Objective

The impact of the severity of secondary mitral regurgitation (MR) on the risk of death and heart failure (HF) hospitalisations in patients with reduced left ventricular (LV) systolic function is poorly defined. The study sought to identify the incremental risk of secondary MR in patients with reduced LV systolic function.

Methods

We studied 615 consecutive patients with LV ejection fraction ≤35% by transthoracic echocardiography at a single medical centre. Patients were divided into three groups of no MR, mild, or moderate to severe MR. The median follow-up was 2.9 years. The primary endpoint was a composite of death or HF hospitalisations.

Results

Compared with patients with no MR, the risk of death or HF hospitalisations was higher for mild MR (HR 1.7, P=0.003) and moderate to severe MR (HR 2.7, P<0.001). The risk was also higher for the component endpoints of HF hospitalisations (mild MR: HR 2.3, P=0.001; moderate to severe MR: HR 3.5, P<0.001) and death (mild MR: HR 1.6, P=0.033; moderate to severe MR: HR 2.6, P<0.001). After adjustment for other covariates, MR was no longer significantly associated with death or HF hospitalisations, or death alone, but remained significantly associated with HF hospitalisations (mild MR: HR 1.7, P=0.028; moderate to severe MR: HR 2.2, P=0.002).

Conclusions

In patients with reduced LV systolic function, secondary MR is associated with an increased risk of HF hospitalisations but not death.

Introduction

Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores.

Methods

From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data).

Results

In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events).

Conclusion

A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification.

Aims

Although there is increasing evidence supporting coronary atherosclerosis evaluation by coronary computed tomography angiography (CCTA), no data are available on age and sex differences for quantitative plaque features. The aim of this study was to investigate sex and age differences in both qualitative and quantitative atherosclerotic features from CCTA prior to acute coronary syndrome (ACS).

Methods and results

Within the ICONIC study, in which 234 patients with subsequent ACS were propensity matched 1:1 with 234 non-event controls, our current subanalysis included only the ACS cases. Both qualitative and quantitative advance plaque analysis by CCTA were performed by a core laboratory. In 129 cases, culprit lesions identified by invasive coronary angiography at the time of ACS were co-registered to baseline CCTA precursor lesions. The study population was then divided into subgroups according to sex and age (<65 vs. ≥ 65 years old) for analysis. Older patients had higher total plaque volume than younger patients. Within specific subtypes of plaque volume, however, only calcified plaque volume was higher in older patients (135.9 ± 163.7 vs. 63.8 ± 94.2 mm3, P < 0.0001, respectively). Although no sex-related differences were recorded for calcified plaque volume, females had lower fibrous and fibrofatty plaque volume than males (Fibrofatty volume 29.6 ± 44.1 vs. 75.3 ± 98.6 mm3, P = 0.0001, respectively). No sex-related differences in the prevalence of qualitative high-risk plaque features were found, even after separate analyses considering age were performed.

Conclusion

Our data underline the importance of age- and sex-related differences in coronary atherosclerosis presentation, which should be considered during CCTA-based atherosclerosis quantification.