Neural Net Modeling of Checkpoint Inhibitor Related Myocarditis and Steroid Response

Abstract

Background 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.

Development and validation of a prediction tool for intraoperative blood transfusion in brain tumor resection surgery: a retrospective analysis

“…In addition, machine learning algorithms have been increasingly used in the anesthesiology 16,17 and pain 18 fields. We included eight machine learning methods and acquired a diameter, [7] gender, [8] heart function, [9] hepatic disease, [10] multi-site tumor, [11] pulmonary disease, [12] surgery history of same site, [13] ALB, [14] APTT, [15] D-dimer, [16] HB, [17] NT-proBNP, [18] PLT, [19] PT, [20] TP, [21] WBC, and [22] fibrinogen. www.nature.com/scientificreports/ balanced performance evaluation by benchmarking various algorithms.…”

“…In our study, eight algorithms were applied to build the models and assess their predictive abilities for intraoperative blood transfusion. These algorithms included the K near neighbor algorithm (KNN) 8 , a linear discriminant analysis (LDA) 9 , a logistic regression model (LR), Naïve Bayes, support vector machine (SVM) 10 , ranger (also called randomized forest) 11 , the extremely gradient boosting machine (xgboost) 12 , and the neural net (Nnet) 13 . The machine learning performance evaluation used a tenfold CV method with 1000 iterations in the training set.…”

“…Seven features, namely hemoglobin, diameter, prothrombin time, WBC, age, the physical status of the American Society of Anesthesiologists classification, and heart function, were identified before the λ1se. Lines 1-22 indicate the exploratory variables: [1] ASA, [2] DM, [3] age, [4] cardiovascular disease, [5] cerebrovascular disease,[6] diameter,[7] gender,[8] heart function,[9] hepatic disease,[10] multi-site tumor,[11] pulmonary disease,[12] surgery history of same site,[13] ALB,[14] APTT,[15] D-dimer,[16] HB,[17] NT-proBNP,[18] PLT,[19] PT,[20] TP,[21] WBC, and[22] fibrinogen.…”