Admission proteinuria predicts the incidence of acute kidney injury among patients with acute ST-segment elevation myocardial infarction: a retrospective cohort study.

BACKGROUND: Proteinuria indicates renal dysfunction and is associated with the development of acute kidney injury (AKI) in several conditions, but the association between proteinuria and AKI in patients with ST-segment elevation myocardial infarction (STEMI) remains unclear. This research aims to investigate the predictive value of proteinuria for the development of AKI in STEMI patients. METHODS: A total of 2735 STEMI patients were enrolled. The present study's endpoint was AKI incidence during hospitalization. AKI is defined according to the Kidney Disease: Improving Global Outcomes criteria. We defined proteinuria, measured with a dipstick, as mild (1+) or heavy (2+ to 4+). Multivariate logistic regression and subgroup analyses were used to testify to the association between proteinuria and AKI. RESULTS: Overall, proteinuria was observed in 634 (23.2%) patients. Multivariate logistic regression analyses revealed that proteinuria [odds ratio (OR), 1.58; 95% confidence interval (CI), 1.25-2.00; P  < 0.001] was the independent predictive factor for AKI. Severe proteinuria was associated with a higher adjusted risk for AKI compared with the nonproteinuria group (mild proteinuria: OR, 1.35; 95% CI, 1.04-1.75; P  = 0.025; severe proteinuria: OR, 2.50; 95% CI, 1.70-3.68; P  < 0.001). The association was highly consistent across all studied subgroups. (all P for interaction >0.05). CONCLUSION: Admission proteinuria measured using a urine dipstick is an independent risk factor for the development of AKI in STEMI patients.

Analyzing predictors of in-hospital mortality in patients with acute ST-segment elevation myocardial infarction using an evolved machine learning approach.

Acute ST-segment elevation myocardial infarction (STEMI) is a severe cardiac ailment characterized by the sudden complete blockage of a portion of the coronary artery, leading to the interruption of blood supply to the myocardium. This study examines the medical records of 3205 STEMI patients admitted to the coronary care unit of the First Affiliated Hospital of Wenzhou Medical University from January 2014 to December 2021. In this research, a novel predictive framework for STEMI is proposed, incorporating evolutionary computational methods and machine learning techniques. A variant algorithm, AGCOSCA, is introduced by integrating crossover operation and observation bee strategy into the original Sine Cosine Algorithm (SCA). The effectiveness of AGCOSCA is initially validated using IEEE CEC 2017 benchmark functions, demonstrating its ability to mitigate the deficiency in local mining after SCA random perturbation. Building upon this foundation, the AGCOSCA approach has been paired with Support Vector Machine (SVM) to forge the predictive framework referred to as AGCOSCA-SVM. Specifically, AGCOSCA is employed to refine the selection of predictors from a substantial feature set before SVM is utilized to forecast the occurrence of STEMI. In our analysis, we observed that SVM excels at managing nonlinear data relationships, a strength that becomes particularly prominent in smaller datasets of STEMI patients. To assess the effectiveness of AGCOSCA-SVM, diagnostic experiments were conducted based on the STEMI sample data. Results indicate that AGCOSCA-SVM outperforms traditional machine learning methods, achieving superior Accuracy, Sensitivity, and Specificity values of 97.83 %, 93.75 %, and 96.67 %, respectively. The selected features, such as acute kidney injury (AKI) stage, fibrinogen, mean platelet volume (MPV), free triiodothyronine (FT3), diuretics, and Killip class during hospitalization, are identified as crucial for predicting STEMI. In conclusion, AGCOSCA-SVM emerges as a promising model framework for supporting the diagnostic process of STEMI, showcasing potential applications in clinical settings.

Sex differences in the association between D-dimer and the incidence of acute kidney injury in patients admitted with ST-segment elevation myocardial infarction: a retrospective observational study.

Identifying the predictors of acute kidney injury (AKI) in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) remains important. We aimed to investigate the predictive value of D-dimer levels for the incidence of AKI in such a population, with particular attention paid to sex differences. A total of 2668 patients with STEMI who underwent PPCI were retrospectively included in this study and divided into quartiles according to their plasma D-dimer levels upon admission (Q1: < 0.36; Q2: 0.36-0.67; Q3: 0.68-1.17; Q4: > 1.17 mg/L). The primary endpoint was the occurrence of AKI during hospitalization. AKI was observed in 503 (18.8%) patients. The mean age of the patients was 63.0 ± 13.2 years, 2155 (80.8%) of whom were men. Multivariate analysis indicated that higher D-dimer levels were associated with a significantly increased risk of AKI (Q4 vs. Q1: OR: 1.57; 95% CI 1.11-2.23; P = 0.011). However, the prognostic effect of D-dimer was only observed in male patients (Q4 vs. Q1: OR: 2.07; 95% CI 1.37-3.13; P < 0.001), not in female patients (Q4 vs. Q1: OR: 0.72; 95% CI 0.37-1.41; P = 0.342) (P for interaction = 0.003). We demonstrated a notable sex difference in the association between D-dimer level upon admission and AKI in a large STEMI patient sample. A higher D-dimer level was associated with an increased risk of AKI in male patients but not in female patients.

Clinical Outcome After Left Ventricular Thrombus Resolution: Who Needs Long-Term or Lifetime Use of Anticoagulants?

Background Patients with left ventricular thrombus (LVT) resolution can have LVT recurrence and risk for thromboembolism. However, these outcomes after LVT resolution are not well known. We aimed to assess the prevalence, risk factors, and clinical outcomes for LVT recurrence in patients with LVT resolution to inform follow-up and treatment. Methods and Results Patients with LVT resolution were identified retrospectively from a large echocardiography database between January 2009 and May 2022. Participants had echocardiograms at 3 time points, including baseline at LVT diagnosis, at LVT resolution, and a follow-up for identification of LVT recurrence. The cumulative LVT recurrence rate was estimated by the Kaplan-Meier method, and predictors of LVT recurrence were evaluated using Cox regression analysis. Among 115 patients with LVT resolution, 28 (24.3%) had LVT recurrence at a median follow-up of 1.2 (0.5-2.8) years. LV aneurysm (hazard ratio [HR], 2.59 [95% CI, 1.20-5.58], P=0.015) and anticoagulant use (HR, 0.12 [95% CI, 0.04-0.41], P=0.001) were predictors of LVT recurrence on multivariable analysis. Patients with an LV aneurysm who did not receive any anticoagulation demonstrated an LVT recurrence rate of 69.5%, whereas those without an LV aneurysm who received anticoagulation had a recurrence rate of 0%. Patients with LVT recurrence had a higher incidence of an embolic event (10.7% versus 1.1%, P=0.016). Conclusions LVT recurrence after LVT resolution is common, especially in those with an LV aneurysm, and is associated with a higher embolic risk. Continued anticoagulation is protective against LVT recurrence, although bleeding risk needs to be considered. These findings can inform follow-up and treatment of patients with documented LVT resolution.