부산대학교 도서관

Filip Stefanovic,1,2 Andres Gomez-Caminero,3 David M Jacobs,2,4 Poornima Subramanian,2 Igor Puzanov,5,6 Maya R Chilbert,4 Steven G Feuerstein,4 Yan Yatsynovich,6,7 Benjamin Switzer,5 Jerome J Schentag2,4,5 1Department of Biomedical Engineering, University at Buffalo School of Engineering and Applied Sciences, Buffalo, NY, USA; 2CPL Associates LLC, Buffalo, NY, USA; 3Worldwide Health Economic and Outcomes Research, Bristol Myers Squibb, Princeton, NJ, USA; 4Department of Pharmacy Practice, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY, USA; 5Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA; 6Department of Medicine, University at Buffalo Jacobs School of Medicine, Buffalo, NY, USA; 7Kettering Medical Center, Kettering, OH, USACorrespondence: Jerome J Schentag, CPL Associates LLC, 73 High St. Suite 310, Buffalo, NY, 14203, USA, Tel +1 716-867-0550, Fax +1 716-633-3331, Email Jerome.schentag@live.comBackground: 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.Methods: 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.Results: 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.Conclusion: 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.Keywords: NN modeling, myocarditis, checkpoint inhibitor, steroid response, dose and timing