partitioned to better reflect their connection. This study offers a good basis for more advanced future studies in this area to use a new variety of analytical tools.
复杂的重症监护病房（ICU）收治决策过程存在众多涉及多种因素的非线性关系。为了更好地描述和分析这一过程，有必要探索新的技术来清晰界定各因素的重要性及其相互关系。基于图论的网络分析（NA）试图识别网络中的连接模式，在这方面可能会有所帮助。
识别ICU针对转诊至ICU的患者进行决策的模式，并确定关键因素、它们的分布、联系及相对重要性。次要目的是根据决策结果和病例标签对亚组进行比较。
使用Gephi软件包进行网络分析，作为对先前一项关于ICU收治决策过程的研究数据集的二次分析，该研究采用了20个问题的博弈方法。为进行此分析，数据被标准化并编码至四级水平。
编码过程产生了31个节点和964条边。无论使用何种度量方法（中心性、威望、权威性和枢纽性），急性疾病的属性、急性疾病的进展以及合并症的属性始终是最重要的因素之一，且它们的相对排名有所不同。使用不同的度量方法会使重要因素以不同方式显现出来。模块化度量产生的六个亚组与传统因素亚组几乎没有相似之处。在决策结果和病例预后的亚组比较中发现了差异。
使用具有多种度量方法的网络分析有助于更全面地探索ICU收治决策，使我们能够对该过程进行反思。需要使用更大的数据集进行进一步研究，以阐明网络分析在决策过程中的确切作用。
我们对一个复杂的决策过程进行了新颖的分析，使其能够与传统分析方法进行比较。它能够识别关键因素、它们的分布、联系及相对重要性。这随后可能促使人们对困难的决策过程进行反思，从而带来更合适的结果。此外，这可能会在开发决策支持系统（如制定预格式化数据采集工具，如转诊表）方面引发新的思考。此外，可能需要重新考虑传统上对因素进行亚组划分的方式，划分不同的亚组以更好地反映它们的联系。本研究为该领域未来使用各种新分析工具进行更高级的研究提供了良好基础。
