Risk Factors Associated With Major Cardiovascular Events 1 Year After Acute Myocardial Infarction

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

Therapeutic education of patients with coronary heart disease: Impact of digital platform monitoring in preventing major cardiovascular events in Tunisia: Study protocol

BACKGROUND

Faced with the increase in the number of chronic diseases with the aging of the population, and with the observation of the insufficiency of therapeutic control, a new need has emerged, that of having a patient as a partner in care.

METHODS

This study is a randomized controlled trial. Patients with coronary heart disease will be recruited from one clinical site and randomly assigned into two groups: the intervention group and the control group. All participants will be followed up for a total of one year (with three-time points for data collection). Patients who are assigned to the intervention group will receive therapeutic education at first. The digital platform will then allow healthcare providers to accompany them outside the hospital walls. The primary outcome is the incidence of major cardiovascular events within one year of discharge. Main secondary outcomes include changes in health behaviors, medication adherence, and quality of life score. The digital platform is a multi-professional telemonitoring platform that allows care teams to accompany the patient outside the hospital walls. It allows the collection and transmits information from the patient's home to the therapeutic education team. All data will be secured at a certified host. The patient application provides data on compliance, adherence to physical activity (number of steps taken per day), adequate diet (weight gain, food consumed during the meal, compliance with low-salt or salt-free diet, diabetic diet), smoking cessation, as well as medication adherence. Access to educational tools (digital media) is provided to all initial program participants. These tools will be updated annually by the rehabilitation team on the recommendations. The platform also offers the possibility of organizing an individual or group remote educational session (videoconference modules allowing group and individual sessions), a secure integrated caregiver-patient messaging system. The control group will receive the usual controls at the hospital.

DISCUSSION

To offer a complete solution of care to our patients, we have thought of setting up a digital platform that aims to monitor the patient and strengthen their abilities to manage their condition daily. This pilot experience could be generalized to several services and disciplines. It could be used in several research works.

TRIAL REGISTRATION

Trial registered with the Pan African Clinical Trial Registry (PACTR202307694422939). URL: https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=24247.

Genomic risk prediction of cardiovascular diseases among type 2 diabetes patients in the UK Biobank

Background: Polygenic risk score (PRS) has proved useful in predicting the risk of cardiovascular diseases (CVD) based on the genotypes of an individual, but most analyses have focused on disease onset in the general population. The usefulness of PRS to predict CVD risk among type 2 diabetes (T2D) patients remains unclear. Methods: We built a meta-PRSCVD upon the candidate PRSs developed from state-of-the-art PRS methods for three CVD subtypes of significant importance: coronary artery disease (CAD), ischemic stroke (IS), and heart failure (HF). To evaluate the prediction performance of the meta-PRSCVD, we restricted our analysis to 21,092 white British T2D patients in the UK Biobank, among which 4,015 had CVD events. Results: Results showed that the meta-PRSCVD was significantly associated with CVD risk with a hazard ratio per standard deviation increase of 1.28 (95% CI: 1.23-1.33). The meta-PRSCVD alone predicted the CVD incidence with an area under the receiver operating characteristic curve (AUC) of 0.57 (95% CI: 0.54-0.59). When restricted to the early-onset patients (onset age ≤ 55), the AUC was further increased to 0.61 (95% CI 0.56-0.67). Conclusion: Our results highlight the potential role of genomic screening for secondary preventions of CVD among T2D patients, especially among early-onset patients.

Impact of myocardial deformation on risk prediction in patients following acute myocardial infarction

Background

Strain analyses derived from cardiovascular magnetic resonance-feature tracking (CMR-FT) provide incremental prognostic benefit in patients sufferring from acute myocardial infarction (AMI). This study aims to evaluate and revalidate previously reported prognostic implications of comprehensive strain analyses in a large independent cohort of patients with ST-elevation myocardial infarction (STEMI).

Methods

Overall, 566 STEMI patients enrolled in the CONDITIONING-LIPSIA trial including pre- and/or postconditioning treatment in addition to conventional percutaneous coronary intervention underwent CMR imaging in median 3 days after primary percutaneous coronary intervention. CMR-based left atrial (LA) reservoir (Es), conduit (Ee), and boosterpump (Ea) strain analyses, as well as left ventricular (LV) global longitudinal strain (GLS), circumferential strain (GCS), and radial strain (GRS) analyses were carried out. Previously identified cutoff values were revalidated for risk stratification. Major adverse cardiac events (MACE) comprising death, reinfarction, and new congestive heart failure were assessed within 12 months after the occurrence of the index event.

Results

Both atrial and ventricular strain values were significantly reduced in patients with MACE (p < 0.01 for all). Predetermined LA and LV strain cutoffs enabled accurate risk assessment. All LA and LV strain values were associated with MACE on univariable regression modeling (p < 0.001 for all), with LA Es emerging as an independent predictor of MACE on multivariable regression modeling (HR 0.92, p = 0.033). Furthermore, LA Es provided an incremental prognostic value above LVEF (a c-index increase from 0.7 to 0.74, p = 0.03).

Conclusion

External validation of CMR-FT-derived LA and LV strain evaluations confirmed the prognostic value of cardiac deformation assessment in STEMI patients. In the present study, LA strain parameters especially enabled further risk stratification and prognostic assessment over and above clinically established risk parameters.

Clinical Trial Registration

ClinicalTrials.gov, identifier NCT02158468.

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

Elevated random glucose levels at admission are associated with all-cause mortality and cardiogenic shock during hospitalisation in patients with acute myocardial infarction and without diabetes: A retrospective cohort study

BACKGROUND

Elevated glucose levels at admission are associated with a worse prognosis in patients with acute myocardial infarction (AMI); additionally, such elevation has a higher prognostic value for patients without diabetes.

METHODS

We retrospectively recruited 2412 AMI patients without diabetes from 1 August 2011 to 10 January 2022. The primary outcome was all-cause mortality during hospitalisation, and the secondary outcomes were cardiogenic shock, ventricular tachycardia, ventricular fibrillation, atrioventricular block and new stroke.

RESULTS

The mean age of participants was 65 years and 78.6% were male. Of the 2412 patients, all-cause mortality occurred in 236 patients (9.8%) during hospitalisation. In multivariate-adjusted models that corrected for variable weights, the risk of all-cause mortality increased with an increase in random glucose levels at admission; specifically, the risk of all-cause mortality increased per 1 mg/dL (odds ratio [OR] 1.006, 95% confidence interval [CI]: 1.004-1.008), per 9 mg/dL (OR: 1.06, 95% CI: 1.04-1.08), and per 18 mg/dL (OR: 1.12, 95% CI: 1.07-1.16) increases in admission glucose levels. When admission glucose levels were expressed as a categorical variable, increased levels of glucose (relative to the reference glucose value <140 mg/dL) led to an increased risk of all-cause mortality; specifically, the OR of all-cause mortality for 140-200 mg/dL glucose was 1.55 (95% CI: 1.09-2.17) and the OR for glucose >200 mg/dL was 3.08 (95% CI: 2.00-4.62) (P for trend <0.001). The risk of cardiogenic shock also increased with glucose levels with an OR of 1.68 (95% CI: 1.21-2.31) for 140-200 mg/dL glucose and an OR of 3.72 (95% CI: 2.50-5.46) for >200 mg/dL, compared with that of glucose <140 mg/dL. In multivariate-adjusted spline regression models, an increased risk of all-cause mortality was observed in patients with glucose ≥122 mg/dL (OR: 1.81, 95% CI: 1.38-2.38, p < 0.001) compared with the reference cohort. Furthermore, patients with glucose ≥111 mg/dL (OR: 2.36, 95% CI: 1.80-3.12) had a higher risk of cardiogenic shock than patients with glucose <111 mg/dL.

CONCLUSIONS

Patients with AMI and without diabetes who had elevated random glucose levels at admission had a higher risk of all-cause mortality and cardiogenic shock during hospitalisation. In particular, patients with glucose ≥122 mg/dL had an increased risk of all-cause mortality, and those with glucose ≥111 mg/dL had an increased risk of cardiogenic shock.

Long-term outcomes in distinct phenogroups of patients with primary mitral regurgitation undergoing valve surgery

OBJECTIVES

Patients with mitral regurgitation (MR) may be heterogeneous with different risk profiles. We aimed to identify distinct phenogroups of patients with severe primary MR and investigate their long-term prognosis after mitral valve (MV) surgery.

METHODS

The retrospective cohort of patients with severe primary MR undergoing MV surgery (derivation, n=1629; validation, n=692) was analysed. Latent class analysis was used to classify patients into subgroups using 15 variables. The primary outcome was all-cause mortality after MV surgery.

RESULTS

During follow-up (median 6.0 years), 149 patients (9.1%) died in the derivation cohort. In the univariable Cox analysis, age, female, atrial fibrillation, left ventricular (LV) end-systolic dimension/volumes, LV ejection fraction, left atrial dimension and tricuspid regurgitation peak velocity were significant predictors of mortality following MV surgery. Five distinct phenogroups were identified, three younger groups (group 1-3) and two older groups (group 4-5): group 1, least comorbidities; group 2, men with LV enlargement; group 3, predominantly women with rheumatic MR; group 4, low-risk older patients; and group 5, high-risk older patients. Cumulative survival was the lowest in group 5, followed by groups 3 and 4 (5-year survival for groups 1-5: 98.5%, 96.0%, 91.7%, 95.6% and 83.4%; p<0.001). Phenogroups had similar predictive performance compared with the Mitral Regurgitation International Database score in patients with degenerative MR (3-year C-index, 0.763 vs 0.750, p=0.602). These findings were reproduced in the validation cohort.

CONCLUSION

Five phenogroups of patients with severe primary MR with different risk profiles and outcomes were identified. This phenogrouping strategy may improve risk stratification when optimising the timing and type of interventions for severe MR.

Prior Myocardial Infarction and Treatment Effect of Ticagrelor Versus Prasugrel in Patients With Acute Coronary Syndromes - A Post-hoc Analysis of the ISAR-REACT 5 Trial

Background The efficacy and safety of ticagrelor versus prasugrel in patients with acute coronary syndrome and prior myocardial infarction (MI) remain unstudied. We aimed to assess the treatment effect of ticagrelor versus prasugrel according to prior MI status in patients with ACS. Methods and Results Patients with acute coronary syndrome planned for an invasive strategy and randomized to ticagrelor or prasugrel in the ISAR-REACT (Intracoronary Stenting and Antithrombotic Regimen: Rapid Early Action for Coronary Treatment) 5 trial were included. The primary end point was the composite of 1-year all-cause death, MI, or stroke; the secondary safety end point was the composite of 1-year Bleeding Academic Research Consortium type 3 to 5 bleeding. The study included 4015 patients (prior MI=631 patients; no prior MI=3384 patients). As compared with patients without prior MI, the primary end point occurred more frequently in patients with prior MI (12.6% versus 7.2%; hazard ratio [HR], 1.78 [95% CI, 1.38-2.29]); the secondary safety end point appears to differ little between patients with and without prior MI (5.8% versus 5.7%, respectively; HR, 1.02 [95% CI, 0.71-1.45]). With regard to the primary end point, ticagrelor versus prasugrel was associated with an HR of 1.62 (95% CI, 1.03-2.55) in patients with prior MI and an HR of 1.28 (95% CI, 0.99-1.65) in patients without prior MI (P_int=0.37). With regard to the secondary safety end point, ticagrelor versus prasugrel was associated with an HR of 1.28 (95% CI, 0.56-2.91) in patients with prior MI and an HR of 1.13 (95% CI, 0.82-1.55) in patients without prior MI (P_int=0.79). Conclusions Patients with acute coronary syndrome and prior MI are at higher risk for recurrent ischemic but not bleeding events. Prasugrel is superior to ticagrelor in reducing the risk of ischemic events without a tradeoff in bleeding regardless of prior MI status. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01944800.

Epicardial Adipose Tissue as an Independent Cardiometabolic Risk Factor for Coronary Artery Disease

During the last decades, visceral adiposity has been at the forefront of scientific research because of its complex role in the pathogenesis of cardiovascular diseases. Epicardial adipose tissue (EAT) is the visceral lipid compartment between the myocardium and the visceral pericardium. Due to their unobstructed anatomic vicinity, epicardial fat and myocardium are nourished by the same microcirculation. It is widely known that EAT serves as an energy lipid source and thermoregulator for the human heart. In addition to this, epicardial fat exerts highly protective effects since it releases a great variety of anti-inflammatory molecules to the adjacent cardiac muscle. Taking into account the unique properties of human EAT, it is undoubtedly a key factor in cardiac physiology since it facilitates complex heart functions. Under pathological circumstances, however, epicardial fat promotes coronary atherosclerosis in a variety of ways. Therefore, the accurate estimation of epicardial fat thickness and volume could be utilized as an early detecting method and future medication target for coronary artery disease (CAD) elimination. Throughout the years, several therapeutic approaches for dysfunctional human EAT have been proposed. A balanced healthy diet, aerobic and anaerobic physical activity, bariatric surgery, and pharmacological treatment with either traditional or novel antidiabetic and antilipidemic drugs are some of the established medical approaches. In the present article, we review the current knowledge regarding the anatomic and physiological characteristics of epicardial fat. In addition to this, we describe the pathogenic mechanisms which refer to the crosstalk between epicardial fat alteration and coronary arterial atherosclerosis development. Lastly, we present both lifestyle and pharmacological methods as possible treatment options for EAT dysfunction. 

Clinical features and burden of osteoporotic fractures among the elderly in the USA from 2016 to 2018

This study provides a national estimate of the incidence of hospitalizations and assesses the clinical features and outcomes during inpatient admission due to osteoporotic fractures diagnosed by ICD-10-CM/PCS among the elderly in the USA, using the US Nationwide Inpatient Sample, 2016-2018.

PURPOSE

To provide a national estimate of the incidence of hospitalizations and assess the clinical features and outcomes during inpatient admission due to osteoporotic fractures (OFs) among the elderly in the USA.

METHODS

The study included all inpatients aged 65 years and older who participated in the US Nationwide Inpatient Sample (NIS). We conducted a retrospective analysis of hospitalizations with OFs diagnosed by the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS), using the US NIS, 2016-2018. Trends in epidemiological characteristics and outcomes were calculated by annual percentage change (APC).

RESULTS

From 2016 to 2018, there were an estimated 0.16 million hospitalizations for OFs, and the estimated annual incidence rate changed from 995 cases per 1 million persons in 2016 to 1114 cases per 1 million persons in 2018 (APC, 5.8% [95% CI, 0.0 to 12.0]; P > 0.05). Over two-thirds of the patients (68.2%) were age-related osteoporosis with current pathological fracture, and OFs were more likely to occur in vertebra (51.7%) and femur (34.7%). During the hospitalization, the average length of stay (LOS) was 5.83 days, the average cost reached $60,901.04, and the overall mortality was 2.3%. All outcomes including LOS, average cost and mortality did not change significantly in 2016-2018 (all P values for trend were over 0.05).

CONCLUSION

Between 2016 and 2018, the incidence rate of OFs remained relatively stable, but the total number of cases was huge. OFs was predominantly age-related, mostly in vertebrae and femurs, with relatively stable cost and mortality during hospitalization.

Patient- and procedure-related factors in the pathophysiology of perioperative myocardial infarction/injury

BACKGROUND

Perioperative myocardial infarction/injury (PMI) is a frequent, often missed and incompletely understood complication of noncardiac surgery. The aim of this study was to evaluate whether patient- or procedure-related factors are more strongly associated to the development of PMI in patients undergoing repeated noncardiac surgery.

METHODS

In this prospective observational study, patient- and procedure-related factors were evaluated for contribution to PMI using: 1) logistic regression modelling with PMI as primary endpoint, 2) evaluation of concordance of PMI occurrence in the first and the second noncardiac surgery (surgery 1 and 2). and 3) the correlation of the extent of cardiomyocyte injury quantified by high-sensitivity cardiac troponin T between surgery 1 and 2. The secondary endpoint was all-cause mortality associated with PMI reoccurrence in surgery 2.

RESULTS

Among 784 patients undergoing repeated noncardiac surgery (in total 1'923 surgical procedures), 116 patients (14.8%) experienced PMI during surgery 1. Among these, PMI occurred again in surgery 2 in 35/116 (30.2%) patients. However, the vast majority of patients developing PMI during surgery 2 (96/131, 73.3%) had not developed PMI during surgery 1 (phi-coefficient 0.150, p < 0.001). The correlation between the extent of cardiomyocyte injury occurring during surgery 1 and 2 was 0.153. All-cause mortality following a second PMI in surgery 2 was dependent on time since surgery (adjusted hazard ratio 5.6 within 30 days and 2.4 within 360 days).

CONCLUSIONS

In high-risk patients, procedural factors are more strongly associated with occurrence of PMI than patient factors, but patient factors are also contributors to the occurrence of PMI.

Predictors of 1-Year Major Cardiovascular Events after ST-Elevation Myocardial Infarction in a Specialized Cardiovascular Center in Western Iran

Background: Identifying the long-term predictors of recurrent cardiovascular events may help improve the quality of care and prevent subsequent events. We aimed to investigate the predictors of 1-year major cardiovascular events (MACE) in patients discharged after ST-elevation myocardial infarction (STEMI) in a tertiary hospital in Iran. Methods: This registry-based cohort study included consecutive STEMI patients between 2016 and 2019 in Imam-Ali Hospital, Kermanshah, Iran. All patients discharged alive from STEMI hospitalization were followed up for 1 year for MACE, consisting of all-cause mortality, nonfatal MI, and nonfatal stroke. We estimated the hazard ratio (HR) and the 95% confidence interval (95% CI) using Cox proportional-hazard models to evaluate potential predictors, including demographic characteristics, medical history, cardiovascular risk factors, laboratory tests, reperfusion therapy, and medications. Results: During 2187.2 person-years, 21 patients were lost to follow-up (success rate =99.1%). Of 2274 post-discharge STEMI patients (mean age =60.26 y; 21.9% female), 151 (6.6%) experienced MACE, including, all-cause mortality (n=115, 5.1%), nonfatal MI (n=20, 0.9%), and nonfatal stroke (n=16, 0.7%). Independent predictors of MACE were age (HR:1.02; 95% CI: 1.00-1.04), no education vs ≥12 years of formal schooling (HR: 2.07; 95% CI: 1.17-3.67), stroke history (HR: 2.37; 95% CI: 1.48-3.81), the glomerular filtration rate (HR: 0.98; 95% CI: 0.97-1.00), the body mass index (HR: 0.94; 95% CI:, 0.89-0.99), peak creatine kinase-MB (HR: 1.00; 95% CI: 1.00-1.002), thrombolysis vs primary percutaneous coronary intervention (HR: 1.85; 95% CI: 1.21-2.81), and left ventricular ejection fraction <35% vs ≥50% (HR: 2.82; 95% CI: 1.46-5.47). Conclusion: Age, education, stroke history, the glomerular filtration rate, the body mass index, peak creatine kinase-MB, reperfusion therapy, and left ventricular function can be independently associated with 1-year MACE.

Lactobacillus levels and prognosis of patients with acute myocardial infarction

OBJECTIVE

To explore the relationship between Lactobacillus and prognosis of acute myocardial infarction (AMI) patients treated by percutaneous coronary intervention (PCI) and its correlation with clinical parameters.

METHODS

Consecutive patients with AMI in the coronary care unit of Tianjin Chest Hospital in China who received emergency PCI between July 2017 and December 2018 were enrolled. Subjects' fecal 16S rDNA gene sequencing data were analyzed and subjects were categorized into low, medium and high level groups according to stool Lactobacillus measurements. The primary endpoints were major adverse cardiac events. Cox regression analysis was used to analyze the relationship between Lactobacillus and prognosis. Spearman correlation analysis and trend tests were used to assess the relationship between Lactobacillus and the clinical indicators.

RESULTS

The data of 254 patients were included in the analysis. Mean age was 65.90 ± 11.56 years, and 152 patients (59.84%) were male. Follow-up time was 652 (548.25-753.00) days. Multivariate Cox regression analysis showed a significantly lower risk of major adverse cardiac events in patients with Lactobacillus > 7.1 copies/g [adjusted hazard ratio (HR) = 0.216, 95% CI: 0.094-0.493,P < 0.001] compared to patients with Lactobacillus ≤ 3.6 copies/g. Statistically significant differences were shown in ST-segment elevation myocardial infarction (STEMI) (HR = 0.217, 95% CI: 0.085-0.551, P = 0.001). Lactobacillus was a protective factor for male smokers aged over 60 years whose brain natriuretic peptide was over 1,000 pg/mL. Spearman correlation analysis showed that Lactobacillus correlated negatively with white blood cells, neutrophils, high-sensitivity C-reactive protein, TroponinT, creatine kinase, creatine kinase-MB and brain natriuretic peptide (downward trend), and correlated positively with left ventricular ejection fraction (upward trend).

CONCLUSIONS

This study is the first to reveal the correlation between Lactobacillus and inflammation and myocardial damage after STEMI. STEMI patients, especially male smokers aged over 60 years with severe impairment of cardiac function, have better outcomes with high levels of Lactobacillus, suggesting new therapeutic strategies for improving the prognosis and quality of life of AMI patients.

Screening and bioinformatics analysis of key biomarkers in acute myocardial infarction

Acute myocardial infarction (AMI) is the most severe manifestation of coronary artery disease. Considerable efforts have been made to elucidate its etiology and pathology, but the genetic factors that play a decisive role in the occurrence of AMI are still unclear. To determine the molecular mechanism of the occurrence and development of AMI, four microarray datasets, namely, GSE29111, GSE48060, GSE66360, and GSE97320, were downloaded from the Gene Expression Omnibus (GEO) database. We analyzed the four GEO datasets to obtain the differential expression genes (DEGs) of patients with AMI and patients with non-AMI and then performed gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Protein-protein interaction (PPI) network analysis. A total of 41 DEGs were identified, including 39 upregulated genes and 2 downregulated genes. The enriched functions and pathways of the DEGs included the inflammatory response, neutrophil chemotaxis, immune response, extracellular space, positive regulation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) transcription factor activity, response to lipopolysaccharide, receptor for advanced glycation end products (RAGE) receptor binding, innate immune response, defense response to bacterium, and receptor activity. The cytoHubba plug-in in Cytoscape was used to select the most significant hub gene from the PPI network. Ten hub genes were identified, and GO enrichment analysis revealed that these genes were mainly enriched in inflammatory response, neutrophil chemotaxis, immune response, RAGE receptor binding, and extracellular region. In conclusion, this study integrated four datasets and used bioinformatics methods to analyze the gene chips of AMI samples and control samples and identified DEGs that may be involved in the occurrence and development of AMI. The study provides reliable molecular biomarkers for AMI screening, diagnosis, and prognosis.

Development and Validation of a Risk Prediction Model for 1-Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction

Background

Readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (≤55 years). Our aim was to develop/validate a risk prediction model that considered a broad range of factors for readmission within 1 year.

Methods and Results

We used data from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study, which enrolled young adults aged 18 to 55 years hospitalized with AMI across 103 US hospitals (N=2979). The primary outcome was ≥1 all‐cause readmissions within 1 year of hospital discharge. Bayesian model averaging was used to select the risk model. The mean age of participants was 47.1 years, 67.4% were women, and 23.2% were Black. Within 1 year of discharge for AMI, 905 (30.4%) of participants were readmitted and were more likely to be female, Black, and nonmarried. The final risk model consisted of 10 predictors: depressive symptoms (odds ratio [OR], 1.03; 95% CI, 1.01–1.05), better physical health (OR, 0.98; 95% CI, 0.97–0.99), in‐hospital complication of heart failure (OR, 1.44; 95% CI, 0.99–2.08), chronic obstructive pulmomary disease (OR, 1.29; 95% CI, 0.96–1.74), diabetes mellitus (OR, 1.23; 95% CI, 1.00–1.52), female sex (OR, 1.31; 95% CI, 1.05–1.65), low income (OR, 1.13; 95% CI, 0.89–1.42), prior AMI (OR, 1.47; 95% CI, 1.15–1.87), in‐hospital length of stay (OR, 1.13; 95% CI, 1.04–1.23), and being employed (OR, 0.88; 95% CI, 0.69–1.12). The model had excellent calibration and modest discrimination (C statistic=0.67 in development/validation cohorts).

Conclusions

Women and those with a prior AMI, increased depressive symptoms, longer inpatient length of stay and diabetes may be more likely to be readmitted. Notably, several predictors of readmission were psychosocial characteristics rather than markers of AMI severity. This finding may inform the development of interventions to reduce readmissions in young patients with AMI.

Gene regulatory network analysis identifies key genes and regulatory mechanisms involved in acute myocardial infarction using bulk and single cell RNA-seq data

Cardiovascular and cerebrovascular diseases are leading causes of death worldwide, accounting for more than 40% of all deaths in China. Acute myocardial infarction (AMI) is a common cardiovascular disease and traditionally divided into ST-segment (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), which are known with different prognoses and treatment strategies. However, key regulatory genes and pathways involved in AMI that may be used as potential biomarker for prognosis are unknown. In this study, we employed both bulk and single-cell RNA-seq to construct gene regulatory networks and cell-cell communication networks. We first constructed weighted gene co-expression networks for differential expressed genes between STEMI and NSTEMI patients based on whole-blood RNA-seq transcriptomics. Network topological attributes (e.g., node degree, betweenness) were analyzed to identify key genes involved in different functional network modules. Furthermore, we used single-cell RNA-seq data to construct multilayer signaling network to infer regulatory mechanisms of the above key genes. PLAUR (receptor for urokinase plasminogen activator) was found to play a vital role in transducing inter-cellular signals from endothelial cells and fibroblast cells to intra-cellular pathways of myocardial cells, leading to gene expression involved in cellular response to hypoxia. Our study sheds lights on identifying molecular biomarkers for diagnosis and prognosis of AMI, and provides candidate key regulatory genes for further experimental validation.

Cardiovascular Risk in Users of Mirabegron Compared with Users of Antimuscarinic Treatments for Overactive Bladder: Findings from a Non-Interventional, Multinational, Cohort Study

INTRODUCTION

During clinical trials, mirabegron, a β3-adrenoreceptor agonist, was associated with increased vital signs vs placebo in patients with overactive bladder.

OBJECTIVE

The purpose of this study was to compare incidence rates of adverse cardiovascular (CV) outcomes following mirabegron or antimuscarinic use.

METHODS

We conducted an observational post-marketing safety study utilising real-world data. The study population was identified within five sources: Danish and Swedish National Registers, Clinical Practice Research Datalink (UK), Optum (USA) and Humana (USA). Episodes of time when patients were new users of mirabegron or antimuscarinics (October 2012-December 2018) were sourced from prescriptions and matched on propensity scores. Occurrences of major adverse cardiovascular events (MACE), acute myocardial infarction (AMI), stroke, CV mortality and all-cause mortality were identified. Outcome incidence rates and hazard ratios from Cox models were estimated.

RESULTS

Overall, 152,026 mirabegron and 152,026 antimuscarinic episodes were matched. The population consisted of 63.1% women and 72.6% were ≥ 65 years old. There were no appreciable differences in the incidence rates of MACE, AMI or stroke between users of mirabegron and antimuscarinics. Incidence rates of CV mortality (hazard ratio 0.83, 95% confidence interval 0.73-0.95) and all-cause mortality (hazard ratio 0.80, 95% confidence interval 0.76-0.84) were no higher with mirabegron vs antimuscarinics. Results restricted to episodes at high risk for CV events or stratified by age (< 65 years, ≥ 65 years) or prior overactive bladder medication use were consistent with overall findings.

CONCLUSIONS

This large, multinational study found no higher risk of MACE, AMI, stroke, CV mortality or all-cause mortality among users of mirabegron relative to users of antimuscarinics.

Plaque Vulnerability Index Predicts Cardiovascular Events: A Histological Study of an Endarterectomy Cohort

Background

The balance between stabilizing and destabilizing atherosclerotic plaque components is used in experimental studies and in imaging studies to identify rupture prone plaques. However, we lack the evidence that this balance predicts future cardiovascular events. Here we explore whether a calculated histological ratio, referred to as vulnerability index (VI), can predict patients at higher risk to suffer from future cardiovascular events.

Methods and Results

Carotid plaques and clinical information from 194 patients were studied. Tissue sections were used for histological analysis to calculate the VI (CD68 [cluster of differentiation 68], alpha‐actin, Oil red O, Movat pentachrome, and glycophorin A). Postoperative cardiovascular events were identified through the Swedish National Inpatient Health Register (2005–2013). During the follow‐up (60 months) 45 postoperative cardiovascular events were registered. Patients with a plaque VI in the fourth quartile compared with the first to third quartiles had significantly higher risk to suffer from a future cardiovascular event (P=0.0002). The VI was an independent predictor and none of the 5 histological variables analyzed separately predicted events. In the 13 patients who underwent bilateral carotid endarterectomy, the VI of the right plaque correlated with the VI of the left plaque and vice versa (r=0.7, P=0.01).

Conclusions

Our findings demonstrate that subjects with a high plaque VI have an increased risk of future cardiovascular events, independently of symptoms and other known cardiovascular risk factors . This strongly supports that techniques which image such plaques can facilitate risk stratification for subjects in need of more intense treatment.

Individualized mobile health interventions for cardiovascular event prevention in patients with coronary heart disease: study protocol for the iCARE randomized controlled trial

Background

Mobile health-based individualized interventions have shown potential effects in managing cardiovascular risk factors. This study aims to assess whether or not mHealth based individualized interventions delivered by an Individualized Cardiovascular Application system for Risk Elimination (iCARE) could reduce the incidence of major cardiovascular events in individuals with coronary heart disease.

Methods

This study is a large-scale, multi-center, parallel-group, open-label, randomized controlled clinical trial. This study will be conducted from September 2019 to December 2025. A total of 2820 patients with coronary heart disease will be recruited from two clinical sites and equally randomized into three groups: the intervention group and two control groups. All participants will be informed of six-time points (at 1, 3, 6, 12, 24, and 36 months after discharge) for follow-up visits. Over a course of 36 months, patients who are randomized to the intervention arm will receive individualized interventions delivered by a fully functional iCARE that using various visualization methods such as comics, videos, pictures, text to provide individualized interventions in addition to standard care. Patients randomized to control group 1 will receive interventions delivered by a modified iCARE that only presented in text in addition to routine care. Control group 2 will only receive routine care. The primary outcome is the incidence of major cardiovascular events within 3 years of discharge. Main secondary outcomes include changes in health behaviors, medication adherence, and cardiovascular health score.

Discussion

If the iCARE trial indeed demonstrates positive effects on patients with coronary heart disease, it will provide empirical evidence for supporting secondary preventive care in this population. Results will inform the design of future research focused on mHealth-based, theory-driven, intelligent, and individualized interventions for cardiovascular risk management.

Trial registration

Trial registered 24th December 2016 with the Chinese Clinical Trial Registry (ChiCTR-INR-16010242). URL: http://www.chictr.org.cn/showproj.aspx?proj=17398.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02153-9.

Development and validation of a risk prediction model for in-hospital major cardiovascular events in patients hospitalised for acute myocardial infarction

OBJECTIVES

Patients admitted to hospital with acute myocardial infarction (AMI) have considerable variability in in-hospital risks, resulting in higher demands on healthcare resources. Simple risk-assessment tools are important for the identification of patients with higher risk to inform clinical decisions. However, few risk assessment tools have been built that are suitable for populations with AMI in China. We aim to develop and validate a risk prediction model, and further build a risk scoring system.

DESIGN

Data from a nationally representative retrospective study was used to develop the model. Patients from a prospective study and another nationally representative retrospective study were both used for external validation.

SETTING

161 nationally representative hospitals, and 53 and 157 other hospitals were involved in the above three studies, respectively.

PARTICIPANTS

8010 patients hospitalised for AMI were included as development sample, and 4485 and 11 223 other patients were included as validation samples in their corresponding studies.

PRIMARY AND SECONDARY OUTCOME MEASURES

The in-hospital major adverse cardiovascular events (MACE) was defined as death from any cause, recurrent AMI, or ischaemic stroke.

RESULTS

The proportion of in-hospital MACE was 11.7%, 8.8% and 11.4% among the development sample and two external-validation samples, respectively. Nine predictors (ie, age, sex, left ventricular ejection fraction, Killip class, systolic blood pressure, creatinine, white blood cell count, heart rate and blood glucose) were independently associated with in-hospital MACE. The model performed well on both discrimination and calibration capability, with areas under the Receiver Operating Characteristic Curve (ROC) curve of 0.85, 0.74 and 0.80, and calibration slopes of 0.98, 0.84 and 0.97 in the development sample and two external validation samples, respectively. A point-based risk scoring system was built with good discrimination and reclassification ability.

CONCLUSIONS

A prediction model using readily available clinical parameters was developed and externally validated to estimate risks of in-hospital MACE among patients with AMI, thereby better informing decision-making in improving clinical care.

External validation of the GRACE risk score 2.0 in the contemporary all-comers GLOBAL LEADERS trial

OBJECTIVES

This study aimed to assess the predictive ability of the Global Registry of Acute Coronary Events (GRACE) risk score 2.0 in contemporary acute coronary syndrome (ACS) patients, and its relation to antiplatelet strategies.

BACKGROUND

The predictive value of the GRACE risk score in the contemporary ACS cohort and the appropriate antiplatelet regimen according to the risk remain unclear.

METHODS

This is a subgroup analysis of the all-comers, randomized GLOBAL LEADERS trial, comparing ticagrelor monotherapy versus conventional dual-antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI). The GRACE risk score 2.0 with 1-year mortality prediction was implemented. The randomized antiplatelet effect was assessed in predefined three GRACE risk-groups; low-risk (GRACE <109), moderate-risk (GRACE 109-140), and high-risk (GRACE >140).

RESULTS

The GRACE risk score was available in 6,594 out of 7,487 ACS patients among whom 1,743, 2,823, and 2,028 patients were classified as low-risk, moderate-risk, and high-risk, respectively. At 1 year, all-cause mortality occurred in 120 patients (1.8%). The discrimination ability of the GRACE model was moderate (C-statistic = 0.742), whereas 1-year mortality risk was overestimated (mean predicted mortality rate: 3.9%; the Hosmer-Lemeshow chi-square: 21.47; p = 0.006). There were no significant interactions between the GRACE risk strata and effects of the ticagrelor monotherapy on ischemic or bleeding outcomes at 1 year compared to the reference strategy.

CONCLUSION

The GRACE risk score 2.0 is valuable in discriminating high risk ACS patients, however, the recalibration of the score is recommended for better risk stratification. There is no significant differences in efficacy and safety of ticagrelor monotherapy across the three GRACE risk strata.

Validating World Health Organization cardiovascular disease risk charts and optimizing risk assessment in China

Background

World Health Organization (WHO) released region-specific cardiovascular disease (CVD) risk prediction charts recently, but the extent to which the charts can apply to Chinese population is unknown. We aimed to validate the WHO CVD risk charts for East Asia, and evaluate their practicability combining with China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) equations among Chinese adults.

Methods

The China-PAR cohort with 93,234 participants aged 40–80 years was followed up during 1992–2015, including 29,337 participants from three sub-cohorts with follow-up period of over 10 years. We validated the WHO CVD risk charts using the China-PAR cohort by assessment of the predicted number of events, C index, calibration χ², and calibration plots, further elaborated the concordance between the China-PAR equations and the WHO risk charts.

Findings

During an average follow-up of 13•64 years, 1849 incident CVD cases were identified from 29,337 participants. Both the laboratory-based and non-laboratory-based charts overestimated CVD events by 59% and 58% in men, and by 72% and 85% in women, respectively. However, 92% of participants identified as high risk by the China-PAR equations could be successfully detected by the laboratory-based charts at the cut-off point of 10%. We also observed that the non-laboratory-based charts demonstrated the poor performance for diabetic population, with high proportion of high-risk individuals (17% for men, 31% for women) would be missed.

Interpretation

Although the WHO CVD risk charts for East Asia apparently overestimated CVD risk among Chinese population, they could be pragmatic pre-selection tools, as potential supplement to the China-PAR equations. The widespread use of the WHO risk charts along with the China-PAR equations might facilitate the implementation of the risk-based CVD prevention in China.

Funding

Full funding sources are listed at the end of the paper (see Acknowledgments).

A novel simple risk model to predict the prognosis of patients with paraquat poisoning

To identify risk factors and develop a simple model to predict early prognosis of acute paraquat (PQ) poisoning patients, we performed a retrospective cohort study of acute PQ poisoning patients (n = 1199). Patients (n = 913) with PQ poisoning from 2011 to 2018 were randomly divided into training (n = 609) and test (n = 304) samples. Another two independent cohorts were used as validation samples for a different time (n = 207) and site (n = 79). Risk factors were identified using a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated using a latent class analysis. The prediction score was developed based on the training sample and was evaluated using the testing and validation samples. Eight factors, including age, ingestion volume, creatine kinase-MB [CK-MB], platelet [PLT], white blood cell [WBC], neutrophil counts [N], gamma-glutamyl transferase [GGT], and serum creatinine [Cr] were identified as independent risk indicators of in-hospital death events. The risk model had C statistics of 0.895 (95% CI 0.855-0.928), 0.891 (95% CI 0.848-0.932), and 0.829 (95% CI 0.455-1.000), and predictive ranges of 4.6-98.2%, 2.3-94.9%, and 0-12.5% for the test, validation_time, and validation_site samples, respectively. In the training sample, the risk model classified 18.4%, 59.9%, and 21.7% of patients into the high-, average-, and low-risk groups, with corresponding probabilities of 0.985, 0.365, and 0.03 for in-hospital death events. We developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This risk scoring system could be helpful for identifying high-risk patients and reducing mortality due to PQ poisoning.

Complementary Therapies Used by Indonesians With Myocardial Infarction

This descriptive, cross-sectional study was conducted to identify types, frequency, methods, duration, and purpose of complementary therapies used by Indonesians with myocardial infarction. The majority of the respondents used biologically based therapies, with the most common subtype being herbs. The purpose of using biologically based therapies was for health promotion.

Detection of Left Ventricular Thrombi on Cardiac Magnetic Resonance Viability Studies

Objective

To highlight detection of left ventricular thrombi on cardiac magnetic resonance (CMR) viability studies.

Method

This retrospective observational study was conducted in the Radiology Department at our Hospital in Dhahran, from April 2015-2019. All recently re-perfused (post-percutaneous coronary intervention/PCI) patients with ST-segment elevation myocardial infarctions (STEMI), having low ejection fractions (<40%), impaired LV functions or abnormal wall motions on transthoracic echocardiographies (TTEs), who underwent cardiac magnetic resonance (CMR) imaging viability studies were included. Patients with incomplete or limited studies (due to artifacts), previous coronary artery bypass graft (CABG), those who lost follow-ups, and those who were contraindicated or unfit for MRIs were excluded. An area of low signal intensity with no late gadolinium enhancement (LGE) was defined as thrombus on MR imaging, and two radiologists reached consensus report for the diagnoses. Patients with anterior or non-anterior wall MI were documented, and their ejection fractions were recorded. Percentage estimation of LV thrombi as detected on CMR studies was made. Any complications (like MI, stroke or death) that occurred within one year of diagnoses were documented. A Chi-square was used to determine association.

Results

Of the 125 patients, most were men (71.2%) with a mean age of 56.78 years. Eleven patients had left ventricular thrombi (8.8%), and most of these were anterior wall infarctions with low ejection fractions (<40%). Three out of 11 patients with LV thrombi developed complications versus 3 out of 114 without LV thrombi (P- value, .0005).

Conclusion

Left ventricular thrombi can be detected on cardiac viability studies in recently re-perfused STEMI patients and may possibly predict the risk of complications.

Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients

Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer treatment. Early detection and prevention of cancer therapy-related cardiac dysfunction (CTRCD) play important roles in precision cardio-oncology. Methods and Results This retrospective study included 4309 cancer patients between 1997 and 2018 whose laboratory tests and cardiovascular echocardiographic variables were collected from the Cleveland Clinic institutional electronic medical record database (Epic Systems). Among these patients, 1560 (36%) were diagnosed with at least 1 type of CTRCD, and 838 (19%) developed CTRCD after cancer therapy (de novo). We posited that machine learning algorithms can be implemented to predict CTRCDs in cancer patients according to clinically relevant variables. Classification models were trained and evaluated for 6 types of cardiovascular outcomes, including coronary artery disease (area under the receiver operating characteristic curve [AUROC], 0.821; 95% CI, 0.815-0.826), atrial fibrillation (AUROC, 0.787; 95% CI, 0.782-0.792), heart failure (AUROC, 0.882; 95% CI, 0.878-0.887), stroke (AUROC, 0.660; 95% CI, 0.650-0.670), myocardial infarction (AUROC, 0.807; 95% CI, 0.799-0.816), and de novo CTRCD (AUROC, 0.802; 95% CI, 0.797-0.807). Model generalizability was further confirmed using time-split data. Model inspection revealed several clinically relevant variables significantly associated with CTRCDs, including age, hypertension, glucose levels, left ventricular ejection fraction, creatinine, and aspartate aminotransferase levels. Conclusions This study suggests that machine learning approaches offer powerful tools for cardiac risk stratification in oncology patients by utilizing large-scale, longitudinal patient data from healthcare systems.

Long-Term Outcomes of Acute Myocardial Infarction With Concomitant Cardiogenic Shock and Cardiac Arrest

This study sought to evaluate long-term mortality and major adverse cardiac and cerebrovascular events (MACCE) in patients with cardiac arrest (CA) and cardiogenic shock (CS) complicating acute myocardial infarction (AMI). This was a retrospective cohort study using an administrative claims database. AMI patients from January 1, 2010 to May 31, 2018 were stratified into CA + CS, CA only, CS only, and AMI alone cohorts. Outcomes of interest were long-term mortality and MACCE (death, AMI, cerebrovascular accident, unplanned revascularization) in AMI survivors. A total 163,071 AMI patients were included with CA + CS, CA only, and CS only in 2.4%, 5.0%, and 4.0%, respectively. The CA + CS cohort had higher rates of multiorgan failure, mechanical circulatory support use and less frequent coronary angiography use. In-hospital mortality was noted in 10,686 (6.6%) patients - CA + CS (48.8%), CA only (35.9%), CS only (24.1%), and AMI alone (2.9%; p < 0.001). Over 23.5 ± 21.7 months follow-up after hospital discharge, patients with CA + CS (hazard ratio [HR] 1.36 [95% confidence interval {CI} 1.19 to 1.55]), CA only (HR 1.16 [95% CI 1.08 to 1.25]), CS only (HR 1.39 [95% CI 1.29 to 1.50]) had higher all-cause mortality compared with AMI alone (all p < 0.001). Presence of CS, either alone (HR 1.22 [95% CI 1.16 to 1.29]; p < 0.001) or with CA (HR 1.18 [95% CI 1.07 to 1.29]; p < 0.001), was associated with higher MACCE compared with AMI alone. In conclusion, CA + CS, CA, and CS were associated with worse long-term survival. CA and CS continue to influence outcomes beyond the index hospitalization in AMI survivors.

Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data

Background

With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data.

Methods

Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyper-parameters were identified using 10-fold cross-validation.

Results

Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest.

Conclusions

Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI.

The Impact of the Associated Pathology in Acute Coronary Events

Acute coronary events (ACE) are one of the main concerns for both clinical medicine and prophylaxis. The study aims to follow the frequency of the pathology associated with ACE and to establish its association with the occurrence of ACE. The study included 865 adult participants between the ages of 19-86. Subjects completed a complex questionnaire that included questions about health status. The study was conducted by applying the subjects to an anonymous questionnaire, in three family medicine practices between November 2018 to May 2019 and targeted healthy people. The frequencies of the following types of associated pathologies were evaluated: high blood pressure (HBP), hypercholesterolemia, stroke, diabetes, depression, stress. In hypertensive patients the prevalence of ACE was 6,99% (N=11) and in those not diagnosed with HBP of only 0,29% (N=2). The risk of ACE was 20 times higher than in those without HBP (RR=20,93; p<0.001). The prevalence of ACE was high among subjects with high cholesterol levels (21,43%) compared with those with normal values (3,03%; N=22), the risk of ACE being 7 times higher (RR=7,06; p<0.001). The prevalence of diabetes was more than four times higher in subjects with ACE (17,3%; N=9) compared with those without ACE (3,9%; N=32). Among those affected by diabetes, the prevalence of ACE was 21,95% (9/41), and risk of ACE in people with diabetes was four times higher (RR=4,21; p<0.001). Although cardiovascular disease is the most common pathology in the contemporary world, a number of comorbidities arise as ACE generators (hypertension, hypercholesterolemia, diabetes), along with psycho-emotional disorders such as depression, anxiety or stress, which outline, ensures, contributes or accelerates the progression to ACE.

A Practical Risk Score to Predict 24-Month Post-Discharge Mortality Risk in Patients With Non-ST-Segment Elevation Myocardial Infarction

BACKGROUND

Risk stratification of patients with non-ST-segment elevation myocardial infarction (NSTEMI) is important in terms of treatment strategy selection. Current efforts have focused on short-term risk prediction after discharge, but we aimed to establish a risk score to predict the 24-month mortality risk in survivors of NSTEMI.Methods and Results:A total of 5,509 patients diagnosed with NSTEMI between January 2013 and September 2014 were included. Primary endpoint was all-cause death at 24 months. A multivariable Cox regression model was used to establish a practical risk score based on independent risk factors of death. The risk score included 9 variables: age, body mass index, left ventricular ejection fraction, reperfusion therapy during hospitalization, Killip classification, prescription of diuretics at discharge, heart rate, and hemoglobin and creatinine levels. The C-statistics for the risk model were 0.83 (95% confidence interval [CI]: 0.81-0.85) and 0.83 (95% CI: 0.79-0.86) in the development and validation cohorts, respectively. Mortality risk increased significantly across groups: 1.34% in the low-risk group (score: 0-58), 5.40% in intermediate group (score: 59-93), and 23.87% in high-risk group (score: ≥94).

CONCLUSIONS

The current study established and validated a practical risk score based on 9 variables to predict 24-month mortality risk in patients who survive NSTEMI. This score could help identify patients who are at high risk for future adverse events who may benefit from good adherence to guideline-recommended secondary prevention treatment.

Incidence, predictors, and prognostic impact of recurrent acute myocardial infarction in China

BACKGROUND

Incidence, predictors, and prognostic impact of recurrent acute myocardial infarction (AMI) after initial AMI remain poorly understood. Data on recurrent AMI in China is unknown.

METHODS

Using the China Patient-centred Evaluative Assessment of Cardiac Events (PEACE)-Prospective AMI Study, we studied 3387 patients admitted to 53 hospitals for AMI and discharged alive. The association of recurrent AMI with 1-year mortality was evaluated using time-dependent Cox regression. Recurrent AMI events were classified as early (1-30 days), late (31-180 days), and very late (181-365 days). Their impacts on 1-year mortality were estimated by Kaplan-Meier methodology and compared by the log-rank test. Multivariable modelling was used to identify factors associated with recurrent AMI.

RESULTS

The mean (SD) age was 60.7 (11.9) years and 783 (23.1%) were women. The observed 1-year recurrent AMI rate was 2.5% (95% CI 2.00 to 3.07) with 35.7% events occurring within the first 30 days. Recurrent AMI was associated with 1-year mortality with an adjusted HR of 25.42 (95% CI 15.27 to 42.34). Early recurrent AMI was associated with the highest 1-year mortality rate of 53.3% (log-rank p<0.001). Predictors of recurrent AMI included age 75-84, in-hospital percutaneous coronary intervention, heart rate >90 min/beats at initial admission, renal dysfunction, and not being prescribed any of guideline-based medications at discharge.

CONCLUSIONS

One-third of recurrent AMI events occurred early. Recurrent AMI is strongly associated with 1-year mortality, particularly if early. Heightened surveillance during this early period and improving prescription of recommended discharge medications may reduce recurrent AMI in China.

Associations between Subsequent Hospitalizations and Primary Ambulatory Services Utilization within the First Year after Acute Myocardial Infarction and Long-Term Mortality

Healthcare resource utilization peaks throughout the first year following acute myocardial infarction (AMI). Data linking the former and outcomes are sparse. We evaluated the associations between subsequent length of in-hospital stay (SLOS) and primary ambulatory visits (PAV) within the first year after AMI and long-term mortality. This retrospective analysis included patients who were discharged following an AMI. Study groups: low (0-1 days), intermediate (2-7) and high (≥8 days) SLOS; low (<10) and high (≥10 visits) PAV, throughout the first post-AMI year. All-cause mortality was set as the primary outcome. Overall, 8112 patients were included: 55.2%, 23.4% and 21.4% in low, intermediate and high SLOS groups respectively; 26.0% and 74.0% in low and high-PAV groups. Throughout the follow-up period (up to 18 years), 49.6% patients died. Multivariable analysis showed that an increased SLOS (Hazard ratio (HR) = 1.313 and HR = 1.714 for intermediate and high vs. low groups respectively) and a reduced number of PAV (HR = 1.24 for low vs. high groups) were independently associated with an increased risk for mortality (p < 0.001 for each). Long-term mortality following AMI is associated with high hospital and low primary ambulatory services utilization throughout the first-year post-discharge. Measures focusing on patients with increased SLOS and reduced PAV should be considered to improve patient outcomes.

Predictors of all-cause 1-year mortality in myocardial infarction patients

Compared with the general population, myocardial infarction (MI) survivors have a higher risk of mortality in the first year after the index event.The aim of this study was to determine the associations between variables obtained during the index admission and 1-year all-cause mortality on follow-up.A cohort of 296 patients was enrolled in the study, with a median age of 63.8 ± 12.68 years. All patients received a coronary angiography and stent implantation by percutaneous coronary intervention. Each variable was tested for association with all-cause mortality, using chi-square tests for categorical and binary variables and t tests for continuous variables. The relative prognostic power of each significant variable was further evaluated by logistic regression before and after adjustment for differences in baseline characteristics.Patients who were deceased after 1-year of MI had significantly higher mean age, increased prevalence of diabetes, and elevated heart rate as compared to those who were surviving. Univariate analysis indicated that patient mortality within 1-year of MI was strongly correlated with higher rates of pump failure on admission (P < .0001), bleeding complications (P = .02), the severity of coronary artery disease measured by Gensini score (P = .04), and decreased left ventricular ejection fraction (LVEF) (P < .0001). After adjustment of baseline variables, only pump failure (P = .006) and reduced LVEF (P < .0001) were independently associated with 1-year mortality.Our study shows that LVEF dysfunction and pump failure are independent predictors of 1-year all-cause post-MI mortality, while the severity of coronary artery disease and bleeding did not qualify as independent predictors. Also, age, history of diabetes, and elevated heart rate may be used as markers for increased mortality rates.

Association Between Subsequent Hospitalizations and Recurrent Acute Myocardial Infarction Within 1 Year After Acute Myocardial Infarction

Background

Patients who survive acute myocardial infarction (AMI) are at high risk for recurrence. We determined whether rehospitalizations after AMI further increased risk of recurrent AMI.

Methods and Results

The study included Medicare fee‐for‐service patients aged ≥65 years discharged alive after AMI from acute‐care hospitals in fiscal years 2009–2014. The outcome was recurrent AMI within 1 year of the index AMI. The Clinical Classifications Software (CCS) was used to classify rehospitalizations into disease categories. A Cox regression model was fit accounting for CCS‐specific hospitalizations as time‐varying variables and patient characteristics at discharge for the index AMI, adjusting for the competing risk of death. The rate of 1‐year recurrent AMI was 5.3% (95% CI, 5.27%–5.41%), and median (interquartile range) time from discharge to recurrent AMI was 115 (34–230) days. Eleven disease categories (diabetes mellitus, anemia, hypertension, coronary atherosclerosis, chest pain, heart failure, pneumonia, chronic obstructive pulmonary disease, gastrointestinal hemorrhage, renal failure, complication of implant or graft) were associated with increased risk of recurrent AMI. Septicemia was associated with lower recurrence risk. Hazard ratios ranged from 1.6 (95% CI, 1.55–1.70, heart failure) to 1.1 (95% CI, 1.04–1.25, pneumonia) to 0.6 (95% CI, 0.58–0.71, septicemia).

Conclusions

Patient risk of recurrent AMI changed based on the occurrence of hospitalizations after the index AMI. Improving post–acute care to prevent unplanned rehospitalizations, especially rehospitalizations for chronic diseases, and extending the focus of outcomes measures to condition‐specific rehospitalizations within 30 days and beyond is important for the secondary prevention of AMI.

Development and validation of a simple risk model to predict major cancers for patients with nonalcoholic fatty liver disease

Objective

To recognize risk factors and build up and validate a simple risk model predicting 8‐year cancer events after nonalcoholic fatty liver disease (NAFLD).

Methods

This was a retrospective cohort study. Patients with NAFLD (n = 5561) were randomly divided into groups: training (n = 1254), test (n = 627), evaluation (n = 627), and validation (n = 3053). Risk factors were recognized by statistical method named as a Cox model with Markov chain Monte Carlo (MCMC) simulation. This prediction score was established based on the training group and was further validated based on the testing and evaluation group from January 1, 2007 to December 31, 2009 and another 3053 independent cases from January 1, 2010 to February 13, 2014.

Results

The main outcomes were NAFLD‐related cancer events, including those of the liver, breast, esophagus, stomach, pancreas, prostate and colon, within 8 years after hospitalization for NAFLD diagnosis. Seven risk factors (age (every 5 years),LDL, smoking, BMI, diabetes, OSAS, and aspartate aminotransferase (every 5 units)) were identified as independent indicators of cancer events. This risk model contained a predictive range of 0.4%‐37.7%, 0.3%‐39.6%, and 0.4%‐39.3% in the training, test, evaluation group, respectively, with a range 0.4%‐30.4% for validation groups. In the training group, 12.6%, 76.9%, and 10.5% of patients, which corresponded to the low ‐, moderate ‐, and high‐risk groups, had probabilities of, <0.01, <0.1, and 0.23 for 8‐year events.

Conclusions

Seven risk factors were recognized and a simple risk model were developed and validated to predict the risk of cancer events after NAFLD based on 8 years. This simple risk score system may recognize high‐risk patients and reduce cancer incidence.

Impact of new-onset diabetes on clinical outcomes after ST segment-elevated myocardial infarction

Objective. Patients with diabetes have higher mortality rate than patients without diabetes after ST-segment elevated myocardial infarction (STEMI). Prognosis of patients with new onset diabetes (NOD) after STEMI remains unclear. The aim of this study was to evaluate the prognosis of patients with NOD compared to that of patients without NOD after STEMI. Design. This study was a retrospective observational study. We enrolled 901 STEMI patients. Patients were divided into diabetic and non-diabetic groups at index admission. Non-diabetic group was divided into NOD and non-NOD groups. Kaplan-Meier analysis and Cox's proportional hazard regression models were used to compare major adverse cardiac events (MACE) free survival rate and hazard ratio for MACE between NOD and non-NOD groups. Results. Mean follow-up period was 59 ± 28 months. Diabetes group had higher MACE than non-diabetes group (p = .038). However, MACE was not different between NOD and non-NOD groups (p = 1.000). After 1:2 propensity score matching, incidence of MACE was not different between the two groups. In Kaplan-Meier survival curves, MACE-free survival rates were not statistically different between NOD and non-NOD groups either (p = .244). Adjusted hazard ratios of NOD for MACE, all-cause of death, recurrent myocardial infarction, and target vessel revascularization were 0.697 (95% confidence interval [CI]: 0.362-1.345, p = .282), 0.625 (95% CI: 0.179-2.183, p = .461), 0.794 (95% CI: 0.223-2.835, p = .723), and 0.506 (95% CI: 0.196-1.303, p = .158), respectively. Conclusion. This retrospective observational study with a limited statistical power did not show a different prognosis in patients with and without NOD.

Association Between Medication Adherence and 1-Year Major Cardiovascular Adverse Events After Acute Myocardial Infarction in China

Background Secondary prevention after acute myocardial infarction ( AMI ) requires long-term guideline-directed medical therapy. However, the level of medication adherence, factors associated with poor adherence, and extent to which good adherence can reduce adverse events after AMI in China remain uncertain. Methods and Results In 2013 to 2014, 4001 AMI patients aged ≥18 years were discharged alive from 53 hospitals across China (mean age 60.5±11.7 years; 22.7% female). Good adherence was defined as taking medications (aspirin, β-blockers, statins, clopidogrel, or angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers) ≥90% of the time as prescribed. Cox models assessed the association between good adherence (a time-varying covariate) and 1-year cardiovascular events after AMI . The most common medications were aspirin (82.2%) and statins (80.5%). There were 243 patients who were not prescribed any medications during follow-up; 1-year event rates were higher for these patients (25.1%, 95% CI 19.7-30.6%) versus those taking ≥1 medications (6.6%, 95% CI 5.76-7.34%). The overall rate of good adherence was 52.9%. Good adherence was associated with lower risk of 1-year events (adjusted hazard ratio 0.61, 95% CI 0.49-0.77). The most common reason for poor adherence was belief that one's condition had improved/no longer required medication. More comorbidities and lower education level were associated with poor adherence. Conclusions Good adherence reduced 1-year cardiovascular event risk after AMI . About half of our cohort did not have good adherence. National efforts to improve AMI outcomes in China should focus on medication adherence and educating patients on the importance of cardiovascular medications for reducing risk of recurrent events. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT01624909.