INTRODUCTION

data-mce-style="text-align: justify;">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.

data-mce-style="text-align: justify;">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.

data-mce-style="text-align: justify;">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.

data-mce-style="text-align: justify;">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.

data-mce-style="text-align: justify;">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: Medical errors commonly occur in medical imaging departments. These errors are frequently influenced by patient safety culture. This study aimed to develop a suitable patient safety culture assessment tool for medical imaging departments. METHODS: Staff members of a teaching hospital medical imaging department were invited to complete the generic short version of the Safety Attitude Questionnaire (SAQ). Internal consistency and reliability were evaluated using Cronbach's α. Confirmatory factor analysis (CFA) was conducted to examine model fit. A cut-off of 60% was used to define the percentage positive responses (PPR). PPR values were compared between occupational groups. RESULTS: A total of 300 complete responses were received and the response rate was 75.4%. In reliability analysis, the Cronbach's α for the original 32-item SAQ was 0.941. Six subscales did not demonstrate good fit with CFA. A modified five-subscale, 22-item model (SAQ-MI) showed better fit (goodness-to-fit index ≥0.9, comparative fit index ≥ 0.9, Tucker-Lewis index ≥0.9 and root mean square error of approximation ≤0.08). The Cronbach's α for the 22 items was 0.921. The final five subscales were safety and teamwork climate, job satisfaction, stress recognition, perception of management and working condition, with PPR of 62%, 68%, 57%, 61% and 60%, respectively. Statistically significant differences in PPR were observed between radiographers, doctors and others occupational groups. CONCLUSION The modified five-factor, 22-item SAQ-MI is a suitable tool for the evaluation of patient safety culture in a medical imaging department. Differences in patient safety culture exist between occupation groups, which will inform future intervention studies.
Humans ; Surveys and Questionnaires ; Patient Safety ; Attitude of Health Personnel ; Diagnostic Imaging ; Reproducibility of Results ; Male ; Female ; Adult ; Job Satisfaction ; Factor Analysis, Statistical ; Middle Aged ; Hospitals, Teaching ; Safety Management ; Organizational Culture ; Medical Errors/prevention & control*

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: The 30-day readmission rate for chronic obstructive pulmonary disease (COPD) is a common performance metric but may be confounded by factors unrelated to quality of care. Our aim was to assess how sociodemographic factors, multimorbidity and frailty impact 30-day readmission risk after COPD hospitalisation, and whether risk adjustment alters interpretation of temporal trends. METHOD: This is a retrospective analysis of administra-tive data from October 2017 to June 2023 from Changi General Hospital, Singapore. Multivariable mixed-effects logistic regression models were used to estimate unadjusted and risk-adjusted 30-day readmission odds. Covariates included age, sex, race, Charlson Comorbidity Index (CCI), Hospital Frailty Risk Score (HFRS) and year. Temporal trends in readmission risk were compared across unadjusted and adjusted models. RESULTS: Of the 2774 admissions, 749 (27%) resulted in 30-day readmissions. Higher CCI (CCI≥4 versus [vs] CCI=1: adjusted odds ratio [aOR] 2.00, 95% confidence interval [CI] 1.33-2.99, P=0.003; CCI 2-3 vs CCI=1: aOR 1.50, 95% CI 1.15-1.96, P=0.001) and higher HFRS (≥5 vs <5: aOR 1.29, 95% CI 1.01-1.65, P=0.04) were independently associated with increased readmission risk. While unadjusted analyses showed no significant temporal trends, the risk-adjusted model revealed a 32-35% reduction in readmission odds in 2021-2023 compared to baseline. CONCLUSION Multimorbidity and frailty significantly impact COPD readmissions. Risk adjustment revealed improvements in readmission risk not evident in unadjusted analyses, emphasising the importance of applying risk adjustments to ensure valid performance metrics.
Humans ; Pulmonary Disease, Chronic Obstructive/therapy* ; Patient Readmission/trends* ; Male ; Female ; Retrospective Studies ; Aged ; Singapore/epidemiology* ; Multimorbidity ; Frailty/epidemiology* ; Middle Aged ; Risk Adjustment ; Aged, 80 and over ; Logistic Models ; Risk Factors

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