Diagnostic and prognostic value of an ejection fraction corrected for myocardial remodelling

Abstract Introduction Despite clinical guideline recommendations to use left ventricular ejection fraction (EF) as a marker of left ventricular function in heart failure (HF), it is prone to measurement error, is load dependent and is skewed during geometric cardiac remodelling. EF also cannot distinguish between patients with HF with preserved EF (HFpEF) and those with a normal heart. By evaluating EF at the ventricular mid-wall layer (the corrected EF (EFc)) instead of the endocardial surface, the geometric dependency shortcomings of EF can be overcome. Purpose We evaluated whether EFc can provide additional diagnostic and prognostic information compared with traditional EF. Methods We conducted a retrospective cohort study using a dataset of all consecutive patients who were admitted to hospital following the request of a troponin across three centres between 2010 and 2017. Patients who underwent an echocardiogram either within 3 months before or 5 months after their hospital admission were included. Patients were classified according to whether their index hospital admission was due to HF, and further classified according to EF ((i) EF<40, HF with reduced EF (HFrEF); (ii) 40≤EF<50, HF with mid-range EF (HFmrEF); (iii) EF≥50, HFpEF). All-cause mortality was assessed as a competing risk to HF admission. Cox-proportional regression models were constructed to compare the EFc to EF in evaluating risk of HF admission. Results The study population consisted of 2752 individuals (56.5% male, age 69.3±16.4 years). Comparing patients with HFpEF to those with an index admission without HF, there was no difference in EF between the groups (62.3% versus 64.2%, p = 0.79), however a significant difference was observed when comparing EFc (56.6% versus 61.8%, p = 0.0007) (Figure 1). Both low EF (<50%) and low EFc (<50%) were associated with a high HF readmission risk, which were comparable. However, in the cohort with a normal EF (EF≥50%), predictive models using EFc were significantly more accurate in predicting HF readmissions within 3 years, where the leave one out cross validation ROC analysis showed a 18.6% reduction in errors when EFc replaces EF in the risk prediction models. Table 1 shows the specific IDI and NRI scores to quantify the improvements to predictions when EF or EFc are added to the baseline model (age, gender and creatinine), for assessing readmissions within 3 years in the EF≥50% cohort. The higher IDI and NRI scores for EFc compared to EF shows that adding EFc to "baseline" improves predictions more than adding EF to "baseline". The results also show that using EFc instead of EF will lead to 12.2% more patients in the event group (HF readmission) being correctly identified as higher risk, and 16.6% more patients in the non-event group (no HF readmission) being correctly identified as lower risk. Conclusions In comparison to EF, the EFc improves the prediction of hospital admissions due to HF for patients where the EF≥50%.Figure 1Table 1