Circulating biomarker- and magnetic resonance-based nomogram predicting long-term outcomes in dilated cardiomyopathy

Abstract

Background: Dilated cardiomyopathy (DCM) has a high mortality rate and is the most common indication for heart transplantation. Our study sought to develop a multiparametric nomogram to assess individualized all-cause mortality or heart transplantation (ACM/HTx) risk in DCM patients. Methods: The present study is a retrospective cohort study. The demographic, clinical, blood test, and cardiac magnetic resonance imaging (CMRI) data of DCM patients in the tertiary center (Fuwai Hospital) were collected. The primary endpoint was ACM/HTx. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied for variable selection. Multivariable Cox regression was used to develop a nomogram. The concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: A total of 218 patients were included in the present study. They were randomly divided into a training cohort and a validation cohort. The nomogram was established based on eight variables, including mid-wall late gadolinium enhancement, systolic blood pressure, diastolic blood pressure, left ventricular ejection fraction, left ventricular end-diastolic diameter, left ventricular end-diastolic volume index, free triiodothyronine, and N-terminal pro-B type natriuretic peptide. The AUCs regarding 1-year, 3-year, and 5-year ACM/HTx events were 0.859, 0.831, and 0.840 in the training cohort and 0.770, 0.789, and 0.819 in the validation cohort, respectively. The calibration curve and DCA showed good accuracy and clinical utility of the nomogram. Conclusions: We established and validated a circulating biomarker- and CMRI-based nomogram that could provide a personalized prediction of ACM/HTx for DCM patients, which might help risk stratification and decision-making in clinical practice.