Prognostic Value of Quantitative Metrics From Positron Emission Tomography in Ischemic Heart Failure

Phenotyping heart failure by nuclear imaging of myocardial perfusion, metabolism, and molecular targets

Nuclear imaging techniques can detect and quantify pathophysiological processes underlying heart failure, complementing evaluation of cardiac structure and function with other imaging modalities. Combined imaging of myocardial perfusion and metabolism can identify left ventricle dysfunction caused by myocardial ischaemia that may be reversible after revascularization in the presence of viable myocardium. High sensitivity of nuclear imaging to detect targeted tracers has enabled assessment of various cellular and subcellular mechanisms of heart failure. Nuclear imaging of active inflammation and amyloid deposition is incorporated into clinical management algorithms of cardiac sarcoidosis and amyloidosis. Innervation imaging has well-documented prognostic value with respect to heart failure progression and arrhythmias. Emerging tracers specific for inflammation and myocardial fibrotic activity are in earlier stages of development but have demonstrated potential value in early characterization of the response to myocardial injury and prediction of adverse left ventricular remodelling. Early detection of disease activity is a key for transition from broad medical treatment of clinically overt heart failure towards a personalized approach aimed at supporting repair and preventing progressive failure. This review outlines the current status of nuclear imaging in phenotyping heart failure and combines it with discussion on novel developments.

Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of-concept study using 13N-ammonia positron emission tomography

AIM

The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR).

METHODS

Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., < 2 and ≥ 2), as derived from quantitative PET analysis. We extracted radiomic features from retention images at each of five different gray-level (GL) discretization (8, 16, 32, 64, and 128 bins). Outcome independent and dependent feature selection and subsequent univariate and multivariate analyses was performed to identify image features predicting reduced global MFR.

RESULTS

A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion.

CONCLUSION

A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease.

Cardiac imaging for the prediction of sudden cardiac arrest in patients with heart failure

The identification of heart failure (HF) patients at risk for arrhythmic sudden cardiac arrest (SCA) is a major challenge in the cardiovascular field. In addition to optimal medical treatment for HF, implantable cardioverter defibrillator (ICD) is currently recommended to prevent SCA in patients with reduced left ventricular ejection fraction (LVEF). The indication for an ICD implantation, in addition to HF etiology, New York Health Association (NYHA) class and life expectancy, mainly depends on LVEF value at echocardiography. However, the actual role of LVEF in the prediction of SCA has recently been debated, while newer multimodality imaging techniques with increased prognostic accuracy have been developed. Speckle tracking imaging allows the quantification of mechanical dispersion, a marker of electrophysiological heterogeneity predisposing to malignant arrhythmias, while advanced cardiac magnetic resonance techniques such as myocardial T1-mapping and extracellular volume fraction assessment allow the evaluation of interstitial diffuse fibrosis. Nuclear imaging is helpful for the appraisal of sympathetic nervous system dysfunction, while newer computed tomography techniques assessing myocardial delayed enhancement allow the identification of focal myocardial scar. This review will focus on the most modern advances in the field of cardiovascular imaging along with its applications for the prediction of SCA in patients with HF. Modern artificial intelligence applications in cardiovascular imaging will also be discussed.

Positron Emission Tomography in Heart Failure: From Pathophysiology to Clinical Application

Imaging modalities are increasingly being used to evaluate the underlying pathophysiology of heart failure. Positron emission tomography (PET) is a non-invasive imaging technique that uses radioactive tracers to visualize and measure biological processes in vivo. PET imaging of the heart uses different radiopharmaceuticals to provide information on myocardial metabolism, perfusion, inflammation, fibrosis, and sympathetic nervous system activity, which are all important contributors to the development and progression of heart failure. This narrative review provides an overview of the use of PET imaging in heart failure, highlighting the different PET tracers and modalities, and discussing fields of present and future clinical application.

Construction of a web-based dynamic nomogram for predicting the prognosis in acute heart failure

AIMS

The early identification and appropriate management may provide clinically meaningful and substained benefits in patients with acute heart failure (AHF). This study aimed to develop an integrative nomogram with myocardial perfusion imaging (MPI) for predicting the risk of all-cause mortality in AHF patients.

METHODS AND RESULTS

Prospective study of 147 patients with AHF who received gated MPI (59.0 [47.5, 68.0] years; 78.2% males) were enrolled and followed for the primary endpoint of all-cause mortality. We analysed the demographic information, laboratory tests, electrocardiogram, and transthoracic echocardiogram by the least absolute shrinkage and selection operator (LASSO) regression for selection of key features. A multivariate stepwise Cox analysis was performed to identify independent risk factors and construct a nomogram. The predictive values of the constructed model were compared by Kaplan-Meier curve, area under the curves (AUCs), calibration plots, continuous net reclassification improvement, integrated discrimination improvement, and decision curve analysis. The 1, 3, and 5 year cumulative rates of death were 10%, 22%, and 29%, respectively. Diastolic blood pressure [hazard ratio (HR) 0.96, 95% confidence interval (CI) 0.93-0.99; P = 0.017], valvular heart disease (HR 3.05, 95% CI 1.36-6.83; P = 0.007), cardiac resynchronization therapy (HR 0.37, 95% CI 0.17-0.82; P = 0.014), N-terminal pro-B-type natriuretic peptide (per 100 pg/mL; HR 1.02, 95% CI 1.01-1.03; P < 0.001), and rest scar burden (HR 1.03, 95% CI 1.01-1.06; P = 0.008) were independent risk factors for patients with AHF. The cross-validated AUCs (95% CI) of nomogram constructed by diastolic blood pressure, valvular heart disease, cardiac resynchronization therapy, N-terminal pro-B-type natriuretic peptide, and rest scar burden were 0.88 (0.73-1.00), 0.83 (0.70-0.97), and 0.79 (0.62-0.95) at 1, 3, and 5 years, respectively. Continuous net reclassification improvement and integrated discrimination improvement were also observed, and the decision curve analysis identified the greater net benefit of the nomogram across a wide range of threshold probabilities (0-100% at 1 and 3 years; 0-61% and 62-100% at 5 years) compared with dismissing the included factors or using either factor alone.

CONCLUSIONS

A predictive nomogram for the risk of all-cause mortality in patients with AHF was developed and validated in this study. The nomogram incorporated the rest scar burden by MPI is highly predictive, and may help to better stratify clinical risk and guide treatment decisions in patients with AHF.

Does quantification of [11C]meta-hydroxyephedrine and [13N]ammonia kinetics improve risk stratification in ischemic cardiomyopathy

BACKGROUND

In ischemic cardiomyopathy patients, cardiac sympathetic nervous system dysfunction is a predictor of sudden cardiac arrest (SCA). This study compared abnormal innervation and perfusion measured by [11C]meta-hydroxyephedrine (HED) vs [13N]ammonia (NH3), conventional uptake vs parametric tracer analysis, and their SCA risk discrimination.

METHODS

This is a sub-study analysis of the prospective PAREPET trial, which followed ischemic cardiomyopathy patients with reduced left ventricular ejection fraction (LVEF ≤ 35%) for events of SCA. Using n = 174 paired dynamic HED and NH3 positron emission tomography (PET) scans, regional defect scores (%LV extent × severity) were calculated using HED and NH3 uptake, as well as HED distribution volume and NH3 myocardial blood flow by kinetic modeling.

RESULTS

During 4.1 years follow-up, there were 27 SCA events. HED defects were larger than NH3, especially in the lowest tertile of perfusion abnormality (P < .001). Parametric defects were larger than their respective tracer uptake defects (P < .001). SCA risk discrimination was not significantly improved with parametric or uptake mismatch (AUC = 0.73 or 0.70) compared to HED uptake defect scores (AUC = 0.67).

CONCLUSION

Quantification of HED distribution volume and NH3 myocardial blood flow produced larger defects than their respective measures of tracer uptake, but did not lead to improved SCA risk stratification vs HED uptake alone.

Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction

(1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognostic value of machine learning (ML) for the prediction of MACEs. (2) Methods: Five-hundred patients diagnosed with AMI and who had undergone successful percutaneous coronary intervention were included in the study. Logistic regression (LR) analysis was used to assess the relevance of MACEs and 24 selected clinical variables. Six ML models were developed with five-fold cross-validation in the training dataset and their ability to predict MACEs was compared to LR with the testing dataset. (3) Results: The MACE rate was calculated as 30.6% after a mean follow-up of 1.42 years. Killip classification (Killip IV vs. I class, odds ratio 4.386, 95% confidence interval 1.943–9.904), drug compliance (irregular vs. regular compliance, 3.06, 1.721–5.438), age (per year, 1.025, 1.006–1.044), and creatinine (1 µmol/L, 1.007, 1.002–1.012) and cholesterol levels (1 mmol/L, 0.708, 0.556–0.903) were independent predictors of MACEs. In the training dataset, the best performing model was the random forest (RDF) model with an area under the curve of (0.749, 0.644–0.853) and accuracy of (0.734, 0.647–0.820). In the testing dataset, the RDF showed the most significant survival difference (log-rank p = 0.017) in distinguishing patients with and without MACEs. (4) Conclusions: The RDF model has been identified as superior to other models for MACE prediction in this study. ML methods can be promising for improving optimal predictor selection and clinical outcomes in patients with AMI.

Relative Prognostic Significance of Positron Emission Tomography Myocardial Perfusion Imaging Markers in Cardiomyopathy

BACKGROUND

Rubidium-82 positron emission tomography myocardial perfusion imaging provides measurements of perfusion, myocardial blood flow and reserve (MBFR), and changes in left ventricular ejection fraction (LVEF) at rest and peak stress. Although all of these variables are known to provide prognostic information, they have not been well studied in patients with heart failure due to reduced LVEF.

METHODS

Between 2010 and 2016, 1255 consecutive unique patients with LVEF≤40% were included in this study who underwent rubidium-82 positron emission tomography myocardial perfusion imaging and did not have subsequent revascularization within 90 days. Perfusion assessment was scored semiquantitatively, and LVEF reserve (stress-rest LVEF) and global MBFR (stress/rest MBF) were quantified using automated software. Cox proportional hazards models adjusted for 14 clinical and 7 test characteristics were used to define the independent prognostic significance of MBFR on all-cause mortality.

RESULTS

Of 1255 patients followed for a mean of 3.2 years, 454 (36.2%) died. After adjusting for clinical variables, the magnitude of fixed and reversible perfusion defects was prognostic of death (P=0.02 and 0.01, respectively), while the rest LVEF was not (P=0.18). The addition of LVEF reserve did not add any incremental value, while the addition of MBFR revealed incremental prognostic value (hazard ratio per 0.1 unit decrease in MBFR=1.08 [95% CI, 1.05-1.11], P<0.001) with fixed and reversible defects becoming nonsignificant (P=0.07 and 0.29, respectively). There was no interaction between MBFR and cause of cardiomyopathy (ischemic versus nonischemic).

CONCLUSIONS

In patients with a known cardiomyopathy who did not require early revascularization, reduced MBFR as obtained by positron emission tomography myocardial perfusion imaging is associated with all-cause mortality while other positron emission tomography myocardial perfusion imaging measures were not.

Prognostic value of regional myocardial flow reserve derived from 13N-ammonia positron emission tomography in patients with suspected coronary artery disease

Purpose

To assess the prognostic value of regional quantitative myocardial flow measures as assessed by ¹³N-ammonia positron emission tomography (PET) myocardial perfusion imaging (MPI) in patients with suspected coronary artery disease (CAD).

Methods

We retrospectively included 150 consecutive patients with suspected CAD who underwent clinically indicated 13 N-ammonia PET-MPI and who did not undergo revascularization within 90 days of PET-MPI. The presence or absence of a decreased global myocardial flow reserve (i.e., MFR < 2) as well as decreased regional MFR (i.e., ≥ 2 adjacent segments with MFR < 2) was recorded, and patients were classified as having preserved global and regional MFR (MFR group 1), preserved global but decreased regional MFR (MFR group 2), or decreased global and regional MFR (MFR group 3). We obtained follow-up regarding major adverse cardiac events (MACE, i.e., a combined endpoint including all-cause death, non-fatal myocardial infarction, and late revascularization) and all-cause death.

Results

Over a median follow-up of 50 months (IQR 38–103), 30 events occurred in 29 patients. Kaplan–Meier analysis showed significantly reduced event-free and overall survival in MFR groups 2 and 3 compared to MFR group 1 (log-rank: p = 0.015 and p = 0.013). In a multivariable Cox regression analysis, decreased regional MFR was an independent predictor for MACE (adjusted HR 3.44, 95% CI 1.17–10.11, p = 0.024) and all-cause death (adjusted HR 4.72, 95% CI 1.07–20.7, p = 0.04).

Conclusions

A decreased regional MFR as assessed by 13 N-ammonia PET-MPI confers prognostic value by identifying patients at increased risk for future adverse cardiac outcomes and all-cause death.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05459-0.

Cardiac perfusion by positron emission tomography

Myocardial perfusion imaging (MPI) with positron emission tomography (PET) is an established tool for evaluation of obstructive coronary artery disease (CAD). The contemporary 3-dimensional scanner technology and the state-of-the-art MPI radionuclide tracers and pharmacological stress agents, as well as the cutting-edge image reconstruction techniques and data analysis software, have all enabled accurate, reliable and reproducible quantification of absolute myocardial blood flow (MBF), and henceforth calculation of myocardial flow reserve (MFR) in several clinical scenarios. In patients with suspected coronary artery disease, both absolute stress MBF and MFR can identify myocardial territories subtended by epicardial coronary arteries with haemodynamically significant stenosis, as defined by invasive coronary fractional flow reserve measurement. In particular, absolute stress MBF and MFR offered incremental prognostic information for predicting adverse cardiac outcome, and hence for better patient risk stratification, over those provided by traditional clinical risk predictors. This article reviews the available evidence to support the translation of the current techniques and technologies into a useful decision-making tool in real-world clinical practice.