Myocardial Flow Reserve From Positron Emission Tomography Predicts Treatment Response in Patient With 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/s0735-1097(20)34291-1

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“…19 Moreover, a retrospective study demonstrated that myocardial scar was independently associated with an increased risk of a composite of all-cause death, heart transplantation, acute coronary syndrome, hospitalization for HF, and late revascularization. 18 This study also showed that rest scar burden was an independent predictor of the prognosis of AHF with an HR (95% CI) of 1.03 (1.01-1.06). Each increase in rest scar burden by 1% significantly increased the risk of all-cause mortality by 3%; thus, rest scar burden was included in the clinical prediction model.…”

Section: Discussionsupporting

confidence: 57%

“…17 In addition, clinically meaningful improvements in cardiac resynchronization therapy (CRT) response, LV ejection fraction (LVEF), and the incidence of major adverse cardiovascular events are seen in patients with lower myocardial scar burden. [18][19][20] However, no studies have been conducted to establish a clinical prediction model using ECG-gated SPECT MPI to date.…”

Section: Introductionmentioning

confidence: 99%

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

Gao

Guo

et al. 2023

ESC Heart Failure

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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.

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Section: Discussionsupporting

confidence: 57%