DCM-PROGRESS: predicting end-stage heart failure in non-ischemic dilated cardiomyopathy patients.

Aims: Patients with non-ischemic dilated cardiomyopathy (DCM) are at considerable risk for end-stage heart failure (HF), requiring close monitoring to identify early signs of disease. We aimed to develop a model to predict the 5-years risk of end-stage HF, allowing for tailored patient monitoring and management. Methods and results: Derivation data were available from a Dutch cohort of 293 DCM patients, with external validation available from a Czech Republic cohort of 235 DCM patients. Candidate predictors spanned patient and family histories, ECG and echocardiogram measurements, and biochemistry. End-stage HF was defined as a composite of death, heart transplantation, or implantation of a ventricular assist device. Lasso and sigmoid kernel support vector machine (SVM) algorithms were trained using cross-validation. During follow-up 65 (22%) of Dutch DCM patients developed end-stage HF, with 27 (11%) cases in the Czech cohort. Out of the two considered models, the lasso model (retaining NYHA class, heart rate, systolic blood pressure, height, R-axis, and TAPSE as predictors) reached the highest discriminative performance (testing c-statistic of 0.85, 95%CI 0.58; 0.94), which was confirmed in the external validation cohort (c-statistic of 0.75, 95%CI 0.61; 0.82), compared to a c-statistic of 0.69 for the MAGGIC score. Both the MAGGIC score and the DCM-PROGRESS model slightly over-estimated the true risk, but were otherwise appropriately calibrated. Conclusion: We developed a highly discriminative risk-prediction model for end-stage HF in DCM patients. The model was validated in two countries, suggesting the model can meaningfully improve clinical decision-making.

Respiratory-induced hemodynamic changes measured by whole-body multichannel impedance plethysmography.

The cardiovascular system is described by parameters including blood flow, blood distribution, blood pressure, heart rate and pulse wave velocity. Dynamic changes and mutual interactions of these parameters are important for understanding the physiological mechanisms in the cardiovascular system. The main objective of this study is to introduce a new technique based on parallel continuous bioimpedance measurements on different parts of the body along with continuous blood pressure, ECG and heart sound measurement during deep and spontaneous breathing to describe interactions of cardiovascular parameters. Our analysis of 30 healthy young adults shows surprisingly strong deep-breathing linkage of blood distribution in the legs, arms, neck and thorax. We also show that pulse wave velocity is affected by deep breathing differently in the abdominal aorta and extremities. Spontaneous breathing does not induce significant changes in cardiovascular parameters.