Ten-Year Mortality in Patients With ST-Elevation Myocardial Infarction

Adenosine deaminase is a risk factor for mortality after discharge in patients with acute myocardial infarction: Long-term clinical follow-up

Background

Variations in adenosine deaminase (ADA) activity have been detected in numerous cardiovascular diseases (CVDs), but there is limited research on its role in the prognosis of CVDs. In this study, we explored the role of ADA in the prognosis of patients with acute myocardial infarction (AMI).

Method

In this study, a total of 1,574 patients with a first diagnosis of acute myocardial infarction (AMI) were followed up for a median (interquartile range [IQR]) of 77.0 (50.0, 95.0) months after discharge. Cox proportional hazards regression models were used to identify factors that are substantially valuable for patient prognosis.

Results

During the follow-up period, the mortality rate of AMI was 12.5 %. The 3-year and 5-year overall survival (OS) rates of AMI patients were 93.8 % and 91.0 %, respectively. Multivariate Cox regression analysis revealed that serum ADA (hazard ratio [HR] = 1.166, 95 % confidence interval [CI]: 1.006-1.352) was an independent risk factor for 5-year OS after discharge in AMI patients. When serum ADA was assessed in quartiles, compared with the reference group (Quartile 1), the adjusted HR for death was 2.498 (95 % CI: 1.344-4.642) in Quartile 4 for 5-year OS and 2.508 (95 % CI: 1.145-5.496) in Quartile 4 for 3-year OS.

Conclusions

Serum ADA levels at admission are a risk factor that affects the long-term prognosis of AMI patients after hospital discharge.

Ten-year prognostic impact of target versus non-target vessel failure after STEMI. Insight from the EXAMINATION-EXTEND trial

INTRODUCTION AND OBJECTIVES

After ST-segment myocardial infarction (STEMI), the impact of different adverse events on prognosis remains unknown. We aimed to assess very long-term predictors of patient-oriented composite endpoints (POCE) and investigate whether the occurrence of target vessel failure (TVF) vs a non-TVF event as the first event could potentially influence subsequent outcomes.

METHODS

The EXAMINATION-EXTEND trial randomized STEMI patients to receive either an everolimus-eluting stent or a bare-metal stent. The follow-up period was 10 years. Predictors of POCE (a composite of all-cause death, any myocardial infarction, or any revascularization) were evaluated in the overall study population. The patients were stratified based on the type of first event (TVF-first vs non-TVF-first) and were compared in terms of subsequent POCE. TVF was defined as a composite of cardiac death, TV myocardial infarction, or TV revascularization.

RESULTS

Out of the 1498 enrolled patients, 529 (35.3%) experienced a POCE during the 10-year follow-up. Independent predictors of POCE were age, diabetes mellitus, previous myocardial infarction, peripheral arterial disease, and multivessel coronary disease. The first event was a TVF in 296 patients and was a non-TVF in 233 patients. No significant differences were observed between TVF-first and non-TVF-first patients in terms of subsequent POCE (21.7% vs 39.3%, time ratio 1.79; 95%CI, 0.87-3.67;P=.12) or its individual components.

CONCLUSIONS

At the 10-year follow-up, approximately one-third of STEMI patients had experienced at least 1 POCE. Independent predictors of these events were age, diabetes, and more extensive atherosclerotic disease. The occurrence of a TVF or a non-TVF as the first event did not seem to influence subsequent outcomes.

TRIAL REGISTRATION NUMBER

NCT04462315.

Prediction models for major adverse cardiovascular events following ST-segment elevation myocardial infarction and subgroup-specific performance

Background

ST-segment elevation myocardial infarction (STEMI) patients are at a high residual risk of major adverse cardiovascular events (MACEs) after revascularization. Risk factors modify prognostic risk in distinct ways in different STEMI subpopulations. We developed a MACEs prediction model in patients with STEMI and examined its performance across subgroups.

Methods

Machine-learning models based on 63 clinical features were trained in patients with STEMI who underwent PCI. The best-performing model (the iPROMPT score) was further validated in an external cohort. Its predictive value and variable contribution were studied in the entire population and subgroups.

Results

Over 2.56 and 2.84 years, 5.0% and 8.33% of patients experienced MACEs in the derivation and external validation cohorts, respectively. The iPROMPT score predictors were ST-segment deviation, brain natriuretic peptide (BNP), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), age, hemoglobin, and white blood cell (WBC) count. The iPROMPT score improved the predictive value of the existing risk score, with an increase in the area under the curve to 0.837 [95% confidence interval (CI): 0.784-0.889] in the derivation cohort and 0.730 (95% CI: 0.293-1.162) in the external validation cohort. Comparable performance was observed between subgroups. The ST-segment deviation was the most important predictor, followed by LDL-C in hypertensive patients, BNP in males, WBC count in females with diabetes mellitus, and eGFR in patients without diabetes mellitus. Hemoglobin was the top predictor in non-hypertensive patients.

Conclusion

The iPROMPT score predicts long-term MACEs following STEMI and provides insights into the pathophysiological mechanisms for subgroup differences.

Simplified acute physiology score III is excellent for predicting in-hospital mortality in coronary care unit patients with acute myocardial infarction: A retrospective study

Background

Coronary care unit (CCU) patients with acute myocardial infarction (AMI) lack effective predictors of in-hospital mortality. This study aimed to investigate the performance of four scoring systems in predicting in-hospital mortality in CCU patients with AMI.

Methods

The baseline data, the logistic organ dysfunction system (LODS), the Oxford acute severity of illness score (OASIS), the simplified acute physiology score II (SAPS II), and the simplified acute physiology score III (SAPS III) scores of the patients were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC-IV) database. Independent risk factors for in-hospital mortality were identified by regression analysis. We performed receiver operating characteristic (ROC) curves and compared the area under the curve (AUC) to clarify the predictive value of the four scoring systems. Meanwhile, Kaplan-Meier curves and decision curve analysis (DCA) were performed to determine the optimal scoring system for predicting in-hospital mortality.

Results

A total of 1,098 patients were included. The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI before and after the propensity score matching (PSM) analysis. The discrimination of in-hospital mortality by SAPS III was superior to that of LODS, OASIS, and SAPS II. The AUC of the SAPS III scoring system was the highest among the four scoring systems, at 0.901 (before PSM) and 0.736 (after PSM). Survival analysis showed that significantly more in-hospital mortality occurred in the high-score SAPS III group compared to the low-score SAPS III group before PSM (HR 7.636, P < 0.001) and after PSM (HR 2.077, P = 0.005). The DCA curve of SAPS III had the greatest benefit score across the largest threshold range compared to the other three scoring systems.

Conclusion

The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI. The predictive value for in-hospital mortality with SAPS III is superior to that of LODS, OASIS, and SAPS II. The results of the DCA analysis suggest that SAPS III may provide a better clinical benefit for patients. We demonstrated that SAPS III is an excellent scoring system for predicting in-hospital mortality for CCU patients with AMI.

Trends in 10-Year Outcomes Among Medicare Beneficiaries Who Survived an Acute Myocardial Infarction

Importance

Short-term outcomes after acute myocardial infarction (AMI) have improved, but little is known about longer-term outcomes.

Objective

To evaluate trends in 10-year all-cause mortality and hospitalization for recurrent AMI by demographic subgroups and examine the association between recurrence and mortality.

Design, Setting, and Participants

Medicare fee-for-service beneficiaries who survived after AMI from 1995 to 2019. Subgroups were defined by age, sex, race, dual Medicare-Medicaid-eligible status, and residence in health priority areas (geographic areas with persistently high adjusted mortality and hospitalization rates). Data were analyzed from October 2020 to February 2022.

Exposure

Medicare fee-for-service beneficiaries who survived an AMI.

Main Outcomes and Measures

Ten-year all-cause mortality and hospitalization for recurrent AMI, beginning 30 days from the index AMI admission.

Results

Of an included 3 982 266 AMI survivors, 1 952 450 (49.0%) were female, and the mean (SD) age was 78.0 (7.4) years. Ten-year mortality and recurrent AMI rates were 72.7% (95% CI, 72.6-72.7) and 27.1% (95% CI, 27.0-27.2), respectively. Adjusted annual reductions were 1.5% (95% CI, 1.4-1.5) for mortality and 2.7% (95% CI, 2.6-2.7) for recurrence. In subgroup analyses balancing patient characteristics, hazard ratios (HRs) for mortality and recurrence were 1.13 (95% CI, 1.12-1.13) and 1.07 (95% CI, 1.06-1.07), respectively, for men vs women; 1.05 (95% CI, 1.05-1.06) and 1.08 (95% CI, 1.07-1.09) for Black vs White patients; 0.96 (95% CI, 0.95-0.96) and 1.00 (95% CI, 1.00-1.01) for other race (including American Indian and Alaska Native, Asian, Hispanic, other race or ethnicity, and unreported) vs White patients; 1.24 (95% CI, 1.24-1.24) and 1.21 (95% CI, 1.20-1.21) for dual Medicare-Medicaid-eligible vs non-dual Medicare-Medicaid-eligible patients; and 1.06 (95% CI, 1.06-1.07) and 1.00 (95% CI, 1.00-1.01) for patients in health priority areas vs other areas. For patients hospitalized in 2007 to 2009, the last 3 years for which full 10-year follow-up data were available, 10-year mortality risk was 13.9% lower than for those hospitalized in 1995 to 1997 (adjusted HR, 0.86; 95% CI, 0.85-0.87) and 10-year recurrence risk was 22.5% lower (adjusted HR, 0.77; 95% CI, 0.76-0.78). Mortality within 10 years after the initial AMI was higher for patients with a recurrent AMI (80.6%; 95% CI, 80.5-80.7) vs those without recurrence (72.4%; 95% CI, 72.3-72.5).

Conclusions and Relevance

In this study, 10-year mortality and hospitalization for recurrence rates improved over the last decades for patients who survived the acute period of AMI. There were marked differences in outcomes and temporal trends across demographic subgroups, emphasizing the urgent need for prioritization of efforts to reduce inequities in long-term outcomes.

Long-term Survival After Out-of-Hospital Cardiac Arrest: A Systematic Review and Meta-analysis

Importance

Data on long-term survival beyond 12 months after out-of-hospital cardiac arrest (OHCA) of a presumed cardiac cause are scarce.

Objective

To investigate the long-term survival of adult patients after surviving the initial hospital stay for an OHCA.

Data Sources

A systematic search of the EMBASE and MEDLINE databases was performed from database inception to March 25, 2021.

Study Selection

Clinical studies reporting long-term survival after OHCA were selected based on predefined inclusion and exclusion criteria according to a preregistered study protocol.

Data Extraction and Synthesis

Patient data were reconstructed from Kaplan-Meier curves using an iterative algorithm and then pooled to generate survival curves. As a separate analysis, an aggregate data meta-analysis was performed.

Main Outcomes and Measures

The primary outcome was long-term survival (>12 months) after OHCA for patients surviving to hospital discharge or 30 days after OHCA.

Results

The search identified 15 347 reports, of which 21 studies (11 800 patients) were included in the Kaplan-Meier-based meta-analysis and 33 studies (16 933 patients) in an aggregate data meta-analysis. In the Kaplan-Meier-based analysis, the median survival time for patients surviving to hospital discharge was 5.0 years (IQR, 2.3-7.9 years). The estimated survival rates were 82.8% (95% CI, 81.9%-83.7%) at 3 years, 77.0% (95% CI, 75.9%-78.0%) at 5 years, 63.9% (95% CI, 62.3%-65.4%) at 10 years, and 57.5% (95% CI, 54.8%-60.1%) at 15 years. Compared with patients with a nonshockable initial rhythm, patients with a shockable rhythm had a lower risk of long-term mortality (hazard ratio, 0.30; 95% CI, 0.23-0.39; P < .001). Different analyses, including an aggregate data meta-analysis, confirmed these results.

Conclusions and Relevance

In this comprehensive systematic review and meta-analysis, long-term survival after 10 years in patients surviving the initial hospital stay after OHCA was between 62% and 64%. Additional research is needed to understand and improve the long-term survival in this vulnerable patient population.

Development and Validation of Risk Prediction Model for New-Onset Diabetes After Percutaneous Coronary Intervention (NODAP): A Study Protocol for a Retrospective, Multicenter Analysis

Background: Type 2 Diabetes mellitus (T2DM) is a major risk factor for cardiovascular diseases and increase mortality. Clinical outcomes of patients after percutaneous coronary intervention (PCI) were worse in T2DM patients than those without T2DM. New-onset diabetes after PCI (NODAP) is often observed during long-term follow-up and this further aggravates cardiovascular diseases. Several studies had focused on patients after PCI with known T2DM. Previous studies showed that impaired glucose tolerance and aging are risk factors that promote NODAP. Considering the unique characteristics of patients after PCI, we will further study relevant risk factors. We sought to investigate the potential predictors of acute coronary syndrome patients with NODAP by a multicenter retrospective cohort study.

Methods: This is a multicenter retrospective cohort study including patients after PCI. Clinical medical records of these patients were collected from four hospitals in different areas in China, from 2010 to 2021. Patients' demographic information, medical history, diagnostic testing, PCI-related information, medication situation will be summarized using descriptive statistics, and correlation analysis was performed on the development of new-onset diabetes. Variation will be described and evaluated using χ² test or Kreskas-Wallis test. The prediction model will be verified by a validation set.

Discussion: A novel diabetes prediction model for patients after PCI is established, and this study can achieve advanced intervention for the occurrence of NODAP. Owing to its retrospective nature, this study has some limitations, but it will be further studied through supplement data collection or prospective study. The study has been registered for clinical trials by the Chinese Clinical Trial Registry (ChiCTR2100047241).