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.

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*

Objective: To compare the predictive efficacy of the two thrombosis risk assessment scores (Padua and IMPEDE scores) in venous thromboembolism (VTE) within 6 months in patients with newly diagnosed multiple myeloma (NDMM) in China. Methods: This study reviewed the clinical data of 421 patients with NDMM hospitalized in Beijing Jishuitan Hospital from April 2014 to February 2022. The sensitivity, specificity, accuracy, and Youden index of the two scores were calculated to quantify the thrombus risk assessment of VTE by the Padua and IMPEDE scores. The receiver operating characteristics curves of the two evaluation scores were drawn. Results: The incidence of VTE was 14.73%. The sensitivity, specificity, accuracy, and Youden index of the Padua score were 100%, 0%, 14.7%, and 0% and that of the IMPEDE score was 79%, 44%, 49.2%, and 23%, respectively. The areas under the curve of Padua and IMPEDE risk assessment scores were 0.591 and 0.722, respectively. Conclusion: IMPEDE score is suitable for predicting VTE within 6 months in patients with NDMM.
Humans ; Venous Thromboembolism/etiology* ; Multiple Myeloma/diagnosis* ; Risk Assessment ; Risk Factors ; ROC Curve ; Retrospective Studies

Objective: To explore the value of cardiac magnetic resonance imaging (CMR) in the risk stratification of hypertrophic cardiomyopathy (HCM). Methods: HCM patients who underwent CMR examination in Fuwai Hospital between March 2012 and May 2013 were retrospectively enrolled. Baseline clinical and CMR data were collected and patient follow-up was performed using telephone contact and medical record. The primary composite endpoint was sudden cardiac death (SCD) or and equivalent event. The secondary composite endpoint was all-cause death and heart transplant. Patients were divided into SCD and non-SCD groups. Cox regression was used to explore risk factors of adverse events. Receiver operating characteristic (ROC) curve analysis was used to assess the performance and the optimal cut-off of late gadolinium enhancement percentage (LGE%) for the prediction of endpoints. Kaplan-Meier and log-rank tests were used to compare survival differences between groups. Results: A total of 442 patients were enrolled. Mean age was (48.5±12.4) years and 143(32.4%) were female. At (7.6±2.5) years of follow-up, 30 (6.8%) patients met the primary endpoint including 23 SCD and 7 SCD equivalent events, and 36 (8.1%) patients met the secondary endpoint including 33 all-cause death and 3 heart transplant. In multivariate Cox regression, syncope(HR=4.531, 95%CI 2.033-10.099, P<0.001), LGE% (HR=1.075, 95%CI 1.032-1.120, P=0.001) and left ventricular ejection fraction (LVEF) (HR=0.956, 95%CI 0.923-0.991, P=0.013) were independent risk factors for primary endpoint; Age (HR=1.032, 95%CI 1.001-1.064, P=0.046), atrial fibrillation (HR=2.977, 95%CI 1.446-6.131, P=0.003),LGE% (HR=1.075, 95%CI 1.035-1.116, P<0.001) and LVEF (HR=0.968, 95%CI 0.937-1.000, P=0.047) were independent risk factors for secondary endpoint. ROC curve showed the optimal LGE% cut-offs were 5.1% and 5.8% for the prediction of primary and secondary endpoint, respectively. Patients were further divided into LGE%=0, 0P<0.001) and the accumulated incidence of primary endpoint was 1.2% (2/161), 2.2% (2/89), 10.5% (16/152) and 25.0% (10/40), respectively. Conclusion: LGE is an independent risk factor for SCD events as well as all-cause death and heart transplant. LGE is of important value in the risk stratification in patients with HCM.
Humans ; Female ; Adult ; Middle Aged ; Male ; Contrast Media ; Retrospective Studies ; Stroke Volume ; Gadolinium ; Ventricular Function, Left ; Magnetic Resonance Imaging ; Cardiomyopathy, Hypertrophic/diagnostic imaging* ; Death, Sudden, Cardiac ; Risk Assessment

Objective: To explore the risk intensity and related influencing factors of post-traumatic stress disorder (PTSD) among high-stress rescue workers, and to provide effective tools for the risk assessment of PTSD in military rescue workers. Method: From June to August 2022, cluster sampling was used to select the high-stress rescue personnel of an Army department as the survey subjects. The acute Stress reaction (ASD) scale and PTSD checklist were used to evaluate the risk of PTSD in military rescue personnel. Multivariate logistic regression were used to analyze the influencing factors of PTSD. Results: The age of 4 460 subjects was (24.38±4.072) years old, including 4 396 males (98.6%). The positive rate of initial screening for ASD was 2.85% (127/4 460). The positive rate of PTSD was 0.67% (30/4 460). Multivariate logistic regression model analysis showed that female, older age, recent trauma exposure history, passive smoking and alcohol consumption were at higher risk of ASD, the values of OR (95%CI) were 4.183 (1.819-9.618), 6.278 (1.363-28.912), 3.094 (1.500-6.379), 2.059 (1.298-3.267) and 2.607 (1.614-4.211), respectively; Lower education level was associated with lower risk of ASD, OR (95%CI) was 0.593 (0.359-0.978); People who are older, thinner, have a history of mental illness, and drink alcohol were at higher risk for PTSD, the values of OR (95%CI) were 20.144 (2.459-165.043), 10.287 (2.218-47.700), 91.104 (8.592-965.980) and 2.866 (1.144-7.180), respectively. Conclusion: Gender, age, education level, passive smoking, alcohol consumption, past history of mental illness and body mass index may be related to the potential risk of PTSD in rescue workers,passive smoking, alcohol consumption, and weight controlling should be focused on to reduce potential risks of PTSD.
Male ; Humans ; Female ; Young Adult ; Adult ; Stress Disorders, Post-Traumatic/prevention & control* ; Tobacco Smoke Pollution ; Risk Assessment ; Military Personnel ; Alcohol Drinking