Rollán-Martínez-Herrera María, Kerexeta-Sarriegi Jon, Gil-Antón Javier, Pilar-Orive Javier, Macía-Oliver Iván
Cruces University Hospital, 48903 Barakaldo, Spain.
Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain.
Diagnostics (Basel). 2022 Aug 10;12(8):1932. doi: 10.3390/diagnostics12081932.
Shock is described as an inadequate oxygen supply to the tissues and can be classified in multiple ways. In clinical practice still, old methods are used to discriminate these shock types. This article proposes the application of unsupervised classification methods for the stratification of these patients in order to treat them more appropriately. With a cohort of 90 patients admitted in pediatric intensive care units (PICU), the k-means algorithm was applied in the first 24 h data since admission (physiological and analytical variables and the need for devices), obtaining three main groups. Significant differences were found in variables used (e.g., mean diastolic arterial pressure p < 0.001, age p < 0.001) and not used for training (e.g., EtCO2 min p < 0.001, Troponin max p < 0.01), discharge diagnosis (p < 0.001) and outcomes (p < 0.05). Clustering classification equaled classical classification in its association with LOS (p = 0.01) and surpassed it in its association with mortality (p < 0.04 vs. p = 0.16). We have been able to classify shocked pediatric patients with higher outcome correlation than the clinical traditional method. These results support the utility of unsupervised learning algorithms for patient classification in PICU.
休克被描述为组织的氧气供应不足，并且可以通过多种方式进行分类。在临床实践中，仍然使用旧方法来区分这些休克类型。本文提出应用无监督分类方法对这些患者进行分层，以便更恰当地治疗他们。对于90名入住儿科重症监护病房（PICU）的患者，在入院后的头24小时数据（生理和分析变量以及对设备的需求）中应用k均值算法，得到三个主要组。在所用变量（例如，平均舒张压p<0.001，年龄p<0.001）和未用于训练的变量（例如，最低呼气末二氧化碳分压p<0.001，肌钙蛋白最大值p<0.01）、出院诊断（p<0.001）和预后（p<0.05）方面发现了显著差异。聚类分类在与住院时间的关联方面与传统分类相当（p = 0.01），在与死亡率的关联方面超过了传统分类（p<0.04对p = 0.16）。我们已经能够对休克的儿科患者进行分类，其与预后的相关性高于临床传统方法。这些结果支持无监督学习算法在PICU患者分类中的实用性。
