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

Benz, Dominik C.; Kaufmann, P; Felten, Elia von; Benetos, Georgios; Rampidis, Georgios; Messerli, Michael; Giannopoulos, Andreas A.; Fuchs, Tobias A.; Gräni, Christoph; Gebhard, Cathérine; Pazhenkottil, Aju P.; Flammer, Andreas J.; Kaufmann, Philipp; Buechel, Ronny R.

doi:10.1016/j.jcmg.2020.05.033

Cited by 18 publications

(13 citation statements)

References 37 publications

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“…Besides the potential of providing important mechanistic insights, a thus functionalized MFR could lead to developments of more targeted therapeutic strategies for raising the vasodilator capacity of the coronary circulation. If in fact MFR does outperform other variables like LVEF, size and severity of perfusion defects and metabolic evidence of myocardial viability in the prediction of MACE, as recently observed in patients with ischemic cardiomyopathy, 5 then MFR becomes a highly critical determinant of the clinical outcome. Accordingly, functionalizing the MFR in the search for targeted therapeutic strategies will be a worthwhile and therapeutically important goal, especially for meeting an unmet therapeutic need in patients with CMD without obstructive coronary disease.…”

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confidence: 83%

“…For example, two studies report significantly better event-free survival outcomes after CABG than after PCI. 4,5 However, impaired MFRs without obstructive coronary disease remain therapeutically challenging because pathophysiologic mechanisms of the impaired vasodilator flow response are not fully understood. Impairments of MFR without obstructive disease are frequently present in patients referred to PET evaluation because of clinical signs of ischemia including angina and ischemic ECG changes.…”

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confidence: 99%

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Let’s functionalize the myocardial flow reserve

Schelbert

2021

Journal of Nuclear Cardiology

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confidence: 83%

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confidence: 99%

Let’s functionalize the myocardial flow reserve

Schelbert

2021

Journal of Nuclear Cardiology

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“…The present study is a retrospective cohort study comprising consecutive patients from the "Zurich Quantitative PET Registry." The latter comprises consecutive patients who underwent 13 N-ammonia PET-MPI at our institution between 2005 and 2015 [14,15]. We identified all patients who underwent PET-MPI due to suspected CAD and excluded those with incomplete or erroneous PET-MPI datasets and those who underwent revascularization within 90 days after PET-MPI.…”

Section: Study Design and Populationmentioning

confidence: 99%

“…Several studies have demonstrated the prognostic value of absolute MBF values (hyperemic MBF or MFR) derived from PET-MPI [8][9][10][11][12][13][14]. However, the vast majority of these studies have focused on global MBF assessment, encompassing the entire myocardium.…”

Section: Introductionmentioning

confidence: 99%

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

Felten

Benz

Benetos

et al. 2021

Eur J Nucl Med Mol Imaging

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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.

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“…Advancements in healthcare, the survival rate, clinical symptoms, and quality of life after successful percutaneous coronary intervention (PCI) and other therapeutic strategies have improved outcomes for patients with AMI [2]. However, major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death, still occur in 20-40% of patients with AMI within 2 years after disease onset [3,4].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction

Xiao

Guo

Zhao

et al. 2022

JCDD

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(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.

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

Cited by 18 publications

References 37 publications

Let’s functionalize the myocardial flow reserve

Let’s functionalize the myocardial flow reserve

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

Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction

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