Background Extracorporeal membrane oxygenation following cardiac surgery safeguards end‐organ oxygenation but unfavorably alters cardiac hemodynamics. Along with the detrimental effects of cardiac surgery to the right heart, this might impact outcome, particularly in patients with preexisting right ventricular ( RV ) dysfunction. We sought to determine the prognostic impact of RV function and to improve established risk‐prediction models in this vulnerable patient cohort. Methods and Results Of 240 patients undergoing extracorporeal membrane oxygenation support following cardiac surgery, 111 had echocardiographic examinations at our institution before implantation of extracorporeal membrane oxygenation and were thus included. Median age was 67 years (interquartile range 60‐74), and 74 patients were male. During a median follow‐up of 27 months (interquartile range 16‐63), 75 patients died. Fifty‐one patients died within 30 days, 75 during long‐term follow‐up (median follow‐up 27 months, minimum 5 months, maximum 125 months). Metrics of RV function were the strongest predictors of outcome, even stronger than left ventricular function ( P <0.001 for receiver operating characteristics comparisons). Specifically, RV free‐wall strain was a powerful predictor univariately and after adjustment for clinical variables, Simplified Acute Physiology Score‐3, tricuspid regurgitation, surgery type and duration with adjusted hazard ratios of 0.41 (95% CI 0.24‐0.68; P =0.001) for 30‐day mortality and 0.48 (95% CI 0.33‐0.71; P <0.001) for long‐term mortality for a 1‐ SD ( SD =−6%) change in RV free‐wall strain. Combined assessment of the additive Euro SCORE and RV free‐wall strain improved risk classification by a net reclassification improvement of 57% for 30‐day mortality ( P =0.01) and 56% for long‐term mortality ( P =0.02) compared with the additive Euro SCORE alone. Conclusions RV function is strongly linked to mortality, even after adjustment for baseline variables and clinical risk scores. RV performance improves established risk prediction models for short‐ and long‐term mortality.