Early risk stratification of acute myocardial infarction using a simple physiological prognostic scoring system: insights from the REACP study

Hollow-microsphere-integrated optofluidic immunochip for myocardial infarction biomarker microanalysis

This work developed an optofluidic immunochip that uses whispering gallery mode with fiber laser enhancement, for the rapid detection of a key biomarker cardiac troponin I for acute myocardial infarction (AMI). The immunochip adopted an innovative design, using perforated hollow glass microspheres (HGMS) as carriers, with antibodies immobilized on the inner surface of the HGMS, thereby achieving ultra-low sample consumption. The performance of the immunochip was improved by fiber laser, including spectral width compression to 0.019 nm, optical signal-to-noise ratio amplification to 63.17 dB, and an enhancement in the limit of detection to 5 pg/mL. Moreover, this immunochip can provide results within 15 min, making it highly suitable for early AMI risk management. Compared to the standard electrochemiluminescence detection method, although some differences exist in the results of the immunochip due to the principle of detection and differences in antibody affinity, its positive reference value can be calculated as 0.0754 ng/mL, with a successful recognition rate of 88% for positive patients. The immunosensor is integrated on a polydimethylsiloxane substrate, with a compact size suitable for use in point-of-care devices and AMI self-screening, as well as rapid disease screening and microanalysis of various biomarkers, offering new possibilities for applications in these fields.

Comparing TIMI, HEART, and GRACE Risk Scores to Predict Angiographic Severity of Coronary Artery Disease and 30-Day Major Adverse Cardiac Events in Emergency Department Patients with NSTEACS

BACKGROUND

Acute coronary syndromes (ACS) are hard to diagnose because their clinical presentation is broad. Current guidelines suggest early clinical risk stratification to the optimal site of care. The aim of this study was to investigate the ability of Thrombolysis in Myocardial Infarction (TIMI); History, Electrocardiogram, Age, Risk Factors, Troponin (HEART); and Global Registry of Acute Coronary Events (GRACE) risk scores to predict the development of major adverse cardiac events (MACE) and the angiographic severity of coronary artery disease (CAD) in patients diagnosed with non-ST-segment elevation acute coronary syndrome (NSTEACS) in the emergency department (ED). In addition, independent variables associated with the development of MACE were also examined.

METHODS

This study is a prospective, observational, single-center study. All patients over 18 years of age who were planned to be hospitalized for pre-diagnosed NSTEACS (NSTEMI + UAP) were included in the study consecutively. Patients' demographic information and all variables necessary for calculating risk scores (TIMI, HEART, and GRACE) were recorded. Two experienced cardiologists evaluated all coronary angiograms and calculated the Gensini score.

RESULTS

The median age was 60 (IQR: 18) years, and 220 (61.6%) were male of the 357 patients included in the study. In this study, 91 MACE (52 percutaneous coronary interventions [PCI], 28 coronary artery bypass graft [CABG], three cerebrovascular disease [CVD], and eight deaths) occurred. The 30-day MACE rate was 25.5%. The low-risk group constituted 40.0%, 1.4%, and 68.0% of the population, respectively, in TIMI, HEART, and GRACE scores. Multiple logistic regression models for predicting MACE, age (P = .005), mean arterial pressure (MAP; P = .015), and High-Sensitive Troponin I (P = .004) were statistically significant.

CONCLUSION

The ability of the GRACE, HEART, and TIMI risk scores to predict severe CAD in patients with NSTEACS is similar. In patients with NSTEACS, the HEART and GRACE risk scores can better predict the development of MACE than the TIMI risk score. When low-risk groups are evaluated according to the three risk scores, the HEART score is more reliable to exclude the diagnosis of NSTEACS.

The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis

BACKGROUND

Acute coronary syndromes (ACS) are the leading cause of global death. Optimizing mortality risk prediction and early identification of high-risk patients is essential for developing targeted prevention strategies. Many researchers have built machine learning (ML) models to predict the mortality risk in ACS patients. Our meta-analysis aimed to evaluate the predictive value of various ML models in predicting death in ACS patients at different times.

METHODS

PubMed, Embase, Web of Science, and Cochrane Library were searched systematically from database establishment to March 12, 2022 for studies developing or validating at least one ML predictive model for death in ACS patients. We used PROBAST to assess the risk of bias in the reported predictive models and a random-effects model to assess the pooled C-index and accuracy of these models.

RESULTS

Fifty papers were included, involving 216 ML prediction models, 119 of which were externally validated. The combined C-index of the ML models in the validation cohort predicting the in-hospital mortality, 30-day mortality, 3- or 6-month mortality, and 1 year or above mortality in ACS patients were 0.8633 (95% CI 0.8467-0.8802), 0.8296 (95% CI 0.8134-0.8462), 0.8205 (95% CI 0.7881-0.8541), and 0.8197 (95% CI 0.8042-0.8354), respectively, with the corresponding combined accuracy of 0.8569 (95% CI 0.8411-0.8715), 0.8282 (95% CI 0.7922-0.8591), 0.7303 (95% CI 0.7184-0.7418), and 0.7837 (95% CI 0.7455-0.8175), indicating that the ML models were relatively excellent in predicting ACS mortality at different times. Furthermore, common predictors of death in ML models included age, sex, systolic blood pressure, serum creatinine, Killip class, heart rate, diastolic blood pressure, blood glucose, and hemoglobin.

CONCLUSIONS

The ML models had excellent predictive power for mortality in ACS, and the methodologies may need to be addressed before they can be used in clinical practice.

Evaluation of risk stratification program based on trajectories of functional capacity in patients with acute coronary syndrome: The REACP study

Background

As a validated assessment tool for functional disability (activities of daily living), the Barthel index (BI) assessed initially at admission has the potential to stratify patients with high-risk acute coronary syndrome (ACS). Dynamic trajectory evaluation of functional capacity in hospitals may provide more prognostic information. We aimed to establish a novel dynamic BI-based risk stratification program (DBRP) during hospitalization to predict outcomes among ACS patients.

Methods

A total of 2,837 ACS patients were included from the Retrospective Multicenter Study for Early Evaluation of Acute Chest Pain. The DBRP rating (low, medium, and high-risk categories) was calculated from dynamic BI at admission and discharge. The primary outcome was all-cause mortality, and the secondary outcome was cardiac mortality.

Results

Of all the included patients, 312 (11%) died during a median follow-up period of 18.0 months. Kaplan-Meier analysis revealed that the cumulative mortality was significantly higher in patients in the higher risk category according to the DBRP. Multivariable Cox regression analysis indicated that, compared to the low-risk category, the higher risk category in the DBRP was an independent strong predictor of all-cause mortality after adjusting for confounding factors (medium-risk category: hazard ratio [HR]: 1.756, 95% confidence interval [95% CI]: 1.214-2.540; P = 0.003; high-risk category: HR: 5.052, 95% CI: 3.744-6.817; P < 0.001), and the same result was found for cardiac mortality.

Conclusion

The DBRP was a useful risk stratification tool for the early dynamic assessment of patients with ACS.

Clinical trial registration

[http://www.chictr.org.cn], identifier [ChiCTR1900024657].

Associations of serum expressions of miR-499 and sex determining region Y-box 6 with prognosis of acute myocardial infarction patients

Background: To explore the associations of serum expressions of miR-499 and sex determining region Y-box 6 (SOX6) with major adverse cardiovascular and cerebrovascular events (MACCE) and prognosis of acute myocardial infarction (AMI) patients undergoing percutaneous coronary intervention (PCI).

Methods: The clinical data of 132 patients diagnosed from February 2016 to October 2019 were collected. Serum miR-499 and SOX6 expressions were detected by RT-qPCR. Optimal cut-off values were determined using receiver operating characteristic curves, based on which patients were divided into low and high miR-499 expression groups, and high and low SOX6 expression groups. Survival curves were plotted using Kaplan-Meier method, and the independent risk factors for MACCE were explored by multivariate logistic regression analysis. A nomogram model was established based on the factors and validated using internal data.

Results: AMI group had higher miR-499 expression and lower SOX6 expression than those of control group (P<0.05). After PCI, miR-499 expression decreased and SOX6 expression increased (P<0.05). Low miR-499 expression group had higher 3-year survival and MACCE-free rates than those of high miR-499 expression group (P<0.05). Low SOX6 expression group had lower 3-year survival and MACCE-free rates than those of high SOX6 expression group (P<0.05). AMI history, LVEF, CK-MB, miR-499 and SOX6 expressions were independent risk factors for MACCE (P<0.05). The nomogram model had high accuracy for predicting overall survival, with a concordance index of 0.742 (95%CI=0.684-0.845).

Conclusions: AMI patients have increased serum expression of miR-499 and decreased expression of SOX6. High miR-499 expression is an independent risk factor for poor prognosis. The established nomogram model can be used to predict the risk of MACCE after PCI.

Silent Myocardial Infarction and Long-Term Risk of Frailty: The Atherosclerosis Risk in Communities Study

Background

Silent myocardial infarction (SMI) accounts for more than half of all MIs, and common risk factors and pathophysiological pathways coexist between SMI and frailty. The risk of frailty among patients with SMI is not well established. This study aimed to examine the association between SMI and frailty.

Methods and Results

This analysis included data from the Atherosclerosis Risk in Communities study. Patients without MI at baseline were eligible for inclusion. SMI was defined as electrocardiographic evidence of MI without clinical MI (CMI) after the baseline and until the fourth visit. Frailty was assessed during the fifth visit. A total of 4953 participants were included with an average age of 52.2±5.1 years. Among these participants, 2.7% (n=135) developed SMI, and 2.9% (n=146) developed CMI. After a median follow-up time of 14.7 (14.0–15.3) years, 6.7% (n=336) of the participants developed frailty. Patients with SMI and CMI were significantly more likely to become frail than those without MI (15.6% vs 6.2%, P<0.001 and 16.4% vs 6.2%, P<0.001, respectively). After adjusting for confounders, SMI and CMI were found to be independent predictors of frailty (odds ratio [OR]=2.243, 95% confidence interval [CI]=1.307–3.850, P=0.003 and OR=2.164, 95% CI=1.259–3.721, P=0.005, respectively). The association was consistent among the subgroups of age, sex, race, diabetes, and hypertension.

Conclusion

In conclusion, both SMI and CMI were found to be associated with a higher risk of frailty. Future studies are needed to confirm the beneficial effects of screening for SMI as well as to implement standardized preventive treatment to reduce the risk of frailty.

Clinical Trial Registration

URL: https://www.clinicaltrials.gov; Unique identifier: NCT00005131.