A bench to bedside perspective on anthracycline chemotherapy-mediated cardiovascular dysfunction: challenges and opportunities. A symposium review.

Cardiovascular diseases (CVD) are the leading cause of death worldwide and the risk of developing CVD is markedly increased following anthracycline chemotherapy treatment. Anthracyclines are an essential component of the cancer treatment regimen used for common forms of cancer in male and female children, adolescents, young adults, and older adults. Increased CVD risk with anthracyclines occurs, in part, due to vascular dysfunction-impaired endothelial function and arterial stiffening. These features of vascular dysfunction also play a major role in other common disorders observed following anthracycline treatment, including chronic kidney disease, dementia, and exercise intolerance. However, the mechanisms by which anthracycline chemotherapy induces and sustains vascular dysfunction are incompletely understood. This budding area of biomedical research is termed cardio-oncology, which presents the unique opportunity for collaboration between physicians and basic scientists. This symposium, presented at Experimental Biology 2022, provided a timely update on this important biomedical research topic. The speakers presented observations made at levels from cells to mice to humans treated with anthracycline chemotherapeutic agents using an array of translational research approaches. The speaker panel included a diverse mix of female and male investigators and unique insight from a cardio-oncology physician-scientist. Particular emphasis was placed on challenges and opportunities in this field as well as mechanisms that could be viewed as therapeutic targets leading to novel treatment strategies.

EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS.

The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.