El Jammal Thomas, Guerber Arthur, Prodel Martin, Fauter Maxime, Sève Pascal, Jamilloux Yvan
Internal Medicine, University Hospital Croix-Rousse, Hospices Civils de Lyon, 69004 Lyon, France.
Independent Researcher, 69006 Lyon, France.
J Clin Med. 2022 Oct 21;11(20):6219. doi: 10.3390/jcm11206219.
Hemophagocytic lymphohistiocytosis is a hyperinflammatory syndrome characterized by uncontrolled activation of immune cells and mediators. Two diagnostic tools are widely used in clinical practice: the HLH-2004 criteria and the Hscore. Despite their good diagnostic performance, these scores were constructed after a selection of variables based on expert consensus. We propose here a machine learning approach to build a classification model for HLH in a cohort of patients selected by glycosylated ferritin dosage in our tertiary center in Lyon, France. On a dataset of 207 adult patients with 26 variables, our model showed good overall diagnostic performances with a sensitivity of 71.4% and high specificity, and positive and negative predictive values which were 100%, 100%, and 96.9%, respectively. Although generalization is difficult on a selected population, this is the first study to date to provide a machine-learning model for HLH detection. Further studies will be required to improve the machine learning model performances with a large number of HLH cases and with appropriate controls.
噬血细胞性淋巴组织细胞增生症是一种高炎症综合征，其特征为免疫细胞和介质的不受控制的激活。两种诊断工具在临床实践中被广泛使用：HLH-2004标准和Hscore。尽管它们具有良好的诊断性能，但这些评分是在基于专家共识选择变量后构建的。我们在此提出一种机器学习方法，以在法国里昂我们的三级中心通过糖基化铁蛋白剂量选择的一组患者中构建HLH的分类模型。在一个包含26个变量的207名成年患者的数据集上，我们的模型显示出良好的总体诊断性能，灵敏度为71.4%，特异性高，阳性和阴性预测值分别为100%、100%和96.9%。尽管在选定人群上进行推广很困难，但这是迄今为止第一项提供用于HLH检测的机器学习模型的研究。需要进一步的研究以通过大量HLH病例和适当对照来提高机器学习模型的性能。
