INTRODUCTION

This retrospective cohort study investigated the quality of care provided to patients with acute myocardial infarction (AMI) at a non-PCI capable tertiary hospital. We employed validated quality indicators (QIs) endorsed by the European Society of Cardiology (ESC) to assess adherence to evidence-based guidelines for AMI care.

This retrospective cohort study aims to comprehensively evaluate the quality of acute myocardial infarction (AMI) care provided at a non-PCI capable tertiary hospital by utilizing validated quality indicators (QIs). The study assesses adherence to evidence-based guidelines, identifies areas of improvement, and explores the association between care processes and patient outcomes.

This retrospective cohort study analyzed patients admitted with acute myocardial infarction (AMI) to a non-percutaneous coronary intervention (PCI) capable tertiary hospital between January 2021 and December 2022. Data on quality indicators were systematically extracted from medical records to assess adherence to clinical guidelines and patient outcomes. Logistic regression was used to identify predictors of mortality, while controlling for potential confounders such as demographic and clinical characteristics. Ethical approval was granted, and patient data was anonymized in compliance with national regulations.

The study identified a patient population consistent with established cardiovascular risk factors. Adherence rates to QIs varied across different domains. Notably, the risk-adjusted 30-day mortality rate was 29.09%, highlighting the need for further investigation into factors influencing patient outcomes.

Our study highlights both strengths and gaps in adherence to AMI quality indicators at a non-PCI hospital. While key treatments such as P2Y12 inhibitor use and anticoagulation were well implemented, areas like reperfusion protocols, LVEF measurement, and data collection require improvement. These findings reinforce the importance of evidence-based practices and the need for targeted quality improvement initiatives to address disparities in care. Future efforts should focus on enhancing data collection and exploring the reasons behind regional variations to optimize outcomes for AMI patients in resource-limited settings.

Environmental Exposure/adverse effects* ; Risk Assessment ; Epidemiologic Studies

Several studies have shown the serious implications of malnutrition, yet it is still underestimated, understudied and an undertreated problem in hospitalized patients. It remains a challenge for hospitals in the Philippines. Pre-operative malnutrition is a risk factor of perioperative morbidity and mortality. Malnourished patients have longer hospital stay and have higher risk of complications. Thus assessing the pre-operative nutritional status is necessary in planning early nutritional interventions and may predict risk of developing postoperative complications.

A prospective cohort study was conducted among adult patients ages 18 to 70 years old admitted for abdominopelvic surgery at St. Paul's Hospital lloilo from January 2021 to January 2022. Within 24-48 hours of admission, patient demographic and clinical profiles were identified and the presence of nutritional risk was evaluated using the Malnutrition Universal Screening tool (MUST). SPSS version 20 was used to analyze the data. Further statistical analysis was done using Cross Tabulation, Pearson Chi-Square and Logistic Regression.

The study demonstrates that nutritional risk, age, presence of malignancy, smoking and alcoholic beverage drinking are significantly correlated with post-operative complications.

Thus, nutritional risk screening using MUST pre-operatively can predict the outcomes of postoperative patients undergoing abdominopelvic operation.

Literature regarding the impacts of heavy metal exposure on erectile dysfunction (ED) is scarce. We aimed to evaluate the correlation between 10 urinary metals and ED in a large, nationally representative adult male sample. The dataset was extracted from the National Health and Nutrition Examination Survey (NHANES) during the period of 2001-2002 and 2003-2004. Weighted proportions and multivariable logistic regression analysis adjusted for confounding variables were utilized to determine the relationship between metal exposure and ED. Weighted quantile sum (WQS) regression was utilized to evaluate the impact of a mixture of urinary metals on ED. A total of 1328 participants were included in our study. In multivariable logistic regression analysis, cobalt (Co) and antimony (Sb) were positively associated with ED (odds ratio [OR]: 1.36, 95% confidence interval [CI]: 1.10-1.73, P = 0.020; and OR: 1.41, 95% CI: 1.12-1.77, P = 0.018, respectively) after full adjustment. Men in tertile 4 for Co (OR: 1.49, 95% CI: 1.02-2.41, P for trend = 0.012) and Sb (OR: 1.53, 95% CI: 1.08-2.40, P for trend = 0.041) had significantly higher odds of ED than those in tertile 1. Furthermore, the WQS index was significantly linked with increased odds of ED after full adjustment (OR: 1.31, 95% CI: 1.04-1.72, P < 0.05). Our study expanded on previous literature indicating the possible role of heavy metal exposure in the etiology of ED. The evaluation of heavy metal exposure should be included in the risk assessment of ED.
Adult ; Humans ; Male ; United States ; Erectile Dysfunction/etiology* ; Nutrition Surveys ; Metals, Heavy ; Risk Assessment

The American Academy of Pediatrics updated the guidelines for the management of hyperbilirubinemia in the newborn infants with a gestational age of ≥35 weeks in September 2022. Based on the evidence over the past 18 years, the guidelines are updated from the aspects of the prevention, risk assessment, intervention, and follow-up of hyperbilirubinemia in the newborn infants with a gestational age of ≥35 weeks. This article gives an interpretation of the key points in the guidelines, so as to safely reduce the risk of bilirubin encephalopathy and unnecessary intervention.
Infant, Newborn ; Humans ; Infant ; United States ; Child ; Hyperbilirubinemia, Neonatal/therapy* ; Bilirubin ; Hyperbilirubinemia/therapy* ; Kernicterus/prevention & control* ; Risk Assessment ; Gestational Age

BACKGROUND: Previous cardiovascular risk prediction models in Japan have utilized prospective cohort studies with concise data. As the health information including health check-up records and administrative claims becomes digitalized and publicly available, application of large datasets based on such real-world data can achieve prediction accuracy and support social implementation of cardiovascular disease risk prediction models in preventive and clinical practice. In this study, classical regression and machine learning methods were explored to develop ischemic heart disease (IHD) and stroke prognostic models using real-world data. METHODS: IQVIA Japan Claims Database was searched to include 691,160 individuals (predominantly corporate employees and their families working in secondary and tertiary industries) with at least one annual health check-up record during the identification period (April 2013-December 2018). The primary outcome of the study was the first recorded IHD or stroke event. Predictors were annual health check-up records at the index year-month, comprising demographic characteristics, laboratory tests, and questionnaire features. Four prediction models (Cox, Elnet-Cox, XGBoost, and Ensemble) were assessed in the present study to develop a cardiovascular disease risk prediction model for Japan. RESULTS: The analysis cohort consisted of 572,971 invididuals. All prediction models showed similarly good performance. The Harrell's C-index was close to 0.9 for all IHD models, and above 0.7 for stroke models. In IHD models, age, sex, high-density lipoprotein, low-density lipoprotein, cholesterol, and systolic blood pressure had higher importance, while in stroke models systolic blood pressure and age had higher importance. CONCLUSION Our study analyzed classical regression and machine learning algorithms to develop cardiovascular disease risk prediction models for IHD and stroke in Japan that can be applied to practical use in a large population with predictive accuracy.
Humans ; Cardiovascular Diseases/epidemiology* ; Prognosis ; Prospective Studies ; Japan/epidemiology* ; Stroke/etiology* ; Myocardial Ischemia/epidemiology* ; Risk Assessment/methods*

BACKGROUND: There is still uncertainty regarding whether diabetes mellitus (DM) can adversely affect patients undergoing carotid endarterectomy (CEA) for carotid stenosis. The aim of the study was to assess the adverse impact of DM on patients with carotid stenosis treated by CEA. METHODS: Eligible studies published between 1 January 2000 and 30 March 2023 were selected from the PubMed, EMBASE, Web of Science, CENTRAL, and ClinicalTrials databases. The short-term and long-term outcomes of major adverse events (MAEs), death, stroke, the composite outcomes of death/stroke, and myocardial infarction (MI) were collected to calculate the pooled effect sizes (ESs), 95% confidence intervals (CIs), and prevalence of adverse outcomes. Subgroup analysis by asymptomatic/symptomatic carotid stenosis and insulin/noninsulin-dependent DM was performed. RESULTS: A total of 19 studies (n = 122,003) were included. Regarding the short-term outcomes, DM was associated with increased risks of MAEs (ES = 1.52, 95% CI: [1.15-2.01], prevalence = 5.1%), death/stroke (ES = 1.61, 95% CI: [1.13-2.28], prevalence = 2.3%), stroke (ES = 1.55, 95% CI: [1.16-1.55], prevalence = 3.5%), death (ES = 1.70, 95% CI: [1.25-2.31], prevalence =1.2%), and MI (ES = 1.52, 95% CI: [1.15-2.01], prevalence = 1.4%). DM was associated with increased risks of long-term MAEs (ES = 1.24, 95% CI: [1.04-1.49], prevalence = 12.2%). In the subgroup analysis, DM was associated with an increased risk of short-term MAEs, death/stroke, stroke, and MI in asymptomatic patients undergoing CEA and with only short-term MAEs in the symptomatic patients. Both insulin- and noninsulin-dependent DM patients had an increased risk of short-term and long-term MAEs, and insulin-dependent DM was also associated with the short-term risk of death/stroke, death, and MI. CONCLUSIONS In patients with carotid stenosis treated by CEA, DM is associated with short-term and long-term MAEs. DM may have a greater impact on adverse outcomes in asymptomatic patients after CEA. Insulin-dependent DM may have a more significant impact on post-CEA adverse outcomes than noninsulin-dependent DM. Whether DM management could reduce the risk of adverse outcomes after CEA requires further investigation.
Humans ; Endarterectomy, Carotid/adverse effects* ; Carotid Stenosis/surgery* ; Risk Factors ; Treatment Outcome ; Time Factors ; Stents/adverse effects* ; Diabetes Mellitus, Type 2/complications* ; Diabetes Mellitus, Type 1 ; Stroke/complications* ; Insulin/therapeutic use* ; Myocardial Infarction/complications* ; Risk Assessment

Pregnancy ; Female ; Humans ; COVID-19 ; SARS-CoV-2 ; Risk Factors ; Risk Assessment ; Pregnancy Complications, Infectious/epidemiology* ; Pregnancy Outcome

Humans ; Diabetes Mellitus, Type 2/epidemiology* ; Air Pollution/adverse effects* ; Air Pollutants/toxicity* ; Risk Assessment ; Particulate Matter ; Environmental Exposure

OBJECTIVE: We aimed to assess the feasibility and superiority of machine learning (ML) methods to predict the risk of Major Adverse Cardiovascular Events (MACEs) in chest pain patients with NSTE-ACS. METHODS: Enrolled chest pain patients were from two centers, Beijing Anzhen Emergency Chest Pain Center Beijing Bo'ai Hospital, China Rehabilitation Research Center. Five classifiers were used to develop ML models. Accuracy, Precision, Recall, F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system. Ultimately, ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs. RESULTS: According to learning metrics, ML models constructed by different classifiers were superior over HEART (History, ECG, Age, Risk factors, & Troponin) scoring system when predicting acute myocardial infarction (AMI) and all-cause death. However, according to ROC curves and AUC, ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI. Among the five ML algorithms, Linear support vector machine (SVC), Naïve Bayes and Logistic regression classifiers stood out with all Accuracy, Precision, Recall and F-Measure from 0.8 to 1.0 for predicting any event, AMI, revascularization and all-cause death ( vs. HEART ≤ 0.78), with AUC from 0.88 to 0.98 for predicting any event, AMI and revascularization ( vs. HEART ≤ 0.85). ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome (ACS), abnormal electrocardiogram (ECG), elevated hs-cTn I, sex and smoking were risk factors of MACEs. CONCLUSION Compared with HEART risk scoring system, the superiority of ML method was demonstrated when employing Linear SVC classifier, Naïve Bayes and Logistic. ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.
Humans ; Acute Coronary Syndrome/epidemiology* ; Bayes Theorem ; Feasibility Studies ; Risk Assessment/methods* ; Chest Pain/etiology* ; Myocardial Infarction/diagnosis*