Predictor Of Major Adverse Cardiac Events Research Articles

Angiography-based Fractional Flow Reserve for the Prediction of Clinical Outcomes After Drug-coated Balloon or Plain Old Balloon Angioplasty.

AccuFFRangio is a novel method for fast computation of fractional flow reserve (FFR) based on coronary angiography and computational fluid dynamics. The association between the AccuFFRangio and clinical outcomes after drug-coated balloon (DCB) or plain old balloon angioplasty (POBA) remains to be investigated. This study included consecutive patients who underwent balloon angioplasty from December 2016 to October 2020. AccuFFRangio was calculated retrospectively based on the post-procedural angiography obtained immediately after angioplasty. The primary endpoint was major adverse cardiac events (MACE), defined as a composite of all-cause death, vessel-related myocardial infarction, and repeat target vessel revascularization. A total of 169 patients were retrospectively analyzed in this study. Post-procedural AccuFFRangio (hazard ratio [HR] per 0.1 increase 0.33, 95% confidence interval [CI] 0.22-0.48, p < 0.001) was an independent predictor for MACE at 2-year follow-up. Post-procedural AccuFFRangio ≤ 0.87 was determined as the optimal cutoff value to predict MACE with an area under the curve (AUC) of 0.872 (95% CI 0.813-0.919, p < 0.001). AccuFFRangio measured immediately after balloon angioplasty is a promising predictor of unfavorable clinical outcomes.

#452 Cardiac troponins are independent predictors of cardiovascular mortality in haemodialysis patients

Abstract Background and Aims Patients with end-stage kidney disease (ESKD) and haemodialysis treatment (HD) have a high risk of cardiovascular (CV) events. The underlying pathophysiology is complex and multifactorial. This study aimed to explore the role of high-sensitivity cardiac Troponin I (hs-cTnI) as a predictor of major adverse cardiac events (MACE), CV- and all-cause mortality. Method A retrospective analysis using data from the AURORA study (CT00240331). Association with baseline hs-cTnI and MACE, CV- and all-cause mortality were assessed using Cox-regression analyses. The analyses were adjusted for multiple background factors and available biomarkers. hs-cTnI was log2 transformed and modelled using a four knot restricted cubic spline. The hazard ratio [HR] estimates are presented for the upper quartile (32.6 pg/mL) vs the lower quartile (10.1 pg/mL) of the distribution of hs-cTnI. Variables were ordered by variable importance in the models using χ2 value minus degrees of freedom. Results During the follow-up, there were 1094 total deaths of which 598 were CV deaths, and 734 MACE in patients with hsTnI measurements available at baseline. Baseline hs-cTnI was associated with an increased risk for MACE (HR 1.92; 95% Confidence interval [CI] 1.57-2.35), CV mortality (HR 2.12; 95% CI 1.69-2.66), all-cause mortality (HR 1.84; 95% CI 1.55-2.17) and non-CV mortality (HR 1.59; 95% CI 1.23-2.05) after adjustments for age, ethnicity, sex, smoking status, diabetes status, history of coronary heart disease, atrial fibrillation, blood pressure, heart rate, height, weight, years on HD, hemoglobin, cholesterol, LDL-C, HDL-C, triglycerides, hs-CRP, creatinine, albumin, phosphate, erythropoietin treatment, ACE -inhibitor use, anticoagulation treatment, treatment with rosuvastatin, sevelamer use, iron supplements, ferritin, transferrin, galectin-3, pro-BNP, CICP and SCF. When ranking variables by variable importance, hs-cTnI was the strongest predictor for MACE (Fig. 1), CV- and all-cause mortality. For non-CV mortality age was the most important variable. Conclusion Baseline hs-cTnI, is a strong and independent risk marker for MACE and all-cause mortality in patients with haemodialysis treatment.

Efficacy and safety of avatrombopag in aplastic anemia patients with liver disease.

Abstract Background and Objectives Hyperglycemia is associated with adverse outcomes in patients with acute myocardial infarction (AMI) as well as in patients with heart failure. However, the significance of admission glycemic variability (GV) in predicting outcomes among diabetes patients with heart failure (HF) following acute ST-segment elevation myocardial infarction (ASTEMI) remains unclear. This study aims to explore the prognostic value of admission GV and admission glycosylated hemoglobin (HbA1c) levels in individuals diagnosed with type 2 diabetes and HF following ASTEMI. Methods We measured GV and HbA1c upon admission in 484 consecutive patients diagnosed with type 2 diabetes and HF following ASTEMI. GV, indicated as the mean amplitude of glycemic excursions (MAGE), was assessed utilizing a continuous glucose monitoring system (CGMS). admission MAGE values were categorized as &lt; 3.9 or ≥ 3.9 mmol/L, while HbA1c levels were classified as &lt; 6.5 or ≥ 6.5%. Participants were followed up prospectively for 12 months. The relationship of admission MAGE and HbA1c to the major adverse cardiac event (MACE) of patients with type 2 diabetes and HF following ASTEMI was analyzed. Results Among the 484 enrolled patients, the occurrence of MACE differed significantly based on MAGE categories (&lt; 3.9 vs. ≥ 3.9 mmol/L), with rates of 13.6% and 25.3%, respectively (P = 0.001). While MACE rates varied by HbA1c categories (&lt; 6.5 vs. ≥ 6.5%) at 15.7% and 21.8%, respectively (P = 0.086). Patients with higher MAGE levels exhibited a notably elevated risk of cardiac mortality and an increased incidence of HF rehospitalization. The Kaplan-Meier curves analysis demonstrated a significantly lower event-free survival rate in the high MAGE level group compared to the low MAGE level group (log-rank test, P &lt; 0.001), while HbA1c did not exhibit a similar distinction. In multivariate analysis, high MAGE level was significantly associated with incidence of MACE (hazard ratio 3.645, 95% CI 1.287–10.325, P = 0.015), whereas HbA1c did not demonstrate a comparable association (hazard ratio 1.075, 95% CI 0.907-1.274, P = 0.403). Conclusions Elevated admission GV emerges as a more significant predictor of 1-year MACE in patients with type 2 diabetes and HF following ASTEMI, surpassing the predictive value of HbA1c.

Efficacy of revascularization in CTO patients based on hibernating myocardium therapy.

The effectiveness of percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) is still uncertain, especially for patients with ischemic left ventricular dysfunction. This study aimed to assess hibernating myocardium (HM), as determined by single-photon emission computed tomography (SPECT) and 18F-FDG positron emission tomography (PET), and to compare the benefits of PCI and optimal medical therapy (OMT). A retrospective study collected data from 332 patients with CTO and ischemic left ventricular dysfunction. The study compared patients who underwent PCI or OMT via propensity score matching (PSM) analysis which was performed with a 1:2 matching protocol using the nearest neighbour matching algorithm. The primary endpoint of the study was the occurrence of major adverse cardiac events (MACE), defined as a composite of cardiac death, readmission for worsening heart failure (WHF), revascularization and myocardial infarction (MI). After PSM, there were a total of 246 individuals in the PCI and OMT groups. Following Cox regression, hibernating myocardium/total perfusion defect (HM/TPD) was identified as an independent risk factor (hazard ratio (HR): 1.03, 95% confidence interval (CI): 1.008-1.052, p = .007). The cut-off value of HM/TPD was 38%. The results of the subgroup analysis suggest that for patients with HM/TPD >38%, the OMT group had a greater risk of MACE (p = .035). A sensitivity analysis restricting patients with single-vessel CTO lesions, HM/TPD remained an independent predictor (HR 1.025, 95% CI 1.008-1.043, p = .005). HM/TPD is an independent predictor of MACE, and for patients with HM/TPD > 38%, CTO-PCI had a lower risk of MACE compared with OMT. However, further validation is still needed through large-scale studies.

Prognostic Value of Microvascular Resistance Reserve Measured Immediately After PCI in Stable Coronary Artery Disease.

Microvascular resistance reserve (MRR) has been proposed as a specific metric to quantify coronary microvascular function. The long-term prognostic value of MRR measured in stable patients immediately after percutaneous coronary intervention (PCI) is unknown. This study sought to determine the prognostic value of MRR measured immediately after PCI in patients with stable coronary artery disease. This study included 502 patients with stable coronary artery disease who underwent elective PCI and coronary physiological measurements, including pressure and flow estimation using a bolus thermodilution method after PCI. MRR was calculated as coronary flow reserve divided by fractional flow reserve times the ratio of mean aortic pressure at rest to that at maximal hyperemia induced by hyperemic agents. An abnormal MRR was defined as ≤2.5. Major adverse cardiac events (MACEs) were defined as a composite of all-cause mortality, any myocardial infarction, and target-vessel revascularization. During a median follow-up of 3.4 years, the cumulative MACE rate was significantly higher in the abnormal MRR group (12.5 versus 8.3 per 100 patient-years; hazard ratio 1.53 [95% CI, 1.10-2.11]; P<0.001). A higher all-cause mortality rate primarily drove this difference. On multivariable analysis, a higher MRR value was independently associated with lower MACE and lower mortality. When comparing 4 subgroups according to MRR and the index of microcirculatory resistance, patients with both abnormal MRR and index of microcirculatory resistance (≥25) had the highest MACE rate. An abnormal MRR measured immediately after PCI in patients with stable coronary artery disease is an independent predictor of MACE, particularly all-cause mortality.

Clinical impact of complex percutaneous coronary intervention in the pre-TAVR workup

Introduction and objectivesIn patients undergoing percutaneous coronary intervention (PCI) in the workup for transcatheter aortic valve replacement (TAVR), the clinical impact of coronary revascularization complexity remains largely unknown. This study sought to examine the impact of PCI complexity on clinical outcomes after TAVR in patients undergoing PCI in the preprocedural workup. MethodsThis was a multicenter study including consecutive patients scheduled for TAVR with concomitant significant coronary artery disease. Complex PCI was defined as having at least 1 of the following features: 3 vessels treated, ≥ 3 stents implanted, ≥ 3 lesions treated, bifurcation with 2 stents implanted, total stent length >60mm, or chronic total occlusion. The rates of major adverse cardiac events (MACE), including cardiovascular mortality, myocardial infarction, and coronary revascularization were evaluated. ResultsA total of 1550 patients were included, of which 454 (29.3%) underwent complex PCI in the pre-TAVR workup. After a median follow-up period of 2 [1-3] years after TAVR, the incidence of MACE was 9.6 events per 100 patients-years. Complex PCI significantly increased the risk of cardiovascular death (HR, 1.44; 95%CI, 1.01-2.07), nonperiprocedural myocardial infarction (HR, 1.52; 95%CI, 1.04-2.21), and coronary revascularization (HR, 2.46; 95%CI, 1.44-4.20). In addition, PCI complexity was identified as an independent predictor of MACE after TAVR (HR, 1.44; 95%CI, 1.09-1.83; P=.009). ConclusionsIn TAVR candidates with significant coronary artery disease requiring percutaneous treatment, complex revascularization was associated with a higher risk of MACE. The degree of procedural complexity should be considered a strong determinant of prognosis in the PCI-TAVR population.

Incremental value of myocardial global longitudinal strain in predicting major adverse cardiac events among patients with hypertrophic cardiomyopathy.

Endocardial global longitudinal strain (endo-GLS) measured with echocardiography (echo) has been demonstrated to be associated with myocardial fibrosis (MF) and is a prognostic predictor in patients with hypertrophic cardiomyopathy (HCM). Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging showed that MF is primarily located in the myocardial layer of the extremely hypertrophic septal or ventricular wall. We hypothesized that GLS of the myocardial layer (myo-GLS) is more strongly correlated with the extent of LGE (%LGE) and is a more powerful prognostic factor than endo-GLS. A total of 177 inpatients (54.0 [IQR: 43.0, 64.0] years, female 37.3%) with HCM were retrospectively included from May 2019 to April 2021. Among them, 162 patients underwent echocardiographic examination and contrast-enhanced CMR within 7 days. Myo-GLS and %LGE were blindly assessed in a core laboratory. All the patients were followed after they were discharged. During a mean follow-up of 33.77 [IQR 30.05, 35.40] months, 14 participants (7.91%) experienced major adverse cardiac events (MACE). The MACE (+) group showed lower absolute endo-GLS and myo-GLS than the MACE (-) group. Myo-GLS was more associated with %LGE (r=-.68, P<.001) than endo-GLS (r=-.64, P<.001). Cox multivariable analysis indicated that absolute myo-GLS was independently associated with MACE (adjusted hazard ratio=.75, P<.05). Myo-GLS was better than endo-GLS at detecting MACE (+) patients (-8.64%, AUC.939vs. - 16.375%, AUC.898, P<.05). Myo-GLS is a stronger predictor of MACE than endo-GLS in patients with HCM and is highly correlated with %LGE.

Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease.

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This study explores the potential of AI-assisted measurements in enhancing the prediction of major adverse cardiac events (MACE) in patients with coronary artery disease (CAD). We conducted a retrospective cohort study involving patients diagnosed with CAD who underwent coronary computed tomography angiography (CCTA). Participants were classified into MACE and non-MACE groups based on their clinical outcomes. Clinical characteristics and AI-assisted measurements of CCTA parameters, including CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI), were collected. Both univariate and multivariable logistic regression analyses were performed to identify independent predictors of MACE, which were used to build predictive models. Statistical analyses revealed three independent predictors of MACE: severe stenosis, CT-FFR ≤ 0.8, and mean FAI (P < 0.05). Seven predictive models incorporating various combinations of these predictors were developed. The model combining all three predictors demonstrated superior performance, as evidenced by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.811 (95% CI 0.774 - 0.847), a sensitivity of 0.776, and a specificity of 0.726. Our findings suggest that AI-assisted CCTA analysis, particularly using FFR and FAI, could significantly improve the prediction of MACE in CAD patients, thereby potentially aiding clinical decision-making.

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [82\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{82}$$\\end{document}Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model (p<0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<0.001$$\\end{document}), where the best AUC of 73.9% (CI 72.5–75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3–74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.

Association of cardiac biomarkers with long-term cardiovascular events in a community cohort

Introduction: We hypothesized that combining biomarkers of atherosclerotic cardiovascular disease (ASCVD) would improve prediction of ASCVD events in a primary prevention cohort. Materials and methods The study assessed major adverse cardiac events (MACE) (myocardial infarction, coronary artery bypass graft, percutaneous intervention, stroke, and death. Cox proportional hazards models assessed apolipoprotein AI (ApoA1), apolipoprotein B (ApoB), ceramide score, cystatin C, galectin-3 (Gal3), LDL-C, Non-HDL-C, total cholesterol (TC), N-terminal B-type natriuretic peptide (NT proBNP), high-sensitivity cardiac troponin (HscTnI) and soluble interleukin 1 receptor-like 1. In adjusted models, Ceramide score was defined by from N-palmitoyl-sphingosine [Cer(16:0)], N-stearoyl-sphingosine [Cer(18:0)], N-nervonoyl-sphingosine [Cer(24:1)] and N-lignoceroyl-sphingosine [Cer(24:0)]. Multi-biomarker models were compared with C-statistics and Integrated Discrimination Index (IDI). Results A total of 1131 patients were included. Adjusted NT proBNP per 1 SD resulted in a 31% increased risk of MACE/death (HR = 1.31) and a 31% increased risk for stroke/MI (HR = 1.31). Adjusted Ceramide per 1 SD showed a 13% increased risk of MACE/death (HR = 1.13) and a 29% increased risk for stroke/MI (HR = 1.29). These markers added to clinical factors for both MACE/death (p = 0.003) and stroke/MI (p = 0.034). HscTnI was not a predictor of outcomes when added to the models. Discussion Ceramide score and NT proBNP improve the prediction of MACE and stroke/MI in a community primary prevention cohort.

Diabetic kidney disease as an independent predictor of long-term adverse outcomes in patients with coronary artery disease and diabetic mellitus.

Diabetic kidney disease (DKD) had been proposed as a contributor in the pathogenesis of coronary artery disease (CAD). However, the relationship of DKD and the long-term adverse outcomes in patients with CAD after percutaneous coronary intervention (PCI) was still undiscovered. Approximately 892 patients with CAD enrolled from January 2012 to December 2016. The patients were divided into two groups, the DKD group (n = 341) and the None DKD group (n = 551). The primary outcome was major adverse cardiac events (MACE) after PCI. The average follow-up time was 1,897 ± 1,276 days. Baseline data showed that some factors were significantly different between the two groups, including age, body mass index, gender (female), hypertension, smoking, stroke history, heart failure, duration of diabetic mellitus (DM), low-density lipoprotein cholesterol, urinary protein/creatinine ratio, serum creatinine, hemoglobin, platelet, antiplatelet, beta blocker, statin, antihypertensive drugs, and insulin (all p < 0.005). There were significant differences between the two groups in MACE, 40.3% vs. 52.2% (p = 0.001), and in cardiovascular death events and all-cause death events (5.6% vs. 20.5%, p < 0.001 and 4.4% vs. 13.5%, p < 0.001, respectively). In the DKD group, the risk of MACE was elevated to 141.9% [hazard ratio (HR) = 1.419, 95% confidence interval (CI): 1.164-1.730, p = 0.001] in the Cox univariable regression analyses; after adjusting co-variables, the Cox multivariable regression analyses demonstrated that DKD was an independent predictor for MACE (HR = 1.291, 95% CI: 1.027-1.624, p = 0.029) in patients with CAD after PCI, as well as in cardiovascular death events (HR = 2.148, 95% CI: 1.292-3.572, p = 0.003) and all-cause death events (HR = 2.229, 95% CI: 1.325-3.749, p = 0.003). This study suggests that DKD is an independent and novel predictor of long-term adverse outcomes in patients with CAD and DM who underwent PCI.

Spherization indices measured by resting SPECT improve risk stratification in patients with ischemia with non-obstructive coronary artery disease (INOCA)

BackgroundThe prevalence of ischemia with non-obstructive coronary artery disease (INOCA) is substantial, but its risk stratification has been suboptimal. Resting SPECT myocardial perfusion imaging (MPI) could provide useful heart information including spherical indices. We aimed to evaluate the prognostic value of spherical indices in individuals with INOCA.ResultsDuring a median follow-up of 47.2 ± 20.8 months, 49 (17.2%) patients experienced major adverse cardiac events (MACE). Compared to those without MACE, those with MACE had a higher shape index (SI) (0.60 ± 0.07 vs. 0.58 ± 0.06; P = 0.028) and a lower E2 (eccentricity index calculated by the QPS) (0.81 ± 0.05 vs. 0.83 ± 0.04; P = 0.019). MACE event-free survival analysis revealed significant differences in the SI and E2 among all patients (all log-rank P < 0.01). Multivariate Cox analysis showed abnormal SI (HR: 2.73, 95% CI 1.44–5.18, P = 0.002) and E2 (HR: 1.94, 95% CI 1.08–3.48, P = 0.026) were both independent predictors for MACE when they were put into the same model, respectively. The incorporation of the SI into the baseline model demonstrated a significant improvement in the predictive accuracy for MACEs (P = 0.026), whereas E2 did not exhibit a similar improvement (P > 0.05).ConclusionFor patients with INOCA, spherical indices (especially the SI) were associated with long-term MACE, which could be a preferable indicator for risk stratification and prognostic prediction.

Long-term prognosis and prognostic factors after primary prophylactic implantable cardioverter-defibrillator therapy

BackgroundLittle is known regarding which patients with ischemic cardiomyopathy (ICM) should be considered for prophylactic therapies, such as an implantable cardioverter-defibrillator (ICD), in the primary percutaneous intervention era. The aim of this study was to investigate the influence of non-sustained ventricular tachycardia (NSVT) on major adverse cardiac events (MACE) in heart failure with reduced ejection fraction (HFrEF) patients. MethodsWe retrospectively analyzed patients of ICM and non-ICM who underwent ICD implantation at our institute from October 2006 to August 2020. MACE were defined as composite outcome of cardiovascular death, heart failure hospitalization, and appropriate ICD therapies. ResultsA total of 167 patients were enrolled [male, 138 (83 %); age, 62.1 ± 11.7 years; left ventricular ejection fraction, 23.5 ± 6.1 %; left ventricular diastolic diameter, 67.4 ± 9.0 mm; atrial fibrillation, 47 (28 %); NSVT, 124 (74 %); use of class III antiarrhythmic drugs, 55 (33 %); ischemic cardiomyopathy, 56 (34 %); cardiac resynchronization therapy, 73 (44 %)]. The median follow-up duration was 61 months. MACE occurred with 71 patients (43 %). When comparing baseline characteristics of the patients, left ventricular ejection fraction (p = 0.02) and atrial fibrillation (p = 0.04) were significantly associated with MACE. The multivariable Cox analysis for the target variable MACE identified atrial fibrillation (hazard ratio 2.00; 95 % confidence index 1.18–3.37; p = 0.01) as an independent predictor for MACE. ConclusionsPrior NSVT before ICD implantation was not an independent predictor of future MACE in patients with HFrEF with primary prophylactic ICD. In contrast, atrial fibrillation was associated with worse prognosis. To predict the prognosis of patients with primary prophylactic ICD, these factors should be assessed as comprehensive risk stratification factors for MACE.

Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients with diabetes mellitus

ObjectivePatients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac event (MACE) and limb event (MALE) outcomes among patients with DM would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among patients with DM and to identify those at greatest risk for major adverse outcome events. MethodsConsecutive patients with DM undergoing lower limb arterial testing (April 1, 2015-December 30, 2020) were randomly allocated to training, validation, and testing subsets (60%, 20%, and 20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict all-cause mortality, MACE, and MALE at 5 years using quartiles based on the distribution of the prediction score. ResultsAmong 11,384 total patients, 4211 patients with DM met study criteria (mean age, 68.6 ± 11.9 years; 32.0% female). After allocating the training and validation subsets, the final test subset included 856 patients. During follow-up, there were 262 deaths, 319 MACE, and 99 MALE. Patients in the upper quartile of prediction based on deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 3.58; 95% confidence interval [CI], 2.31-5.56), MACE (HR, 2.06; 95% CI, 1.49-2.91), and MALE (HR, 13.50; 95% CI, 5.83-31.27). ConclusionsAn artificial intelligence enabled analysis of a resting Doppler arterial waveform permits identification of major adverse outcomes including all-cause mortality, MACE, and MALE among patients with DM.

Fractal Analysis of Left Ventricular Trabeculae in Patients with End-Stage Renal Disease: A Random Survival Tree Analysis.

The complexity of left ventricular (LV) trabeculae is related to the prognosis of several cardiovascular diseases. To evaluate the prognostic value of LV trabecular complexity in patients with end-stage renal disease (ESRD). Prospective outcome study. 207 participants on maintenance dialysis, divided into development (160 patients from 2 centers) and external validation (47 patients from a third center) cohorts, and 72 healthy controls. 3.0T, steady-state free precession (SSFP) and modified Look-Locker imaging sequences. All participants had their trabecular complexity quantified by fractal analysis using cine SSFP images. Patients were followed up every 2 weeks until April 2023, or endpoint events happened. Random Forest (RF) and Cox regression models including age, diabetes, LV mass index, mean basal fractal dimension (FD), and left atrial volume index, were developed to predict major adverse cardiac events (MACE). Patients were divided into low- and high-risk groups based on scores derived from the RF model and survival compared. Receiver operating characteristic curve analysis; Kaplan-Meier survival analysis with log rank tests; Harrel's C-index to assess model performance. A P value <0.05 was considered statistically significant. Fifty-five patients (26.57%) experienced MACE during a median follow-up time of 21.83 months. An increased mean basal FD (≥1.324) was associated with a significantly higher risk of MACE. The RF model (C-index: 0.81) had significantly better discrimination than the Cox regression model (C-index: 0.74). Participants of the external validation dataset classified into the high-risk group had a hazard of experiencing MACE increased by 12.29 times compared to those in the low-risk group. LV basal FD was an independent predictor for MACE in patients with ESRD. Reliable risk stratification models could be generated based on LV basal FD and other MRI variables using RF analysis. 2 TECHNICAL EFFICACY: Stage 2.

Clinical characteristics and prognosis of patients with hypertrophic cardiomyopathy and heart failure with preserved ejection fraction.

Whether heart failure with preserved ejection fraction (HFpEF) is associated with an increased risk of developing systolic dysfunction and a poor prognosis in hypertrophic cardiomyopathy (HCM) patients is unknown. We aimed to assess risk factors for the development of end-stage (ES) heart failure (HF) (ejection fraction < 50%) and compare the prognosis of different HF phenotypes. This retrospective study was conducted on patients with HCM in China between January 2009 and February 2023. Patients were stratified into three different groups: HCM-non-HF, HCM-HFpEF and HCM-heart failure with reduced ejection fraction (HCM-HFrEF). The primary outcome was a composite of major adverse cardiac events (MACEs), including all-cause deaths, HF hospitalization, sudden cardiac death and ventricular tachycardia. Of 3,620 HCM patients enrolled, 1,553 (42.9%) had non-HF, 1,666 (46.0%) had HFpEF, and 579 patients (11.1%) had HFrEF at baseline. During the median follow-up period of 4.0 years (IQR 1.4-9.4 years), patients with HCM-HFpEF exhibited a higher incidence of ES-HF than those with HCM-non-HF (12.4% vs. 2.7%, P < 0.001). HFpEF was an independent risk factor for ES-HF development (HR 3.84, 2.54-5.80, P < 0.001). MACEs occurred in 26.9% with a higher incidence in HCM-HFpEF than HCM-non-HF (36.6% vs 12.2%, P < 0.001). HFpEF was an independent predictor of MACEs (HR 2.13, 1.75-2.59, P < 0.001). HFpEF is common in HCM. Compared to non-HF, it increases the risk of LVEF decline and poor prognosis. It may aid in risk stratification and need close echocardiography follow-up.

Optimized gated a SPECT-derived myocardial salvage index: its prognostic significance in predicting major adverse cardiac events following acute myocardial infarction percussion.

Estimate myocardial salvage index (MSI) using a single-gated Single-Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging (GSMPI) early after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) and compare its predictive value with the traditional method especially for post-PCI left ventricular ejection fraction (LVEF) improvement and major adverse cardiac events (MACEs). GSMPI was performed in 62 patients with AMI early after PCI (3-10days). The MSI and the conventional parameters were obtained, including total perfusion deficit, LVEF, peak ejection rate (PER), and peak filling rate (PFR). The new calculation method (scoring evaluation method means the extent of abnormality is the percentage of the total scores of abnormal segments divided by the sum of the maximum scores of all myocardial segments using 4-point and 5-point scale semi-quantitative scoring method) and the reference method (number evaluation method means the extent of abnormality is the percentage of the number of abnormal segments divided by the total number of myocardial segments) were applied to acquire the MSI. We compared the predictive ability of the 2 methods based on the area under the receiver operating characteristic curve for LVEF improvement 6months after PCI using MSI. The Kaplan-Meier method was used for depicting survival curves for predicting MACEs by the 2 methods. Cox proportional-hazards regression was applied to confirm the independent predictors of MACEs. The MSI obtained by the new method indicated stronger prognostic significance in LVEF improvement [area under the curve (AUC): 0.793, 95% confidence interval (CI) 0.620-0.912, P < .001] compared with the reference method (AUC: 0.634, 95%CI 0.452-0.792, P = .187). Delong's test revealed a statistically significant difference in AUCs between the 2 methods (P < .05, 95%CI 0.003-0.316). The diagnostic value of the scoring evaluation method was higher than that of the number evaluation method. The Cox prevalence of MACEs was substantially higher in the < median MSI group than in the ≥ median MSI group (hazard ratio: 0.172; 95% CI 0.041-0.724; P < .05] using the new method, whereas no considerable differences were observed between the 2 groups using the reference method (P = .12). Further, the multivariate Cox regression analysis revealed that MSI was an independent indicator for predicting MACEs (P < .05). The MSI obtained from a simple GSMPI early after PCI, using the scoring evaluation method, was a reliable prognostic indicator for predicting LVEF improvement and MACEs in AMI. It remarkably improved the prognostic value compared with the previous reference methods.

Left Ventricular-Arterial Coupling as an Independent Predictor of Adverse Events in Young Patients with ST Elevation Myocardial Infarction-A 3D Echocardiographic Study.

Left ventricular-arterial coupling (VAC) is a key determinant of global cardiovascular performance, calculated as the ratio between arterial elastance (EA) and left ventricular end-systolic elastance (EES). Over the years, acute myocardial infarction (STEMI) has remained an important cause of morbidity and mortality worldwide. Although, until recently, it was considered a disease occurring mostly in older patients, its prevalence in the young population is continuously rising. In this study, we aimed to investigate the role of 3D VAC and its derived indices in predicting adverse outcomes in young patients with STEMI. We prospectively enrolled 84 young patients (18-51 years) with STEMI who underwent primary PCI and 28 healthy age and sex-matched controls. A 3D echocardiography was used for non-invasive measurements of end-systolic elastance (EES), arterial elastance (EA), and VAC (EA/EES). The occurrence of major adverse cardiac events (MACE) was assessed one year after the index STEMI. Out of 84 patients, 15.4% had adverse events at 12 months follow-up. Patients were divided into two groups according to the presence or absence of MACE. There were no significant differences in arterial elastance between the two groups. EA was higher in the MACE group but without statistical significance (2.65 vs. 2.33; p = 0.09). EES was significantly lower in the MACE group (1.25 ± 0.34 vs. 1.91 ± 0.56. p < 0.0001) and VAC was higher (2.2 ± 0.62 vs. 1.24 ± 0.29, p < 0.0001). ROC analysis showed that VAC has a better predictive value for MACE (AUC 0.927) compared with EA or EEA but also compared with a classical determinant of LV function (LVEF and LVGLS). A VAC value over 1.71 predicts unfavourable outcome with 83.3% sensitivity and 97.1% specificity. In both univariate and multivariate COX regression analysis, VAC remained an independent predictor for MACE and demonstrated incremental prognostic value over LVEF and LVGLS in the proposed statistical models. In conclusion, 3D VAC is an independent predictor of adverse events in young patients with STEMI at a 12 month follow-ups and could be used for a more accurate risk stratification in the acute phase.

Prediction of major adverse cardiac events in the emergency department using an artificial neural network with a systematic grid search

BackgroundThe aim of our research was to design and evaluate an Artificial Neural Network (ANN) model using a systemic grid search for the early prediction of major adverse cardiac events (MACE) among patients presenting to the triage of an emergency department.MethodsThis is a single-center, cross-sectional study using electronic health records from January 2017 to December 2020. The research population consists of adults coming to our emergency department triage at Aga Khan University Hospital. The MACE during hospitalization was the main outcome. To enhance the architecture of an ANN using triage data, we used a systematic grid search strategy. Four hidden ANN layers were used, followed by an output layer. Following each hidden layer was back normalization and a dropout layer. MACE was predicted using three binary classifiers: ANN, Random Forests (RF), and logistic regression (LR). The overall accuracy, sensitivity, specificity, precision, and recall of these models were examined. Each model was evaluated using the receiver operating characteristic curve (ROC) and an F1-score with a 95% confidence interval.ResultsA total of 97,333 emergency department visits were recorded during the study period, with 33% of patients having cardiovascular symptoms. The mean age was 54.08 (19.18) years old. The MACE was observed in 23,052 (23.7%) of the patients, in-hospital (up to 30 days) mortality in 10,888 (11.2%) patients, and cardiac arrest in 5483 (5.6%) patients. The data used for training and validation were 77,866 and 19,467 in an 80:20 ratio, respectively. The AUC score for MACE with ANN was 0.97, which was greater than RF (0.96) and LR (0.96). Similarly, the precision-recall curve for MACE utilizing ANN was greater (0.94 vs. 0.93 for RF and 0.93 for LR). The sensitivity for MACE prediction using ANN, RF, and LR classifiers was 99.3%, 99.4%, and 99.2%, respectively, with the specificities being 94.5%, 94.2%, and 94.2%, respectively.ConclusionWhen triage data is used to predict MACE, death, and cardiac arrest, ANN with systemic grid search gives precise and valid outcomes and will benefit in predicting MACE in emergency rooms with limited resources that have to deal with a substantial number of patients.

Prognostic value of mid-term cardiovascular magnetic resonance follow-up in patients with non-ischemic dilated cardiomyopathy: a prospective cohort study

BackgroundThe prognostic value of follow-up cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) patients is unclear. We aimed to investigate the prognostic value of cardiac function, structure, and tissue characteristics at mid-term CMR follow-up. MethodsThe study population was a prospectively enrolled cohort of DCM patients who underwent guideline-directed medical therapy with baseline and follow-up CMR, which included measurement of biventricular volume and ejection fraction, late gadolinium enhancement, native T1, native T2, and extracellular volume. During follow-up, major adverse cardiac events (MACE) were defined as a composite endpoint of cardiovascular death, heart transplantation, and heart-failure readmission. ResultsAmong 235 DCM patients (median CMR interval: 15.3 months; interquartile range: 12.5–19.2 months), 54 (23.0%) experienced MACE during follow-up (median: 31.2 months; interquartile range: 20.8–50.0 months). In multivariable Cox regression, follow-up CMR models showed significantly superior predictive value than baseline CMR models. Stepwise multivariate Cox regression showed that follow-up left ventricular ejection fraction (LVEF; hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.91–0.96; p < 0.001) and native T1 (HR, 1.01; 95% CI, 1.00–1.01; p = 0.030) were independent predictors of MACE. Follow-up LVEF ≥ 40% or stable LVEF < 40% with T1 ≤ 1273 ms indicated low risk (annual event rate < 4%), while stable LVEF < 40% and T1 > 1273 ms or LVEF < 40% with deterioration indicated high risk (annual event rate > 15%). ConclusionsFollow-up CMR provided better risk stratification than baseline CMR. Improvements in the LVEF and T1 mapping are associated with a lower risk of MACE.