Prognostic value of cardiac magnetic resonance early after ST-segment elevation myocardial infarction in older patients

Abstract

Background older patients with ST-segment elevation myocardial infarction (STEMI) represent a very high-risk population. Data on the prognostic value of cardiac magnetic resonance (CMR) in this scenario are scarce. Methods the registry comprised 247 STEMI patients over 70 years of age treated with percutaneous intervention and included in a multicenter registry. Baseline characteristics, echocardiographic parameters and CMR-derived left ventricular ejection fraction (LVEF, %), infarct size (% of left ventricular mass) and microvascular obstruction (MVO, number of segments) were prospectively collected. The additional prognostic power of CMR was assessed using adjusted C-statistic, net reclassification index (NRI) and integrated discrimination improvement index (IDI). Results during a 4.8-year mean follow-up, the number of first major adverse cardiac events (MACE) was 66 (26.7%): 27 all-cause deaths and 39 re-admissions for acute heart failure. Predictors of MACE were GRACE score (HR 1.03 [1.02–1.04], P < 0.001), CMR–LVEF (HR 0.97 [0.95–0.99] per percent increase, P = 0.006) and MVO (HR 1.24 [1.09–1.4] per segment, P = 0.001). Adding CMR data significantly improved MACE prediction compared to the model with baseline and echocardiographic characteristics (C-statistic 0.759 [0.694–0.824] vs. 0.685 [0.613–0.756], NRI = 0.6, IDI = 0.08, P < 0.001). The best cut-offs for independent variables were GRACE score > 155, LVEF < 40% and MVO ≥ 2 segments. A simple score (0, 1, 2, 3) based on the number of altered factors accurately predicted the MACE per 100 person-years: 0.78, 5.53, 11.51 and 78.79, respectively (P < 0.001). Conclusions CMR data contribute valuable prognostic information in older patients submitted to undergo CMR soon after STEMI. The Older-STEMI–CMR score should be externally validated.

MRI Investigation of the Differential Impact of Left Ventricular Ejection Fraction After Myocardial Infarction in Elderly vs. Nonelderly Patients to Predict Readmission for Heart Failure

“…There is no well-established cut-off value to define elderly population, so a 70-year cutoff was selected in accordance with our previous experience. 7,37 A relatively low incidence of AHF was recorded in our population, although concordant with contemporary registries. Furthermore, several biochemical and clinical variables such as geriatric assessment, frailty evaluation, and other imaging parameters which could have played a role in patient prognosis were not included in the registry.…”

“…Referral and survival bias cannot be excluded due to the observational nature of our study, and patients referred for MRI may not be entirely representative of the whole STEMI population. There is no well‐established cut‐off value to define elderly population, so a 70‐year cutoff was selected in accordance with our previous experience 7,37 . A relatively low incidence of AHF was recorded in our population, although concordant with contemporary registries.…”

“…Despite their increased risk of adverse events after STEMI, 4–6 elderly patients have traditionally been underrepresented in observational studies and clinical trials. Nonetheless, recent studies have focused on risk prediction in this population by clinical and MRI‐derived parameters 37,38 . Both STEMI and AHF are more prevalent in the elderly, 39 and age is among the key clinical risk factors for developing AHF after STEMI 26 .…”

“…Nonetheless, recent studies have focused on risk prediction in this population by clinical and MRI-derived parameters. 37,38 Both STEMI and AHF are more prevalent in the elderly, 39 and age is among the key clinical risk factors for developing AHF after STEMI. 26 Thus, strategies addressed specifically at identifying patients at risk of this adverse outcome stratified precisely by age are of importance.…”

The impact of tumor characteristics on cardiovascular disease death in breast cancer patients with CT or RT: a population-based study

“…Univariate and multivariate Cox regression analyses (enter method) were used to identify the risk factors of CVD death in breast cancer patients with CT or RT ( 24 – 26 ). A sensitivity analyze (3 models with increasing degrees of adjustment) was conducted to adjust for potential confounding variables at baseline ( 27 , 28 ). In detail, model 1 was adjusted for all variables with P -values less than 0.05 at univariate analysis, including age at diagnosis, marital status, race, tumor size, and stage.…”