Improving prognostic accuracy in ischemic cardiomyopathy: the CMR-LGE score

Abstract Background There is a need for accurate prognostic stratification tools in patients with ischemic cardiomyopathy (ICM). Several studies have shown the considerable impact of cardiac magnetic resonance (CMR) imaging findings notably late gadolinium enhancement (LGE) but an easy interpretable score to guide clinical practice is lacking. Purpose To determine the CMR parameters most predictive of mortality in a cohort of ICM patients with left ventricular ejection fraction (LVEF) less than 50%, and then to develop a readily interpretable score based on these parameters. Methods Between 2008 and 2022, all consecutive patients with a history of ICM with LVEF<50% and presence of ischemic LGE on viability CMR examination, were recruited by two independent centers. The primary outcome was all-cause death using French National Registry of Death. The first center (N=2,900) was designated as the derivation cohort for variable selection and score development, and the second center (N=691) was used as the validation cohort to evaluate the performance of the final score. Thirty-two variables (17 clinical and 15 CMR) were initially assessed. Automatic feature selection was performed using a machine-learning approach with a Random Survival Forest (RSF) algorithm. Using the selected variables, our proposed score, the CMR-LGE score, was derived from coefficients of the Cox regression. Performance evaluation on the validation cohort was conducted using Harrel’s C index compared with traditional prognostic factors associated with poor outcomes in ICM. Categories for the prognostic score were identified using a survival conditional inference tree analysis on the derivation cohort, aiming to maximize the log-rank score. Results Among the 3,591 patients included (mean age 65±12 years; 75% male; mean LVEF 44±6%), 549 (15.3%) died over a median follow-up of 9 (interquartile range: 7–12) years. Feature selection with RSF highlighted that the most important variables to predict death were LGE variables: the extent, the location (septal and anterior) and the transmurality of ischemic LGE as well as the extent of additional midwall LGE (Figure 1). Following these findings, we developed the CMR-LGE score by rounding the coefficient of a Cox regression (Figure 2A). Harrel’s C index of CMR-LGE score outperformed traditional prognostic factors, including LVEF (0.88 vs 0.69). Finally, based on our CMR-LGE score, we identified a low-risk population (CMR-LGE score below 6) and a high-risk population (CMR-LGE above 8), validated using survival curves in the validation cohort (Figure 2B). Conclusion Using RSF, we identified that the 5 most important CMR variables to predict mortality in ICM were all LGE features. Our CMR-LGE score showed excellent performance to stratify patient risk compared to traditional prognostic factors including LVEF.Variable selectionModel building and evaluation