Stefanovic Filip, Gomez-Caminero Andres, Jacobs David M, Subramanian Poornima, Puzanov Igor, Chilbert Maya R, Feuerstein Steven G, Yatsynovich Yan, Switzer Benjamin, Schentag Jerome J
Department of Biomedical Engineering, University at Buffalo School of Engineering and Applied Sciences, Buffalo, NY, USA.
CPL Associates LLC, Buffalo, NY, USA.
Clin Pharmacol. 2022 Aug 10;14:69-90. doi: 10.2147/CPAA.S369008. eCollection 2022.
Serious but rare side effects associated with immunotherapy pose a difficult problem for regulators and practitioners. Immune checkpoint inhibitors (ICIs) have come into widespread use in oncology in recent years and are associated with rare cardiotoxicity, including potentially fatal myocarditis. To date, no comprehensive model of myocarditis progression and outcomes integrating time-series based laboratory and clinical signals has been constructed. In this paper, we describe a time-series neural net (NN) model of ICI-related myocarditis derived using supervised machine learning.
We extracted and modeled data from electronic medical records of ICI-treated patients who had an elevation in their troponin. All data collection was performed using an electronic case report form, with approximately 300 variables collected on as many occasions as available, yielding 6000 data elements per patient over their clinical course. Key variables were scored 0-5 and sequential assessments were used to construct the model. The NN model was developed in MatLab and applied to analyze the time course and outcomes of treatments.
We identified 23 patients who had troponin elevations related to their ICI therapy, 15 of whom had ICI-related myocarditis, while the remaining 8 patients on ICIs had other causes for troponin elevation, such as myocardial infarction. Our model showed that troponin was the most predictive biomarker of myocarditis, in line with prior studies. Our model also identified early and aggressive use of steroid treatment as a major determinant of survival for cases of grade 3 or 4 ICI-related myocarditis.
Our study shows that a supervised learning NN can be used to model rare events such as ICI-related myocarditis and thus provide clinical insight into drivers of progression and treatment outcomes. These findings direct attention to early detection biomarkers and clinical symptoms as the best means of implementing early and potentially life-saving steroid treatment.
免疫疗法相关的严重但罕见的副作用给监管机构和从业者带来了难题。免疫检查点抑制剂（ICI）近年来在肿瘤学中已广泛应用，且与罕见的心脏毒性有关，包括潜在致命的心肌炎。迄今为止，尚未构建出一个整合基于时间序列的实验室和临床信号的心肌炎进展及预后综合模型。在本文中，我们描述了一种使用监督式机器学习得出的ICI相关心肌炎的时间序列神经网络（NN）模型。
我们从接受ICI治疗且肌钙蛋白升高的患者的电子病历中提取数据并进行建模。所有数据收集均使用电子病例报告表，尽可能多地收集约300个变量，每位患者在其临床过程中产生6000个数据元素。关键变量评分为0至5分，并使用连续评估来构建模型。NN模型在MatLab中开发，并应用于分析治疗的时间进程和结果。
我们确定了23例肌钙蛋白升高与ICI治疗相关的患者，其中15例患有ICI相关心肌炎，而其余8例接受ICI治疗的患者肌钙蛋白升高有其他原因，如心肌梗死。我们的模型表明，肌钙蛋白是心肌炎最具预测性的生物标志物，这与先前的研究一致。我们的模型还确定，早期积极使用类固醇治疗是3级或4级ICI相关心肌炎病例生存的主要决定因素。
我们的研究表明，监督学习的NN可用于对ICI相关心肌炎等罕见事件进行建模，从而为进展驱动因素和治疗结果提供临床见解。这些发现将注意力引向早期检测生物标志物和临床症状，将其作为实施早期且可能挽救生命的类固醇治疗的最佳手段。
