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.

INTRODUCTION: The diagnosis of sarcopenia relies on key indicators such as handgrip strength, walking speed and muscle mass. Developing a composite index that integrates these measures could enhance clinical evaluation in older adults. This study aimed to standardise and combine these metrics to establish a z score for the sarcopenia composite index (ZoSCI) tailored for the ageing population. Additionally, we explore the risk factors associated with ZoSCI to provide insights into early prevention and intervention strategies. METHOD: This retrospective study analysed data between January 2017 and December 2021 from an elderly health programme in Taiwan, applying the Asian Working Group for Sarcopenia criteria to assess sarcopenia. ZoSCI was developed by standardising handgrip strength, walking speed and muscle mass into z scores and integrating them into a composite index. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values, and multiple regression analysis identified factors influencing ZoSCI. RESULTS: Among the 5047 participants, the prevalence of sarcopenia was 3.7%, lower than the reported global prevalence of 3.9-15.4%. ROC curve analysis established optimal cut-off points for distinguishing sarcopenia in ZoSCI: -1.85 (sensitivity 0.91, specificity 0.88) for males and -1.97 (sensitivity 0.93, specificity 0.88) for females. Factors associated with lower ZoSCI included advanced age, lower education levels, reduced exercise frequency, lower body mass index and creatinine levels. CONCLUSION This study introduces ZoSCI, a new compo-site quantitative indicator for identifying sarcopenia in older adults. The findings highlight specific risk factors that can inform early intervention. Future studies should validate ZoSCI globally, with international collaborations to ensure broader applicability.
Humans ; Sarcopenia/physiopathology* ; Male ; Aged ; Female ; Retrospective Studies ; Hand Strength ; Taiwan/epidemiology* ; ROC Curve ; Aged, 80 and over ; Risk Factors ; Walking Speed ; Geriatric Assessment/methods* ; Prevalence ; Muscle, Skeletal ; Middle Aged

INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction. METHOD: Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI. RESULTS: There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV. CONCLUSION The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
Humans ; Male ; Female ; Emergency Service, Hospital ; Middle Aged ; Electrocardiography ; Machine Learning ; Singapore ; Chest Pain/blood* ; Troponin I/blood* ; Myocardial Infarction/blood* ; Risk Assessment/methods* ; Aged ; Algorithms ; Acute Coronary Syndrome/blood* ; Adult ; Sensitivity and Specificity

INTRODUCTION: Risk-based screening (RBS) has emerged as a promising alternative to age-based cancer screening. However, evidence regarding real-world implementation outcomes remains fragmented. Thus, a systematic review was conducted to evaluate the implementation metho-dologies and outcomes of RBS programmes across different cancer types. METHODS: MEDLINE, Embase, CINAHL, Web of Science, Cochrane Central Register of Controlled Trials and Scopus were systematically searched from their respective dates of inception up to 8 July 2024. Prospective and rando-mised controlled trials (RCTs), which implement the RBS of cancer in an asymptomatic population, or studies retrospectively evaluating the outcomes of the same were included. Geographic distribution, population characteristics, RBS methodology, diagnostic accuracy and clinical outcomes were narratively synthesised. RESULTS: Among the 33 included studies (i.e. 21 prospective cohort, 8 RCTs, 3 retrospective and 1 non-RCT), sample sizes ranged from 102 to 1,429,890 participants. Most RBS trials were conducted in China (n=7, 21.2%), followed by the Netherlands (n=4, 12.1%) then the US, Australia and Sweden (n=3, 9.8%). Studies predominantly examined colorectal (27.3%), breast (21.2%) and prostate cancer (18.2%). Three main stratification approaches emerged: algorithmic (48.5%), validated risk models (39.4%) and physician assessment (9.1%). Implementation outcomes showed higher uptake in moderate-risk (75.4%) compared to high-risk (71.3%) and low-risk groups (67.9%). Five studies demonstrated cost-effectiveness with increased quality-adjusted life years, while 12 studies showed superior or non-inferior cancer detection rates compared to traditional screening. CONCLUSION The RBS of cancer has the potential to optimise healthcare resource allocation while minimising harm and increasing receptiveness for patients. More work is needed to evaluate long-term outcomes prior to the scaling of RBS programmes.
Humans ; Early Detection of Cancer/methods* ; Neoplasms/diagnosis* ; Risk Assessment ; Mass Screening/methods*

This study aims to investigate the polarized microscopic mineral phase characteristics, inorganic element content, and potential health risks associated with the intake of raw and calcined fossil mineral Chinese medicinal material Draconis Os. Microscopy was employed to observe the mineralogical characteristics of Draconis Os and compare the microscopic features and phase composition of raw and calcined Draconis Os under monochromatic and orthogonal polarized light. Inductively coupled plasma mass spectrometry(ICP-MS) was employed to determine the content of 30 inorganic elements. Health risk assessment was conducted by calculating the single pollution index(P_i), average daily intake of elements for adults(ADI), target hazard quotient(THQ), non-carcinogenic assessment method-hazard quotient(HQ), and the carcinogenic risk of elements(CR). The results indicated that under monochromatic polarized light, the Draconis Os powder sections exhibited light gray-brown to gray-brown irregular fragments, some with undulating textures that were slightly curved. Under crossed polarized light, they appeared dark gray, grayish-white, and yellowish-white. Clear apatite was visible in the ground sections of Draconis Os under crossed polarized light. P_i results indicated that Draconis Os samples were free from contamination and were of good quality. According to the maximum allowable limits of heavy metals stipulated in ISO Traditional Chinese Medicine: Determination of heavy metals in herbal medicines used in Traditional Chinese Medicine, ADI, THQ, HQ, and CR were taken as assessment indicators. Only the THQ value for As(arsenic) in raw Draconis Os was greater than 1, while the THQ values for other heavy metal elements in the Draconis Os samples were all less than 1. The study demonstrates that the primary mineral phase of raw and calcined Draconis Os is apatite, with some samples co-existing with calcite, which can serve as one of the means for quality control of Draconis Os. The elemental analysis results from ICP-MS provide scientific evidence for the safety assessment of Draconis Os, indicating that Draconis Os is safe in clinical application.
Drugs, Chinese Herbal/analysis* ; Risk Assessment ; Minerals/chemistry* ; Fossils ; Humans ; Drug Contamination ; Mass Spectrometry

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
Humans ; Atrial Fibrillation/diagnosis* ; Machine Learning ; Deep Learning ; Risk Assessment/methods*

In China, the regulatory framework for medical device procurement and sales, particularly concerning anti-commercial bribery, relies heavily on punitive mechanisms applied after violations occur. Consequently, there is an urgent need to establish a scientific risk regulation framework as a complementary approach. Effective risk-oriented regulatory models require precise identification of risk areas in commercial bribery. Focusing on several major procurement scenarios such as centralized bulk-buying, tendering and bidding processes, in-hospital procurement, and online purchasing, this article analyzes the structural factors contributing to these risks, represented by the absence of certification mechanisms, lack of transparency in information disclosure, and inadequate checks and balances. Based on official risk assessment results, this study applies the theory of power and responsibility to propose a preventive regulatory framework that combines industry self-discipline and administrative oversight. By combining these approaches, the framework aims to develop regulatory measures that can effectively reduce commercial bribery risks and prevent illegal and non-compliant conduct.
China ; Equipment and Supplies/economics* ; Commerce/legislation & jurisprudence* ; Humans ; Risk Assessment

OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). METHODS: A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables. RESULTS: According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95% CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95% CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications. CONCLUSION Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
Humans ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthritis, Rheumatoid/blood* ; Machine Learning ; Postoperative Complications/blood* ; Female ; Male ; Retrospective Studies ; Middle Aged ; Aged ; Risk Factors ; Preoperative Period ; C-Reactive Protein/analysis* ; Risk Assessment

PURPOSE: To analyze risk factors for severe trauma and establish a nomogram for early risk prediction, to improve the early identification of severe trauma. METHODS: This study was conducted on the patients treated in 81 trauma treatment institutions in Gansu province from 2020 to 2022. Patients were grouped by year, with 5364 patients from 2020 to 2021 as the training set and 1094 newly admitted patients in 2020 as the external validation set. Based on the injury severity score (ISS), patients in the training set were classified into 2 subgroups of the severe trauma group (n = 478, ISS scores ≥25) and the non-severe trauma group (n = 4886, ISS scores <25). Univariate and binary logistic regression analyses were employed to identify independent risk factors for severe trauma. Subsequently, a predictive model was developed using the R software environment. Furthermore, the model was subjected to internal and external validation via the Hosmer-Lemeshow test and receiver operating characteristic curve analysis. RESULTS: In total, 6458 trauma patients were included in this study. Initially, this study identified several independent risk factors for severe trauma, including multiple traumatic injuries (polytrauma), external hemorrhage, elevated shock index, elevated respiratory rate, decreased peripheral oxygen saturation, and decreased Glasgow coma scale score (all p < 0.05). For internal validation, the area under the receiver operating characteristic curve was 0.914, with the sensitivity and specificity of 88.4% and 87.6%, respectively; while for external validation, the area under the receiver operating characteristic curve was 0.936, with the sensitivity and specificity of 84.6% and 93.7%, respectively. In addition, a good model fitting was observed through the Hosmer-Lemeshow test and calibration curve analysis (p > 0.05). CONCLUSION This study establishes a nomogram for early risk prediction of severe trauma, which is suitable for primary healthcare institutions in underdeveloped western China. It facilitates early triage and quantitative assessment of trauma severity by clinicians prior to clinical interventions.
Humans ; Nomograms ; Male ; Female ; Wounds and Injuries/diagnosis* ; Risk Factors ; Middle Aged ; Adult ; Injury Severity Score ; Risk Assessment ; ROC Curve ; Aged ; Logistic Models ; China ; Glasgow Coma Scale

OBJECTIVES: To create a mixed valvular heart disease (MVHD)-related age-adjusted comorbidity index (MVACI) model for predicting mortality risk of patients with MVHD. METHODS: A total of 4080 patients with moderate or severe MVHD in the China-VHD study were included. The primary endpoint was 2-year all-cause mortality. A MVACI model prediction model was constructed based on the mortality risk factors identified by univariate and multivariate Cox regression analysis. Restricted cubic splines were used to assess the relationship between MVACI scores and 2-year all-cause mortality. The optimal threshold, determined by the maximum Youden index from receiver operator characteristic (ROC) curve analysis, was used to stratify patients. Kaplan-Meier method was used to calculate 2-year all-cause mortality and compared using the Log-rank test. Univariate and multivariate Cox proportional hazards models were employed to calculate hazard ratios (HR) and 95% confidence intervals (CI), evaluating the association between MVACI scores and mortality. Paired ROC curves were used to compare the discriminative ability of MVACI scores with the European System for Cardiac Operative Risk Evaluation Ⅱ(EuroSCORE Ⅱ) or the age-adjusted Charlson comorbidity index (ACCI) in predicting 2-year clinical outcomes, while calibration curves assessed the calibration of these models. Internal validation was performed using the Bootstrap method. Subgroup analyses were conducted based on etiology, treatment strategies, and disease severity. RESULTS: Multivariate analysis identified the following variables independently associated with 2-year all-cause mortality in patients: pulmonary hypertension, myocardiopathy, heart failure, low body weight (body mass index <18.5 kg/m²), anaemia, hypoalbuminemia, renal insufficiency, cancer, New York Heart Association (NYHA) class and age. The score was independently associated with the risk of all-cause mortality, and exhibited good discrimination (AUC=0.777, 95%CI: 0.755-0.799) and calibration (Brier score 0.062), with significantly better predictive performance than EuroSCORE Ⅱ or ACCI (both adjusted P<0.01). The internal validation showed that the MVACI model's predicted probability of 2-year all-cause mortality was generally consistent with the actual probability. The AUCs for predicting all-cause mortality risk were all above 0.750, and those for predicting adverse events were all above 0.630. The prognostic value of the score remained consistent in patients regardless of their etiology, therapeutic option, and disease severity. CONCLUSIONS The MVACI was constructed in this study based on age and comorbidities, and can be used for mortality risk prediction and risk stratification of MVHD patients. It is a simple algorithmic index and easy to use.
Humans ; Prognosis ; Comorbidity ; Heart Valve Diseases/epidemiology* ; Female ; Male ; Middle Aged ; Aged ; Proportional Hazards Models ; Risk Factors ; China/epidemiology* ; Age Factors ; Risk Assessment ; Adult ; ROC Curve