Totzeck, Matthias

Abstract Cancer outcomes vary widely between individual patients, each accumulating an irregular record of treatments, diagnoses, measurements, and complications. Current prognostic models reduce this complexity into a single snapshot, focus on narrow clinical settings, and rarely generalize across hospitals. Here we introduce Chronicle , an explainable transformer that learns from entire patient trajectories to predict diverse clinical outcomes throughout the disease course while capturing both short- and long-term temporal dependencies. Trained on 53.7 million longitudinal data points from 51,711 patients spanning 67 cancer types, Chronicle operates natively on irregular data without imputation and jointly predicts eight endpoints within a flexible framework adaptable to additional outcomes. Chronicle outperformed cross-sectional models for overall survival prediction (C-index 0.84 vs 0.76-0.79), stratified patients more accurately than established prognostic systems, including TNM stage, and predicted seven adverse event and transfusion endpoints (AUC 0.80-0.92). Applied without retraining to 69,341 patients in Germany, Switzerland, and the United States, Chronicle generalized across healthcare systems and improved further with local fine-tuning. Integrated explainability traced each risk update to patient-specific clinical factors, revealing distinct temporal persistence of prognostic information, with relevance half-lives ranging from weeks for therapies to nearly one year for baseline characteristics. These findings demonstrate that learning from hospital-wide patient trajectories enables interpretable and continuously updated predictions, providing a scalable framework to support individualized treatment decisions.

Abstract Chimeric antigen receptor (CAR)-T cell therapy is the next revolutionary advance in cancer therapy. By using ex vivo engineered T cells to specifically target antigens, a targeted immune reaction is induced. Chimeric antigen receptor-T cell therapy is approved for patients suffering from advanced and refractory B cell and plasma cell malignancies and is undergoing testing for various other haematologic and solid malignancies. In the process of triggering an anticancer immune reaction, a systemic inflammatory response can emerge as cytokine release syndrome (CRS). The severity of CRS is highly variable across patients, ranging from mild flu-like symptoms to fulminant hyperinflammatory states with excessive immune activation, associated multiorgan failure and high mortality risk. Cytokine release syndrome is also an important factor for adverse cardiovascular (CV) events. Sinus tachycardia and hypotension are the most common reflections, similar to what is seen with other systemic inflammatory response syndromes. Corrected QT interval prolongation and tachyarrhythmias, including ventricular arrhythmias and atrial fibrillation, also show a close link with CRS. Events of myocardial ischaemia and venous thromboembolism can be provoked during CAR-T cell therapy. Although not as closely related to CRS, changes in cardiac function can be observed to the point of heart failure and cardiogenic shock. This may also be encountered in patients with severe valvular heart disease in the setting of CRS. This review will discuss the pertinent CV risks of the growing field of CAR-T cell therapy for today’s cardiologists, including incidence, characteristics, and treatment options, and will conclude with an integrated management algorithm.