Towards Remote Continuous Monitoring of Cytokine Release Syndrome

Cytokine release syndrome (CRS) is a noninfec-tious systemic inflammatory response syndrome condition and a principle severe adverse event common in oncology patients treated with immunotherapies. Accurate monitoring and timely prediction of CRS severity remain a challenge. This study presents an XGBoost-based machine learning algorithm for forecasting CRS severity (no CRS, mild- and severe-CRS classes) in the 24 hours following the time of prediction utilizing the common vital signs and Glasgow coma scale (GCS) questionnaire inputs. The CRS algorithm was developed and evaluated on a cohort of patients (n=1,139) surgically treated for neoplasm with no ICD9 codes for infection or sepsis during a collective 9,892 patient-days of monitoring in ICU settings. Different models were trained with unique feature sets to mimic practical monitoring environments where different types of data availability will exist. The CRS models that incorporated all time series features up to the prediction time showcased a micro-average area under curve (AUC) statistic for the receiver operating characteristic curve (ROC) of 0.94 for the 3 classes of CRS grades. Models developed on a second cohort requiring data within the 24 hours preceding prediction time showcased a relatively lower 0.88 micro-average AUROC as these models did not benefit from implicit information in the data availability. Systematic removal of blood pressure and/or GCS inputs revealed significant decreases (p<0.05) in model performances that confirm the importance of such features for CRS prediction. Accurate CRS prediction and timely intervention can reverse CRS adverse events and maximize the benefit of immunotherapies in oncology patients.