Plasma BNP Levels and Diuretics Use as Predictors of Cardiovascular Events in Patients with Myocardial Infarction and Impaired Glucose Tolerance

The predictive value of relative wall thickness on the prognosis of the patients with ST-segment elevation myocardial infarction

OBJECTIVE

The study aimed to evaluate the prognostic value of relative wall thickness (RWT) in the patients with ST-segment elevation myocardial infarction (STEMI).

METHODS

A total of 866 patients with STEMI admitted in Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School from November 2010 to December 2018 were enrolled in the current study retrospectively. Three methods were used to calculate RWT: RWTPW, RWTIVS+PW and RWTIVS. The included patients were divided according to the median values of RWTPW, RWTIVS+PW, and RWTIVS, respectively. Survival analysis were performed with Kaplan-Meier plot and multivariate Cox proportional hazard model was established to evaluate the adjusted hazard ratio of the three kinds of RWT for all cause death, cardiac death and MACE (major adverse cardiac death).

RESULTS

There was no significance for the survival analysis between the low and high groups of RWTPW, RWTIVS+PW and RWTIVS at 30 days and 12 months. Nonetheless, the cumulative incidence of all cause death and cardiac death in the low group of RWTPW and RWTIVS+PW was higher than those in the high group at 60 months. The cumulative incidence of MACE in the low group of RWTPW was higher than the high group at 60 months. Multivariate Cox regression model showed that RWTPW were inversely associated with long-term cardiac death and MACE in STEMI patients. In the subgroup analysis, three calculations of RWT had no predictive value for the patients with anterior myocardial infarction. By contrast, RWTPW was the most stable independent predictor for the long-term outcomes of the patients with non-anterior myocardial infarction.

CONCLUSION

RWTPW, RWTIVS+PW and RWTIVS had no predictive value for the long-term clinical outcomes of patients with anterior myocardial infarction, whereas RWTPW was a reliable predictor for all cause death, cardiac death and MACE in patients with non-anterior myocardial infarction.

Predicting heart failure onset in the general population using a novel data-mining artificial intelligence method

We aimed to identify combinations of clinical factors that predict heart failure (HF) onset using a novel limitless-arity multiple-testing procedure (LAMP). We also determined if increases in numbers of predictive combinations of factors increases the probability of developing HF. We recruited people without HF who received health check-ups in 2010, who were followed annually for 4 years. Using 32,547 people, LAMP was performed to identify combinations of factors of fewer than four factors that could predict the onset of HF. The ability of the method to predict the probability of HF onset based on the number of matching predictive combinations of factors was determined in 275,658 people. We identified 549 combinations of factors for the onset of HF. Then we classified 275,658 people into six groups who had 0, 1-50, 51-100, 101-150, 151-200 or 201-250 predictive combinations of factors for the onset of HF. We found that the probability of HF progressively increased as the number of predictive combinations of factors increased. We identified combinations of variables that predict HF onset. An increased number of matching predictive combinations for the onset of HF increased the probability of HF onset.

From Classic to Modern Prognostic Biomarkers in Patients with Acute Myocardial Infarction

Despite all the important advances in its diagnosis and treatment, acute myocardial infarction (AMI) is still one of the most prominent causes of morbidity and mortality worldwide. Early identification of patients at high risk of poor outcomes through the measurement of various biomarker concentrations might contribute to more accurate risk stratification and help to guide more individualized therapeutic strategies, thus improving prognoses. The aim of this article is to provide an overview of the role and applications of cardiac biomarkers in risk stratification and prognostic assessment for patients with myocardial infarction. Although there is no ideal biomarker that can provide prognostic information for risk assessment in patients with AMI, the results obtained in recent years are promising. Several novel biomarkers related to the pathophysiological processes found in patients with myocardial infarction, such as inflammation, neurohormonal activation, myocardial stress, myocardial necrosis, cardiac remodeling and vasoactive processes, have been identified; they may bring additional value for AMI prognosis when included in multi-biomarker strategies. Furthermore, the use of artificial intelligence algorithms for risk stratification and prognostic assessment in these patients may have an extremely important role in improving outcomes.

Detection of sieric BAG3 in patients affected by cardiovascular diseases: State of art and perspectives

BAG3 is highly expressed in the heart and its functions are essential in maintaining cardiac muscle cells homeostasis. In the past, BAG3 was detected in serum from advanced heart failure patients and its higher levels were correlated to an increased death risk. Moreover, it has also been reported that BAG3 levels in serum are increased in patients with hypertension, a known cardiovascular risk marker. Evidence from different laboratories suggested the possibility to use BAG3-based strategies to improve the clinical outcome of cardiovascular disease patients. This review aims to highlight the biological roles of intracellular or secreted BAG3 in myocardiocytes and propose additional new data on the levels of sieric BAG3 in patients with acute myocardial infarction (AMI), never assessed before. We evaluated BAG3 serum levels in relation to cardiovascular risk parameters in 64 AMI patients aged ≥18 years of either sex. We observed significant (p < .01) correlations of BAG3 positivity with dyslipidemic status and diabetic disease. We did not observe any significant correlations of BAG3 levels with smoking habit, hypertension or familiarity for AMI, although BAG3-positive seemed to be more numerous than BAG3-negative patients among hypertensives and among patients with familiarity for AMI. Furthermore, a significant (p < .001) correlation of BAG3 positivity with diuretics assumption was also noted. In conclusion, 32.8% of the patients were BAG3-positive and were characterized by some particular features as comorbidity presence or concomitant therapies. The significance of these observations needs to be verified by more extensive studies and could help in the validation of the use of BAG3 as a biomarker in heart attack risk stratification.

Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance

PURPOSE

Glucose intolerance (GI), defined as either prediabetes or diabetes, promotes cardiovascular events in patients with myocardial infarction (MI). Using the pooled clinical data from patients with MI and GI in the completed ABC and PPAR trials, we aimed to identify their clinical risk factors for cardiovascular events.

METHODS

Using the limitless-arity multiple testing procedure, an artificial intelligence (AI)-based data mining method, we analyzed 415,328 combinations of < 4 clinical parameters.

RESULTS

We identified 242 combinations that predicted the occurrence of hospitalization for (1) percutaneous coronary intervention for stable angina, (2) non-fatal MI, (3) worsening of heart failure (HF), and (4) all causes, and we analyzed combinations in 1476 patients. Among these parameters, the use of proton pump inhibitors (PPIs) or plasma glucose levels > 200 mg/dl after 2 h of a 75 g oral glucose tolerance test were linked to the coronary events of (1, 2). Plasma BNP levels > 200 pg/dl were linked to coronary and cardiac events of (1, 2, 3). Diuretics use, advanced age, and lack of anti-dyslipidemia drugs were linked to cardiovascular events of (1, 3). All of these factors were linked to (4). Importantly, each finding was verified by independently drawn Kaplan-Meier curves, indicating that the determined factors accurately affected cardiovascular events.

CONCLUSIONS

In most previous MI patients with GI, progression of GI, PPI use, or high plasma BNP levels were linked to the occurrence of coronary stenosis or recurrent MI. We emphasize that use of AI may comprehensively uncover the hidden risk factors for cardiovascular events.