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

COMPERA 2.0 risk stratification has been demonstrated to be useful in patients with precapillary pulmonary hypertension (PH). However, its suitability for patients at risk for post-capillary PH or PH associated with left heart disease (PH-LHD) is unclear. To investigate the use of COMPERA 2.0 in patients with severe aortic stenosis (SAS) undergoing transcatheter aortic valve replacement (TAVR), who are at risk for post-capillary PH, a total of 327 eligible SAS patients undergoing TAVR at our institution between September 2015 and November 2020 were included in the study. Patients were classified into four strata before and after TAVR using the COMPERA 2.0 risk score. The primary endpoint was all-cause mortality. Survival analysis was performed using Kaplan-Meier curves, log-rank test, and Cox proportional hazards regression model. The study cohort had a median (interquartile range) age of 76 (70‒80) years and a pulmonary arterial systolic pressure of 33 (27‒43) mmHg (1 mmHg=0.133 kPa) before TAVR. The overall mortality was 11.9% during 26 (15‒47) months of follow-up. Before TAVR, cumulative mortality was higher with an increase in the risk stratum level (log-rank, both P<0.001); each increase in the risk stratum level resulted in an increased risk of death (hazard ratio (HR) 2.53, 95% confidential interval (CI) 1.54‒4.18, P<0.001), which was independent of age, sex, estimated glomerular filtration rate (eGFR), hemoglobin, albumin, and valve type (HR 1.76, 95% CI 1.01‒3.07, P=0.047). Similar results were observed at 30 d after TAVR. COMPERA 2.0 can serve as a useful tool for risk stratification in patients with SAS undergoing TAVR, indicating its potential application in the management of PH-LHD. Further validation is needed in patients with confirmed post-capillary PH by right heart catheterization.
Humans ; Aortic Valve Stenosis/complications* ; Aged ; Hypertension, Pulmonary/mortality* ; Male ; Female ; Transcatheter Aortic Valve Replacement ; Aged, 80 and over ; Risk Assessment/methods* ; Proportional Hazards Models ; Kaplan-Meier Estimate ; Retrospective Studies

OBJECTIVES: We propose a multi-feature fusion model based on manually extracted features and deep learning features from endoscopic images for grading rebleeding risk of peptic ulcers. METHODS: Based on the endoscopic appearance of peptic ulcers, color features were extracted to distinguish active bleeding (Forrest I) from non-bleeding ulcers (Forrest II and III). The edge and texture features were used to describe the morphology and appearance of the ulcers in different grades. By integrating deep features extracted from a deep learning network with manually extracted visual features, a multi-feature representation of endoscopic images was created to predict the risk of rebleeding of peptic ulcers. RESULTS: In a dataset consisting of 3573 images from 708 patients with Forrest classification, the proposed multi-feature fusion model achieved an accuracy of 74.94% in the 6-level rebleeding risk classification task, outperforming the experienced physicians who had a classification accuracy of 59.9% (P<0.05). The F1 scores of the model for identifying Forrest Ib, IIa, and III ulcers were 90.16%, 75.44%, and 77.13%, respectively, demonstrating particularly good performance of the model for Forrest Ib ulcers. Compared with the first model for peptic ulcer rebleeding classification, the proposed model had improved F1 scores by 5.8%. In the simplified 3-level risk (high-risk, low-risk, and non-endoscopic treatment) classification task, the model achieved F1 scores of 93.74%, 81.30%, and 73.59%, respectively. CONCLUSIONS The proposed multi-feature fusion model integrating deep features from CNNs with manually extracted visual features effectively improves the accuracy of rebleeding risk classification for peptic ulcers, thus providing an efficient diagnostic tool for clinical assessment of rebleeding risks of peptic ulcers.
Humans ; Deep Learning ; Peptic Ulcer ; Risk Assessment ; Peptic Ulcer Hemorrhage ; Recurrence

OBJECTIVES: To develop an early atrial fibrillation (AF) risk prediction model based on large-scale electrocardiogram (ECG) data from the Chinese population. METHODS: The data of multiple ECG records of 30 383 patients admitted in the Chinese PLA General Hospital between 2009 and 2023 were randomly divided into the training set and the internal testing set in a 7:3 ratio. The predictive factors were selected based on the training set using univariate analysis, LASSO regression, and the Boruta algorithm. Cox proportional hazards regression was used to establish the ECG model and the composite model incorporating age, gender, and ECG model score. The discrimination power, calibration, and clinical net benefits of the models were evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curves. RESULTS: The cohort included 51.1% male patients with a median age of the patients of 51 (36, 62) years and an AF incidence of 4.5% (1370/30 383). In the ECG model, the parameters related to the P wave and QRS complex were identified as significant predictors. In the testing set, the AUROC of the ECG model for predicting 5-year AF risk was 0.77 (95% CI: 0.74-0.80), which was increased to 0.81 (95% CI: 0.78-0.83) after incorporating age and gender, with a net reclassification improvement of 0.123 and an integrated discrimination improvement of 0.04 (P<0.05). The calibration curve of the model was close to the diagonal line. Decision curve analysis showed that the clinical net benefit of the composite model was higher than that of the ECG model across the majority of threshold probability. CONCLUSIONS The composite model incorporating quantitative ECG features during sinus rhythm, along with age and gender, can effectively predict AF risk in the Chinese population, thus providing a low-cost screening tool for early AF risk assessment and management.
Humans ; Atrial Fibrillation/epidemiology* ; Electrocardiography ; Middle Aged ; Male ; Female ; China/epidemiology* ; Proportional Hazards Models ; Adult ; Risk Factors ; Risk Assessment ; East Asian People

OBJECTIVES: To develop a risk prediction model for left ventricular adverse remodeling (LVAR) based on cardiac magnetic resonance (CMR) parameters in patients undergoing percutaneous coronary intervention (PCI) for acute ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 329 acute STEMI patients undergoing primary PCI at 8 medical centers from January, 2018 to December, 2021 were prospectively enrolled. The parameters of CMR, performed at 7±2 days and 6 months post-PCI, were analyzed using CVI42 software. LVAR was defined as an increase >20% in left ventricular end-diastolic volume or >15% in left ventricular end-systolic volume at 6 months compared to baseline. The patients were randomized into training (n=230) and validation (n=99) sets in a 7∶3 ratio. In the training set, potential predictors were selected using LASSO regression, followed by univariate and multivariate logistic regression to construct a nomogram. Model performance was evaluated using receiver-operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis. RESULTS: LVAR occurred in 100 patients (30.40%), who had a higher incidence of major adverse cardiovascular events than those without LVAR (58.00% vs 16.16%, P<0.001). Left ventricular global longitudinal strain (LVGLS; OR=0.76, 95% CI: 0.61-0.95, P=0.015) and left atrial active strain (LAAS; OR=0.78, 95% CI: 0.67-0.92, P=0.003) were protective factors for LVAR, while infarct size (IS; OR=1.05, 95% CI: 1.01-1.10, P=0.017) and microvascular obstruction (MVO; OR=1.26, 95% CI: 1.01-1.59, P=0.048) were risk factors for LVAR. The nomogram had an AUC of 0.90 (95% CI: 0.86-0.94) in the training set and an AUC of 0.88 (95% CI: 0.81-0.94) in the validation set. CONCLUSIONS LVGLS, LAAS, IS, and MVO are independent predictors of LVAR in STEMI patients following PCI. The constructed nomogram has a strong predictive ability to provide assistance for management and early intervention of LVAR.
Humans ; Percutaneous Coronary Intervention ; Prospective Studies ; ST Elevation Myocardial Infarction/diagnostic imaging* ; Ventricular Remodeling ; Magnetic Resonance Imaging ; Male ; Female ; Middle Aged ; Risk Factors ; Aged ; Risk Assessment

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*

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*