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INTRODUCTION. Pharmacy compounding involves certain risks that are directly related to the process of extemporaneous preparation and the actions of its participants. However, the role of pharmacy cleaners, whose actions can influence the quality of compounded medicinal products, has not been studied yet.AIM. This study aimed to analyse the risks to the quality of compounded medicinal products associated with the functions of a pharmacy cleaner in pharmaceutical compounding of non-sterile dosage forms.MATERIALS AND METHODS. The study used failure modes and effects analysis (FMEA) in accordance with the relevant Russian State Standard, as well as open brainstorming discussion. The study examined the functions of a pharmacy cleaner in pharmaceutical compounding of non-sterile dosage forms. The results were assessed by statistically processing the study data and assigning each potential failure mode a certain category.RESULTS. The risk analysis identified 45 potential failure modes for the functions of a pharmacy cleaner. Of these, 12 failure modes (26.7%) had a high level of criticality, 14 failure modes (31.1%) had a medium level of criticality, and 19 failure modes (42.2%) had a low level of criticality. Not all potential failure modes had valid documented regulatory provisions for the risk assessment and the development of prevention-type controls.CONCLUSIONS. The analysis of the functions of a pharmacy cleaner highlighted the potential risks to the quality of extemporaneous medicinal products prepared by compounding pharmacies. The study identified a lack of valid regulatory documentation. The authors suggested using the study results to amend the regulatory documents and standard operating procedures (SOPs) guiding the work of a pharmacy cleaner, to develop illustrative educational materials, and to conduct on-the-job training for pharmacy staff.

Hazard and risk assessment of complex petroleum-derived substances has been in a state of continuous improvement since the 1970s, with the development of approaches that continue to be applied and refined. Alternative feeds are defined here as those coming into a refinery or chemical plant that are not hydrocarbons from oil and gas extraction such as biologically derived oils, pyrolysis oil from biomass or other, and recycled materials. These feeds are increasingly being used for production of liquid hydrocarbon streams, and hence, there is a need to assess these alternatives, subsequent manufacturing and refining processes and end products for potential risk to humans and the environment. Here we propose a tiered, problem formulation-driven framework for assessing the safety of hydrocarbon streams and products derived from alternative feedstocks in use. The scope of this work is only focused on petrochemical safety assessment, though the principles may be applicable to other chemistries. The framework integrates combinations of analytical chemistry, in silico and in vitro tools, and targeted testing together with conservative assumptions/approaches to leverage existing health, environmental, and exposure data, where applicable. The framework enables the identification of scenarios where de novo hazard and/or exposure assessments may be needed and incorporates tiered approaches to do so. It can be applied to enable decisions efficiently and transparently and can encompass a wide range of compositional space in both feedstocks and finished products, with the objective of ensuring safety in manufacturing and use. [ABSTRACT FROM AUTHOR]

The paper examines the issue of risk management in critical infrastructure facilities, highlighting the analysis of existing approaches and methods used to assess negative threats. Particular attention is paid to the importance of using information technologies for data collection and processing, as well as to the complexity of developing a systematic risk management methodology. Tracking activity in social networks, analyzing browser history and other data allows you to analyze and track a person’s psychological and physical state. Having access to information about interests and personal issues gives attackers the opportunity to develop various types of influence, including pressure, blackmail and manipulation. [ABSTRACT FROM AUTHOR]

Every work environment contains different types of risks and interactions between risks. Therefore, the method to be used when making a risk assessment is very important. When determining which risk assessment method (RAM) to use, there are many factors such as the types of risks in the work environment, the interactions of these risks with each other, and their distance from the employees. Although there are many RAMs available, there is no RAM that will suit all workplaces and which method to choose is the biggest question. There is no internationally accepted scale or trend on this subject. In the study, 26 sectors, 10 different RAMs and 10 criteria were determined. A hybrid approach has been designed to determine the most suitable RAMs for sectors by using k-means clustering and support vector machine (SVM) classification algorithms, which are machine learning (ML) algorithms. First, the data set was divided into subsets with the k-means algorithm. Then, the SVM algorithm was run on all subsets with different characteristics. Finally, the results of all subsets were combined to obtain the result of the entire dataset. Thus, instead of the threshold value determined for a single and large cluster affecting the entire cluster and being made mandatory for all of them, a flexible structure was created by determining separate threshold values for each sub-cluster according to their characteristics. In this way, machine support was provided by selecting the most suitable RAMs for the sectors and eliminating the administrative and software problems in the selection phase from the manpower. The first comparison result of the proposed method was found to be the hybrid method: 96.63%, k-means: 90.63 and SVM: 94.68%. In the second comparison made with five different ML algorithms, the results of the artificial neural networks (ANN): 87.44%, naive bayes (NB): 91.29%, decision trees (DT): 89.25%, random forest (RF): 81.23% and k-nearest neighbours (KNN): 85.43% were found. [ABSTRACT FROM AUTHOR]

Chemical industries are among the process industries and cause many risks. The present research aimed to analyze the health risks of a chemical warehouse of drilling rigs chemical Supply Company based on COSHH (Control of Substances Hazardous to Health), CHEM-SAM (Chemical Risk Management Self-Assessment Model), and SQRA (Subjective Quantified Risk Assessment) methods in 2021. The information was collected based on document review, MSDS of chemicals, processes, employees, and chemical exposure. Flammability, toxicity, allergy-causing, corrosivity, reactivity, LD50, and permissible thresholds of chemicals were also collected. The present research results showed that out of 59 main chemicals in the operational processes of the chemical warehouse of drilling rigs, 14 chemicals are flammable, 22 cause allergy responses, and three can cause death if inhaled. According to the results of the CHEM-SAM method, the employees and people outside the organization are at medium and low chemical risk based on the current management of the chemical warehouse, respectively. The results of the COSHH health assessment showed that chemicals had low, medium, high, and very high risk in 31, 13, 12, and 3 cases, respectively. The high-risk cases consisted of Ammonium Nitrate، Caustic Soda, and Poly.Aluminum.Chloride. Health risk assessment was also performed using the SQRA method, and results showed that chemicals have a very low, low, medium, high, and very high risk in 27, 12, 8, 9, and 3 cases, respectively. The results showed that the adverse health effects of chemical exposure in the drilling industry are alarming. Employees of different sectors of oil and gas industries are exposed to chemicals.

Every work environment contains different types of risks and interactions between risks. Therefore, the method to be used when making a risk assessment is very important. When determining which risk assessment method (RAM) to use, there are many factors such as the types of risks in the work environment, the interactions of these risks with each other, and their distance from the employees. Although there are many RAMs available, there is no RAM that will suit all workplaces and which method to choose is the biggest question. There is no internationally accepted scale or trend on this subject. In the study, 26 sectors, 10 different RAMs and 10 criteria were determined. A hybrid approach has been designed to determine the most suitable RAMs for sectors by using k-means clustering and support vector machine (SVM) classification algorithms, which are machine learning (ML) algorithms. First, the data set was divided into subsets with the k-means algorithm. Then, the SVM algorithm was run on all subsets with different characteristics. Finally, the results of all subsets were combined to obtain the result of the entire dataset. Thus, instead of the threshold value determined for a single and large cluster affecting the entire cluster and being made mandatory for all of them, a flexible structure was created by determining separate threshold values for each sub-cluster according to their characteristics. In this way, machine support was provided by selecting the most suitable RAMs for the sectors and eliminating the administrative and software problems in the selection phase from the manpower. The first comparison result of the proposed method was found to be the hybrid method: 96.63%, k-means: 90.63 and SVM: 94.68%. In the second comparison made with five different ML algorithms, the results of the artificial neural networks (ANN): 87.44%, naive bayes (NB): 91.29%, decision trees (DT): 89.25%, random forest (RF): 81.23% and k-nearest neighbours (KNN): 85.43% were found.

Summary: The management of pregnant women with thrombophilia and a history of gestational vascular complications remains debatable. Treatment of the latter is often based on clinical outcome rather than disease mechanism. While the use of venous thromboembolism prophylaxis in pregnancy is recommended for those at increased risk, the ability of anticoagulant and/or antiplatelet agents to lower the risk of placenta‐mediated complications in this clinical setting remains controversial. The available guidelines are inconsistent in some situations, which reflects the limited evidence base. This review critically discusses risk assessment models (RAMs) and management strategies of women with thrombophilia and pregnancy complications, using clinical vignettes. RAMs, taking into account obstetric and thrombotic history as well as thrombophilia status, could drive a precision medicine approach, based on disease mechanism, and guide individual therapeutic interventions in high‐risk clinical settings. [ABSTRACT FROM AUTHOR]

Leading risk assessment standards such as the NIST SP 800-39 and ISO 27005 state that information security risk assessment (ISRA) is one of the crucial stages in the risk-management process. It pinpoints current weaknesses and potential risks, the likelihood of their materializing, and their potential impact on the functionality of critical information systems such as advanced metering infrastructure (AMI). If the current security controls are insufficient, risk assessment helps with applying countermeasures and choosing risk-mitigation strategies to decrease the risk to a controllable level. Although studies have been conducted on risk assessment for AMI and smart grids, the scientific foundations for selecting and using an appropriate method are lacking, negatively impacting the credibility of the results. The main contribution of this work is identifying an appropriate ISRA method for AMI by aligning the risk assessment criteria for AMI systems with the ISRA methodologies' characteristics. Consequently, this work makes three main contributions. First, it presents a comprehensive comparison of multiple ISRA methods, including OCTAVE Allegro (OA), CORAS, COBRA, and FAIR, based on a variety of input requirements, tool features, and the type of risk assessment method. Second, it explores the necessary conditions for carrying out a risk assessment for an AMI system. Third, these AMI risk assessment prerequisites are aligned with the capabilities of multiple ISRA approaches to identify the best ISRA method for AMI systems. The OA method is found to be the best-suited risk assessment method for AMI, and this outcome paves the way to standardizing this method for AMI risk assessment. [ABSTRACT FROM AUTHOR]

Introduction: The introduction of invasive fish species into aquatic ecosystems causes various adverse ecological and socio-economic impacts. The first step in analyzing the effects of these species is to identify the risk. Then, different tools have been developed to identify potential invasive species and evaluate the potential degree of their invasiveness to support decision-makers in analyzing the invasive risk of these species. This study aimed to assess the invasive potential of the non-native species redbelly tilapia (Coptodon zillii, Gervais 1848) in the Shadegan Wetland basin (the Karun and Jarahi catchments) using some of these tools.Material and methods: The level of risk for C. zillii in the trinational risk assessment protocol was determined based on the results of the two components "Probability of Establishment" and "Consequences of Establishment". In the German-Austrian Blacklist Information System (GABLIS), the invasive potential of the species was evaluated according to the distribution in the study area. The non-native species were screened by the Aquatic Species Invasiveness Screening Kit (AS-ISK) model according to the threshold of the assessment area; and the rank of species invasion was calculated based on the probability of species establishment, expansion, and environmental effects, using the Harmonia+ method. Also, the climate matching between the introduced and native range of the species was carried out with the Köppen-Geiger climate classification system.Results and discussion: The results of trinational risk assessment indicated that the species posed a high potential rank of placing at each step of introduction, establishment, and expansion potential and the possibility of economic and environmental impacts in the study area. The results of the GABLIS protocol showed that the non-native species C. zillii has been widely distributed in the risk assessment area and was placed on the blacklist and the subset management list (b3). In the AS-ISK risk assessment, the risk score of the species was 44, which was higher than the tool threshold (22.5) for the study area. This score indicates that this species has a high invasive risk in the wetland. The overall risk score in the Harmonia+ method, which is a function of invasiveness and species impacts, was assessed as high for the redbelly tilapia. Based on the results, the risk of establishment and dispersal of this species in the study area is high, and its environmental impacts are significant. Also, there was a high climate match between the risk assessment area and the native range of the species in the Köppen-Geiger climate classification system. Conclusions: The trinational risk assessment methods, GABLIS, AS-ISK, and Harmonia+ models were able to show the invasiveness of the non-native C. zillii in Shadegan Wetland basin as literature and field evidence demonstrate that the species has exerted strong and adverse impacts on native fishes and local people livelihood in the risk assessment area. Given the results of risk assessment methods and the risks posed by this species, it is highly recommended that large-scale control and management measures should be seriously implemented.

The purpose of this study is to examine the purpose of this study is to examine the features of managing financial institutions and risks under conditions of uncertainty. The current period of development of the financial sphere is characterized by availability of a significant number of approaches and methods of management through forecasting and assessing risks, their identifying and minimizing. Analysis of recent research and publications has shown that the use of innovative mathematical management methods can minimize risks and provide significant competitive advantages in the future. Methodology: general and special methods of system-structural analysis and synthesis, grouping and comparison were used in the research process. The risk management scheme proposed by the NBU for use by financial institutions has been analyzed and supplemented by taking into account reputational risks. The research findings show that the existing methods are based on the use of expert judgment and require a significant amount of information that is not always available. It has been proven that the construction, use, calibration and interpretation of results obtained during risk assessment should be handled by a risk committee, whose main task is to monitor, identify and manage risks that arise during the operation of the financial institution. It has been revealed that there is no single complete Bureau of Credit Histories in Ukraine, which is a significant barrier to the effective management of financial institutions. The prospect for further research is the formation of approaches that would allow managing several types of risks, in compliance with all regulations under current legislation. This will make it possible to fully satisfy customers, while the setup of a single bureau of credit histories will minimize risk manifestations.

Objective: This study aimed to systematically evaluate published predictive models for dental caries in children and adolescents., Design: A systematic review and meta-analysis of observational studies., Data Sources: Comprehensive searches were conducted in PubMed, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Embase, China National Knowledge Infrastructure, Wanfang Database, China Science and Technology Journal Database (VIP) and SinoMed for relevant studies published up to 18 January 2024. The search focused on caries prediction models in children and adolescents., Eligibility Criteria: Eligible studies included observational research (cohort, case-control and cross-sectional designs) that developed risk prediction models for dental caries in children and adolescents aged ≤18 years. Each model was required to include a minimum of two predictors. Studies were excluded if they were not available in English or Chinese, primarily focused on oral microbiome modelling, or lacked essential details regarding study design, model construction or statistical analyses., Results: A total of 11 studies were included in the review. All models demonstrated a high risk of bias, primarily due to inappropriate statistical methods and unclear applicability resulting from insufficiently detailed presentations of the models. Logistic regression, random forests and support vector machines were the most commonly employed methods. Frequently used predictors included fluoride toothpaste use and brushing frequency. Reported area under the curve (AUC) values ranged from 0.57 to 0.91. A combined predictive model incorporating six caries predictors achieved an AUC of 0.79 (95% CI: 0.73 to 0.84)., Conclusions: Simplified predictive models for childhood caries showed moderate discriminatory performance but exhibited a high risk of bias, as assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Future research should adhere to PROBAST guidelines to minimise bias risk, focus on enhancing model quality, employ rigorous study designs and prioritise external validation to ensure reliable and generalisable clinical predictions., Prospero Registration Number: CRD42024523284., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Background: The aim of this study was to develop an accurate and clinically relevant prediction model for 30-day mortality following hip fracture surgery., Methods: A previous study protocol was utilized as a guideline for data collection and as the standard for the hip fracture treatment. Two prospective, detailed hip fracture databases of 2 different hospitals (hospital A, training cohort; hospital B, testing cohort) were utilized to obtain data. On the basis of the literature, the results of a univariable analysis, and expert opinion, 26 candidate predictors of 30-day mortality were selected. Subsequently, the training of the model, including variable selection, was performed on the training cohort (hospital A) with use of adaptive least absolute shrinkage and selection operator (LASSO) logistic regression. External validation was performed on the testing cohort (hospital B)., Results: A total of 3,523 patients were analyzed, of whom 302 (8.6%) died within 30 days after surgery. After the LASSO analysis, 7 of the 26 variables were included in the prediction model: age, gender, an American Society of Anesthesiologists score of 4, dementia, albumin level, Katz Index of Independence in Activities of Daily Living total score, and residence in a nursing home. The area under the receiver operating characteristic curve of the prediction model was 0.789 in the training cohort and 0.775 in the testing cohort. The calibration curve showed good consistency between observed and predicted 30-day mortality., Conclusions: The Rotterdam Hip Fracture Mortality Prediction-30 Days (RHMP-30) was developed and externally validated, and showed adequate performance in predicting 30-day mortality following hip fracture surgery. The RHMP-30 will be helpful for shared decision-making with patients regarding hip fracture treatment., Level of Evidence: Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence., Competing Interests: Disclosure: No external funding was received for this work. The Article Processing Charge for open access publication was funded by the Maasstad Hospital Science Board. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article ( http://links.lww.com/JBJS/I372 )., (Copyright © 2025 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated.)

Objective: The study investigates the impact of age at initial diagnosis on the prognosis of pediatric hepatoblastoma (HB) patients, aiming to provide a basis for optimizing risk stratification., Methods: Data from 403 patients with HB diagnosed at the First Affiliated Hospital of Sun Yat-sen University between February 2010 and September 2023 were collected. Kaplan‒Meier survival analysis, Cox regression analysis, and binary logistic regression were employed for statistical analysis., Results: The hazard ratios (HRs) for event-free survival (EFS) reduction in HB patients were 1, 0.862, 1.393, 2.008, 1.325, 1.859, 3.667 (P = 0.001), and 2.502 (P = 0.023) for first-diagnosis ages of 0-1, 1-2, 2-3, 3-4, 4-5, 5-6, 6-8, and ≥ 8 years, respectively. After adjusting for newly diagnosed alpha-fetoprotein (AFP), pretreatment extent of tumor (PRETEXT) stage, and PRETEXT stage annotation factors, the prognosis of HB for patients diagnosed at ≥ 6 years old and < 6 years old remained significantly different. Children diagnosed at age ≥ 6 years who received the full-course high-risk group chemotherapy regimen had a higher EFS compared to those who did not receive the full-course high-risk group chemotherapy regimen (P = 0.033)., Conclusion: Age ≥ 6 years is an independent risk factor for poor prognosis in HB patients. The inclusion of patients aged ≥ 6 years at first diagnosis in the high-risk group for risk stratification was deemed appropriate. This age factor can guide adjustments in chemotherapy intensity., Clinical Trial Number: Not applicable., Competing Interests: Declarations. Ethical approval: This study follows the Declaration of Helsinki. This single-center retrospective study was approved by the First Affiliated Hospital of Sun Yat-sen University (No. 2024[242]). This study was a retrospective study, and the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University applied for exemption of informed consent and was approved. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, with the aim of improving early detection and survival rates. However, no comprehensive review or meta-analysis has assessed the performance of these models across different sociocultural contexts. Therefore, this review systematically examines the performance of lung cancer risk prediction models in Western and Asian populations. PubMed and EMBASE were searched from inception through January 2023. Studies published in English that proposed a validated model on human populations with well-defined predictive performances were included. Two reviewers independently screened the titles and abstracts, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess study quality. A random-effects meta-analysis was performed, and a 95% confidence interval (CI) for model performance was reported. Between-study heterogeneity was adjusted for using the Hartung-Knapp-Sidik-Honkman test. A total of 54 studies were included, with 42 from Western countries and 12 from Asian countries. Most Western studies focused on ever-smokers (19/42; 45.2%) and the general population (17/42; 40.5%), and only two Asian studies developed models exclusively for never-smokers. Across both Western and Asian prediction models, the three most consistently included risk factors were age, sex, and family cancer history. In 45.2% (19/42) of Western and 50.0% (6/12) of Asian studies, models incorporated both traditional risk factors and biomarkers. In addition, 14.8% (8/54) of the studies directly compared biomarker-based models with those incorporating only traditional risk factors, demonstrating improved discrimination. Machine-learning algorithms were applied in eight Western models and two Asian models. External validation of PLCO M2012 (AUC = 0.748; 95% CI: 0.719-0.777) outperformed other prediction models, such as Bach (AUC = 0.710; 95% CI: 0.674-0.745) and Spitz models (AUC = 0.698; 95% CI: 0.640-0.755). Despite showing promising results, the majority of Asian risk models in our study lack external validation. Our review also highlights a significant gap in prediction models for never-smokers. Future research should focus on externally validating existing Asian models or incorporating relevant Asian risk factors into widely used Western models (PLCO M2012 ) to better account for unique risk profiles and lung cancer progression patterns in Asian populations., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Purpose: Frailty has gained recognition as a crucial determinant of patient outcomes following traumatic spinal injury (TSI), particularly due to its increasing incidence in elderly populations. The aim of the current investigation was therefore to compare the ability of several frailty scores to predict adverse outcomes in surgically managed isolated TSI patients without spinal cord injury., Methods: All adult patients (18 years or older) who suffered an isolated TSI due to blunt trauma, and required surgical management, were extracted from the 2013-2021 Trauma Quality Improvement Program database. The ability of the Orthopedic Frailty Score (OFS), the Hospital Frailty Risk Score (HFRS), the 11-factor (11-mFI) and 5-factor (5-mFI) modified frailty index, as well as the Johns Hopkins Frailty Indicator to predict adverse outcomes was compared based on the area under the receiver-operating characteristic curve (AUC). Subgroup analyses were also performed on patients who were ≥ 65 years old and those who were injured due to a ground-level fall (GLF)., Results: A total of 39,449 patients were selected from the TQIP database. The 5-mFI and 11-mFI outperformed all other frailty scores when predicting in-hospital mortality (5-mFI AUC: 0.73) (11-mFI AUC: 0.73), any complication (5-mFI AUC: 0.65) (11-mFI AUC: 0.65), and FTR (5-mFI AUC: 0.75) (11-mFI AUC: 0.75). Among the 14,257 geriatric patients, however, the OFS demonstrated the highest predictive ability for in-hospital mortality (AUC: 0.65). The OFS (AUC: 0.64) also performed on the same level as both the 5-mFI (AUC: 0.63) and the 11-mFI (AUC: 0.63) when predicting FTR in this population. Among the 9616 patients who were injured due to a GLF, the OFS performed on par with the 5-mFI and 11-mFI when predicting in-hospital mortality and FTR., Conclusion: Simpler scores like the 5-factor modified Frailty Index and Orthopedic Frailty Score outperform or perform on par with more complicated frailty scores when predicting mortality, complications, and failure-to-rescue in surgically managed isolated traumatic spinal injury patients without spinal cord injury, particularly among geriatric patients and those injured in a GLF., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Congress presentation: The abstract of this paper has been accepted for presentation at the 24th European Congress of Trauma and Emergency Surgery in Aachen, Germany, between 13 and 15 April 2025., (© 2025. The Author(s).)

Background: Thoracic endovascular aortic repair (TEVAR) and complex endovascular aneurysm repair (EVAR) are complex procedures that carry a significant risk of complications. While risk prediction tools can aid in clinical decision making, they remain limited. We developed machine learning algorithms to predict outcomes following TEVAR and complex EVAR., Methods: The Vascular Quality Initiative database was used to identify patients who underwent elective TEVAR and complex EVAR for noninfrarenal aortic aneurysms between 2012 and 2023. We extracted 172 features from the index hospitalization, including 93 preoperative (demographic/clinical), 46 intraoperative (procedural), and 33 postoperative (in-hospital course/complications) variables. The primary outcome was 1-year thoracoabdominal aortic aneurysm life-altering event, defined as new permanent dialysis, new permanent paralysis, stroke, or death. The data were split into training (70%) and test (30%) sets. We trained 6 machine learning models using preoperative features with 10-fold cross-validation. Model robustness was evaluated using calibration plots and Brier scores., Results: Overall, 10 738 patients underwent TEVAR or complex EVAR, with 1485 (13.8%) experiencing 1-year thoracoabdominal aortic aneurysm life-altering event. Extreme Gradient Boosting was the best preoperative prediction model, achieving an area under the receiver operating characteristic curve of 0.96 (95% CI, 0.95-0.97), compared with 0.70 (95% CI, 0.68-0.72) for logistic regression. The Extreme Gradient Boosting model maintained excellent performance at the intra- and postoperative stages, with areas under the receiver operating characteristic curves of 0.97 (95% CI, 0.96-0.98) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots indicated good agreement between predicted/observed event probabilities, with Brier scores of 0.09 (preoperative), 0.08 (intraoperative), and 0.05 (postoperative)., Conclusions: Machine learning models can accurately predict 1-year outcomes following TEVAR and complex EVAR, performing better than logistic regression.

Background: Obesity, measured by body mass index, is a risk factor for cardiovascular disease. However, the role of body composition, including body fat percentage and lean body mass (LBM), in cardiovascular outcomes has not been well studied in patients with coronary artery disease (CAD). This study aims to evaluate the association of body composition with cardiovascular outcomes and all-cause mortality in patients with CAD., Methods and Results: Body composition was obtained via bioelectrical impedance analysis from 1291 patients with CAD before starting cardiac rehabilitation. Patients were divided into quintiles by body composition and analyzed in total and after sex stratification. All-cause mortality and a composite of major adverse cardiovascular events, including acute coronary syndrome, coronary revascularization, heart failure hospitalization, and stroke, were primary study outcomes. In the total cohort adjusted analyses, body mass index, body fat percentage, and LBM were not predictors of all-cause mortality or major adverse cardiovascular events. In sex-stratified analyses, among women, the third LBM quintile was associated with decreased risk of all-cause mortality compared with the lowest LBM quintile (adjusted hazard ratio, 0.07 [95% CI, 0.01-0.57]; P =0.01). No other body composition variables were associated with all-cause mortality or major adverse cardiovascular events in either sex., Conclusions: In women with CAD, moderate LBM was associated with lower mortality when compared with low LBM, whereas body fat percentage and body mass index were not associated with mortality or major adverse cardiovascular events in either sex. Future research studying the implications of changes in body composition on outcomes in men and women with CAD is warranted.

Background: Systemic sclerosis (SSc) primary heart involvement (SSc-pHI) is one of the leading causes of mortality in SSc. We aimed to evaluate risk factors for SSc-pHI and its progression and the outcomes in the EUSTAR (European Scleroderma Trials and Research) cohort., Methods: SSc-pHI was defined according to the World Scleroderma Foundation/Heart Failure Association definition. Data from 5741 patients with SSc in the EUSTAR cohort were analyzed. Additional cardiovascular data were collected from a subcohort of 838 patients with SSc. Lasso regression was used for risk factor analyses. Kaplan-Meier estimator was used for survival analyses. Progression of SSc-pHI was evaluated by a study definition developed by rheumatology and cardiology experts., Results: Risk factors for the presence of SSc-pHI comprised skeletal muscle atrophy (odds ratio [OR], 2.00 [95% CI, 1.00-2.68]), age (OR, 1.91 [95% CI, 1.73-2.03]), male sex (OR, 1.77 [95% CI, 1.42-2.05]), swollen joints (OR, 1.70 [95% CI, 1.47-1.98]), skeletal muscle weakness (OR, 1.38 [95% CI, 1.00-1.85]), and tendon friction rubs (OR, 1.46 [95% CI, 1.00-1.77]) (n=3276). Telangiectasia (OR, 2.10 [95% CI, 1.38-2.72]), intestinal symptoms (OR, 1.70 [95% CI, 1.04-2.42]), age (OR, 1.47 [95% CI, 1.21-1.62]), and antitopoisomerase I antibodies (OR, 1.37 [95% CI, 1.00-1.77]) were associated with an increased risk for new onset of SSc-pHI (n=1000). Survival rate was significantly lower in patients with SSc-pHI than in those without ( P value <0.0001, n=3768). Patients with SSc-pHI had a lower survival rate than patients with interstitial lung disease (n=3365). Swollen joints were associated with an increased risk of progressive SSc-pHI (OR, 2.49 [95% CI, 1.79-3.52]) (n=595). Tendon friction rubs (OR, 1.21 [95% CI, 0.94-1.90]) increased the risk of heart failure with preserved ejection fraction in patients with SSc-pHI., Conclusions: We defined progressive SSc-pHI and identified risk factors for new onset and progression of SSc-pHI and for SSc-pHI-associated heart failure with preserved ejection fraction in the largest cohort with SSc. These findings may guide patient stratification for diagnostic workup and therapy.

Background: Heart failure (HF) constitutes the source of various damaging pathophysiological mechanisms in acute ischemic stroke (AIS). The effect of a previous HF on the in-hospital outcomes for patients with AIS still lacks effective research. We aimed to evaluate the severity of stroke, in-hospital mortality, major adverse cardiovascular events, and complications associated with a previous HF in these patients., Methods and Results: This cross-sectional study was conducted at 1476 hospitals in the Chinese Stroke Center Alliance. Multivariable logistic regression and propensity score-matched analyses were used to evaluate the association between a history of HF and in-hospital outcomes. Of 836 885 patients with AIS, 1.1% (n=8950) patients had a history of HF. Patients with a history of HF had a higher National Institutes of Health Stroke Scale score at admission (6.0 versus 3.0) than those without a history of HF. Multivariable analysis revealed that a history of HF was associated with an 80% higher risk of all-cause mortality (odds ratio [OR], 1.80 [95% CI, 1.54-2.10]), a 34% higher risk of major adverse cardiovascular events (OR, 1.34 [95% CI, 1.26-1.43]), and a 92% higher risk of complications (OR, 1.92 [95% CI, 1.83-2.02]). Further propensity score matching showed that patients with a history of HF had higher risks of adverse in-hospital outcomes (all-cause mortality: OR, 1.62 [95% CI, 1.30-2.02]; major adverse cardiovascular events: OR, 1.39 [95% CI, 1.26-1.53]; complications: OR, 1.70 [95% CI, 1.58-1.82])., Conclusions: Patients with AIS and a history of HF have increased risks of severe stroke, in-hospital mortality, major adverse cardiovascular events, and complications. Systematic cardiovascular evaluation and integrated multidisciplinary care for patients with AIS in clinical practice are warranted.

Background: Cardiac fibrosis is common in patients with severe aortic stenosis and an independent predictor of death. Therefore, we examined the additional value of circulating fibrosis markers as a putative biomarker platform to identify patients with aortic stenosis undergoing transcatheter aortic valve replacement (TAVR) who are at a higher risk of death., Methods: In this study, 2-year survival analyses were conducted in 378 consecutive patients undergoing TAVR to evaluate the association between fibrosis marker and risk of adverse long-term outcome. Implementation of fibrosis marker into TAVR risk stratification was tested by a machine-learning algorithm., Results: Among 20 circulating fibrosis markers involved in pathological extracellular matrix remodeling, high tissue inhibitor of metalloproteinase-1 (TIMP-1) levels independently predicted risk of death in univariable (hazard ratio, 5.0 [95% CI, 2.6-9.7]; P <0.001) and multivariable (adjusted hazard ratio, 2.2 [95% CI, 1.0-4.7]; P =0.046) Cox regression analyses. Consequently, higher TIMP-1 levels offered a significantly higher overall prediction of reduced survival compared with the conventional Society of Thoracic Surgeons Predicted Risk of Mortality score (area under the curve, 0.753 [95% CI, 0.682-0.824] versus area under the curve, 0.656 [95% CI, 0.578-0.734]; P <0.05). Applying an independent machine-learning algorithm allowed identification of a simple combination of 2 biomarkers (TIMP-1 and high-sensitivity cardiac troponin T) with superior prognostic value compared with Society of Thoracic Surgeons Predicted Risk of Mortality alone (area under the curve, 0.757 [95% CI, 0.686-0.828] versus 0.656 [95% CI, 0.578-0.34]; P <0.05)., Conclusions: Circulating TIMP-1 is an independent predictor of reduced 2-year overall survival in patients undergoing TAVR. Combined with high-sensitivity cardiac troponin T, circulating TIMP-1 should be incorporated into risk stratification to identify patients undergoing TAVR who are at a higher risk of death.

Background: We tested whether biologic age, as estimated by deficits, functional impairments, or Age-Gap or their combination, provide improved estimation of cause-specific death as compared with chronological age., Methods: Cardiovascular and noncardiovascular deficits, functional impairments, and Age-Gap were prospectively collected in 535 patients aged ≥55 years undergoing percutaneous coronary interventions between August 1, 2014, and March 31, 2018. Age-Gap was calculated as the difference between chronological age and age estimated by artificial intelligence ECG using a convolutional neural network. The full biological age model included deficits, functional impairments, and Age-Gap >2 SD. A multivariable reduced model with the least number of variables was also created to provide a comparable C index to the full model., Results: The average chronological age was 72.1±9.5 years, and there were 68% of men. During a median follow-up of 2.61 years, 124 (23%) patients died. There was a modest correlation between Age-Gap and biological age ( r =0.28 [95% CI, 0.20-0.35]; P <0.001). When modeled with chronologic age as a covariate, Age-Gap predicted all-cause (hazard ratio [HR], 1.07 [95% CI, 1.04-1.10]; P <0.001) and cardiovascular (HR, 1.07 [95% CI, 1.04-1.11]; P <0.001) mortality. As compared with chronological age, the full biological age model noted significant improvement in the prediction of long-term overall (95% CI, 0.65-0.78), cardiovascular (95% CI, 0.69-0.77), and noncardiovascular (95% CI, 0.55-0.86) mortality. In the reduced models, most prognostic information for noncardiovascular mortality (C index: 0.79) was obtained by subjective difficulty in performing tasks, whereas the deficit-based estimation predicted cardiovascular mortality (C index: 0.72)., Conclusions: Estimated biological age from deficits and functional impairments was superior to chronological age in predicting long-term cause-specific mortality following percutaneous coronary interventions.

Background: Heart failure (HF) has a major impact on public health. HF staging helps capture preclinical disease and its progression to advanced stages. There are scarce data on HF staging from longitudinal studies in Latin America. This study aimed to determine the prevalence and mortality of HF stages in a Brazilian adult cohort of participants 60 years old and over., Methods: The ELSA-Brasil (Brazilian Longitudinal Study of Adult Health) multicentric cohort comprises 15 105 adults. From 2008 to 2010, ELSA carried out interviews and clinical, laboratory, and cardiovascular tests. In this analysis, we included participants 60 years old and over who had an echocardiogram performed at baseline visit and additional information regarding risk factors and functional capacity., Results: There were 2356 participants (65±4 years; 53% women) with baseline echocardiogram. Of these, 504 (21%) participants were considered at low risk for developing HF (stage 0). Prevalence of HF was 1026 (44%) for stage A (at risk for HF), 557 (24%) for stage B (pre-HF), and 269 (11%) for stage C (symptomatic HF), with different distribution between sexes ( P <0.001). Multivariable risk-adjusted model for all-cause mortality over a median follow-up of 12 years, found 6%, 12%, 18%, and 27% for stages 0, A, B, and C, respectively, with hazard ratios of 1.82 [95% CI, 1.2-2.7], 2.52 [95% CI, 1.7-3.8], and 4.29 [95% CI, 2.8-6.6], using as reference stage 0., Conclusions: The high prevalence of symptomatic, and preclinical HF in older adults and the increased mortality with disease progression may be responsible for an elevated public health burden. This information is critical for understanding and planning health policies for older adults in middle-income countries.

Background: Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in cardiac intensive care units., Methods: This historical cohort study included consecutive adults admitted to Mayo Clinic cardiac intensive care unit from 2007 to 2018 with an admission AIECG. The AIECG assigned the LVDD grade (0-3). Medial mitral E/e' ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. In-hospital and 1-year mortality was evaluated, before and after multivariable adjustment., Results: We included 11 868 patients (median age 69.5 years, 37.7% female); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grade was 0 (normal), 33%; 1, 7%; 2, 39%; and 3, 21%. In-hospital and 1-year mortality increased in each higher AIECG LVDD grade. After adjustment, each higher AIECG LVDD grade was associated with higher in-hospital (adjusted odds ratio [OR], 1.22 [95% CI, 1.13-1.32]) and 1-year mortality (adjusted hazard ratio [HR], 1.23 [95% CI, 1.19-1.29]); this persisted after adjustment for TTE measurements. Patients with grade 2 or 3 LVDD by AIECG and medial mitral E/e' ratio >15 by TTE had the highest in-hospital (adjusted OR, 2.54 [95% CI, 1.69-3.88]) and 1-year (adjusted HR, 2.03 [95% CI, 1.65-2.48]) mortality, whereas patients meeting either of these criteria had similar, elevated mortality., Conclusions: The AIECG LVDD grade was strongly associated with in-hospital and 1-year mortality in patients in cardiac intensive care units, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures were at highest risk.

Background: Most data linking chronic stress with cardiovascular disease (CVD) risk factors and outcomes have focused on single-domain stress measurements. We evaluated the association between a novel composite measure of chronic perceived stress and CVD risk factors and outcomes in a diverse population., Methods and Results: Individual chronic stress subcomponents (generalized stress, psychosocial, financial, and neighborhood stress) were standardized and integrated to create a novel composite stress score (CSS). Participants from the DHS (Dallas Heart Study) phase 2 (2007-2009) visit without prevalent CVD who completed chronic stress questionnaires were included (n=2685). Associations between CSS and demographics, cardiac risk factors, and health behaviors were assessed in multivariable analyses. Cox proportional hazards models adjusting for traditional risk factors were used to determine associations of the CSS with adjudicated atherosclerotic CVD and global CVD (atherosclerotic CVD, heart failure, and atrial fibrillation) outcomes. CSS was higher among participants who were younger, women, and Black or Hispanic individuals, with lower income and educational attainment ( P <0.0001 for each). In multivariable regression models adjusting for age, sex, race and ethnicity, income and education, higher CSS associated with hypertension, smoking, higher body mass index, hemoglobin A 1C , high-sensitivity C-reactive protein, and sedentary time ( P <0.01 for each). Over a median follow-up of 12.4 years, higher CSS associated with atherosclerotic CVD (adjusted hazard ratio [HR]. 1.22 per SD [95% CI, 1.01-1.47]) and global CVD (adjusted HR, 1.20 [95% CI, 1.03-1.40]). No interactions were seen between CSS, demographic factors, and outcomes., Conclusions: Composite measures of chronic stress are higher in vulnerable populations and may help identify individuals at risk for CVD who may benefit from enhanced prevention strategies.

Background: Sex differences have not been fully explored for certain risk factors or by age or age-related factors, such as menopause. We addressed this issue in a large population cohort., Methods: UK Biobank participants with ≥1 risk factor measured at baseline were included. We assessed sex differences, by age and menopausal status, in prevalence, treatment and control of cardiometabolic risk factors., Results: 501 389 adults (54.4% women, mean age 56.6 (SD 8.1) years) were included. Mean risk factor levels that were lower in women than men include systolic blood pressure (women-to-men difference: -5.6 mm Hg), diastolic blood pressure (-3.4 mm Hg), body mass index (-0.75 kg/m 2 ), waist circumference (-12.2 cm), triglycerides (0.34 mmol/L), glycated haemoglobin (-0.52 mmol/mol) and glucose (-0.08 mmol/L), while high-density lipoprotein cholesterol (+0.31 mmol/L) and C reactive protein (+0.08 mg/L) were higher among women. Total cholesterol and low-density lipoprotein cholesterol (LDL-C) were lower in women than men at younger ages (-0.23 and -0.30 mmol/L, respectively, at <50 years), and higher at older ages (+0.74 and +0.41 mmol/L, at ≥60 years). Total cholesterol and LDL-C were lower in premenopausal women (-0.29 and -0.34 mmol/L, respectively) and higher in postmenopausal women (+0.61 and +0.31 mmol/L), compared with similarly aged men. Prevalence was lower among women than men for current smoking (-3.6%), hypertension (-13.9%), obesity (-1.9%) and diabetes (-2.0%), and sex differences were smaller at older ages and in postmenopausal women. Dyslipidaemia prevalence was lower in women aged <50 years (-8.8%) and premenopausal women (-11.0%), and higher in women aged ≥60 years (+5.4%) and postmenopausal women (+4.6%). Treatment and control of dyslipidaemia were lower in women than men (-12.5% and -12.6%, respectively)., Conclusions: Effective public health policy is required to address suboptimal risk factor prevalence, treatment and control in both sexes. Targeted interventions may be warranted to address dyslipidaemia among women at older ages., Competing Interests: Competing interests: MW has been a recent consultant to Freeline. All other authors have no relationships relevant to the contents of this paper to disclose., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Background: Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model to calculate the risk of mortality in patients with AP admitted in intensive care unit (ICU) and compared it with existing scoring systems., Methods: A gradient-boosting ML (XGBoost) model was developed and externally validated based on two public databases: Medical Information Mart for Intensive Care (MIMIC, training cohort) and the eICU Collaborative Research Database (eICU-CRD, validation cohort). We compared the performance of the XGBoost model with validated clinical risk scoring systems (the APACHE IV, SOFA, and Bedside Index for Severity in Acute Pancreatitis [BISAP]) by area under receiver operating characteristic curve (AUC) analysis. SHAP (SHapley Additive exPlanations) method was applied to provide the explanation behind the prediction outcome., Results: The XGBoost model performed better than the clinical scoring systems in correctly predicting mortality risk of AP patients, achieving an AUC of 0.89 (95% CI: 0.84-0.94). When set the sensitivity at 100% for death prediction, the model had a specificity of 38%, much higher than the APACHE IV, SOFA and BISAP score, which had a specificity of 1%, 16% and 1% respectively., Conclusions: This model might increase identification of very low-risk patients who can be safely monitored in a general ward for management. By making the model explainable, physicians would be able to better understand the reasoning behind the prediction., Competing Interests: Declarations. Ethics approval and consent to participate: Since the study was an analysis of the third party anonymized publicly available database with pre-existing institutional review board (IRB) approval, IRB approval from The First Affiliated Hospital of Zhejiang university was waived. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: Mild acute biliary pancreatitis (MABP) presents significant clinical and economic challenges due to its potential for relapse. Current guidelines advocate for early cholecystectomy (EC) during the same hospital admission to prevent recurrent acute pancreatitis (RAP). Despite these recommendations, implementation in clinical practice varies, highlighting the need for reliable and accessible predictive tools. The MINERVA study aims to develop and validate a machine learning (ML) model to predict the risk of RAP (at 30, 60, 90 days, and at 1-year) in MABP patients, enhancing decision-making processes., Methods: The MINERVA study will be conducted across multiple academic and community hospitals in Italy. Adult patients with a clinical diagnosis of MABP, in accordance with the revised Atlanta Criteria, who have not undergone EC during index admission will be included. Exclusion criteria encompass non-biliary aetiology, severe pancreatitis, and the inability to provide informed consent. The study involves both retrospective data from the MANCTRA-1 study and prospective data collection. Data will be captured using REDCap. The ML model will utilise convolutional neural networks (CNN) for feature extraction and risk prediction. The model includes the following steps: the spatial transformation of variables using kernel Principal Component Analysis (kPCA), the creation of 2D images from transformed data, the application of convolutional filters, max-pooling, flattening, and final risk prediction via a fully connected layer. Performance metrics such as accuracy, precision, recall, and area under the ROC curve (AUC) will be used to evaluate the model., Discussion: The MINERVA study aims to address the specific gap in predicting RAP risk in MABP patients by leveraging advanced ML techniques. By incorporating a wide range of clinical and demographic variables, the MINERVA score aims to provide a reliable, cost-effective, and accessible tool for healthcare professionals. The project emphasises the practical application of AI in clinical settings, potentially reducing the incidence of RAP and associated healthcare costs., Trial Registration: ClinicalTrials.gov ID: NCT06124989., Competing Interests: Declarations. Consent for publication: All authors of this study protocol provide their full consent for the publication of this work. Competing interests: The authors declare no competing interests. Ethics approval: The study has undergone Institutional Review Board Ethical approval (ID 2.7 09/01/2024, University of Cagliari, Italy)., (© 2025. The Author(s).)

Objectives: This study seeks to construct and internally validate a clinical prediction model for predicting new-onset dialysis-requiring acute kidney injury (AKI) following heart transplantation (HT)., Methods: The Kaplan-Meier survival analysis and log-rank test were utilized for conducting the survival analysis. A clinical prediction model was developed to predict postoperative dialysis-requiring AKI, based on a logistic regression model and likelihood ratio test with Akaike Information Criterion. The performance of the prediction model was assessed using C-index, receiver operating characteristic curves, calibration curves, Brier score, and the Spiegelhalter Z-test. Clinical utility was evaluated using decision curve analysis and clinical impact curves., Results: This study included a total of 525 patients who underwent orthotopic HT in the single center located in Wuhan, China between January 2015 and December 2021, with 16.57% developing postoperative dialysis-requiring AKI. Patients who experienced postoperative dialysis-requiring AKI exhibited a lower overall survival rate. All enrolled participants were randomly allocated into derivation (n = 350) and validation (n = 175) cohorts at a ratio of 2:1. The final prediction model comprised six indicators: diabetes, stroke, gout, prognostic nutritional index, estimated glomerular filtration rate, and cardiopulmonary bypass duration. The prediction model demonstrated outstanding discrimination (C-index of 0.792 in the derivation cohort and 0.834 in the validation cohort) as well as calibration performance, indicating strong concordance between observed and nomogram-predicted probabilities. Subgroup analysis based on age, preoperative serum creatine levels, and year of surgery also exhibited robust discrimination and calibration capabilities., Conclusions: Dialysis-requiring AKI following HT is associated with poor clinical prognosis. The prediction model, comprising six indicators, is capable of predicting dialysis-requiring AKI following HT. This prediction model holds promise in assisting both patients and clinicians in forecasting postoperative renal failure, thereby improving clinical management., Clinical Trial Number: Not applicable., Competing Interests: Declarations. Human ethics and consent to participate: This study was approved by the Ethics Committee of Tongji College, Huazhong University of Science and Technology (No: IORG0003263). Clinical and research activities comply the ‘Declaration of Istanbul on Organ Trafficking and Transplant Tourism’. Written informed consent was obtained from individual or guardian participants. Consent for publication: Written informed consent was obtained from individual or guardian participants. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

In January 2023, ICH Q9 was updated to include expanded guidance on risk-based decision-making, emphasizing its application in informing science-driven and strategic decisions. The revised guidance highlights that while quality risk management can aid decision-making, it does not eliminate the industry's obligation to comply with regulatory requirements. This article introduces a framework for evaluating the risks and benefits of pre-use/post-sterilization integrity testing (PUPSIT) using risk management principles. It provides a structured approach to assess the acceptability of alternative methods to the EU Annex 1 PUPSIT requirement, which acknowledges that PUPSIT may not always be feasible due to constraints such as the filtration of small solution volumes. In such cases, Annex 1 permits alternative approaches if a comprehensive risk assessment is conducted and effective controls are implemented to mitigate the risk of non-integral filtration systems. The proposed framework considers three critical domains-patient safety, process integrity, and regulatory compliance-to ensure decisions are well-informed and balanced. By applying this science- and risk-based approach, organizations can navigate PUPSIT requirements effectively, ensuring compliance while addressing operational limitations., (© PDA, Inc. 2025.)

Human health is at risk from drinking water contamination, which causes a number of health problems in many parts of the world. The geochemistry of groundwater, its quality, the origins of groundwater pollution, and the associated health risks have all been the subject of substantial research in recent decades. In this study, groundwater in the west Rosetta Nile branch of the Nile Delta Aquifer is examined for drinking potential. Numerous water quality indices were applied, such as water quality index (WQI), synthetic pollution index (SPI) models, and health risk assessment (HRA) method. The limits of the measured parameters are used to test its drinking validity on the basis of WHO recommendations. TDS in the southern regions is within the desirable to allowable limits with percent 25.3% and 29.33%, respectively. Nearly all the study area has desirable value for HCO 3 , Al and Ba. Ca and Mg have desirable values in the center and south portion of the investigated area, whereas in the north are unsuitable. Na, Cl and SO 4 fall within the desired level in the regions of the south but become unsuitable towards the north. Mn and NO 3 are inappropriate except in the northwestern part. Fe is within suitable range in the southwestern and northwestern regions. Pb, Zn, Cu, and Cd were undetected in the collected samples. Regarding to WQI the study area is classified into 4 classes good, poor, very poor and unfit for drinking water from south to north. According to SPI model, 20%, 18.7%, 18.7%, 8% and 34.6% of water samples are suitable, slightly, moderately, highly polluted and unfit, respectively from south to north. Based on HRA, Children are the most category endangered with percent 14.7% of the overall samples obtained, followed by females and males with percent 12% and 8%, respectively. This study offers insights into the conservation and management of coastal aquifers' groundwater supplies. These findings have significant implications for developing strategies and executing preventative actions to reduce water resource vulnerability and related health hazards in West Nile Delta, Egypt., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objectives: Peak exercise systolic blood pressure (SBP) is associated with future cardiovascular disease (CVD) and mortality. We aimed to evaluate the predictive value of different SBP patterns at the end of exercise with these outcomes., Methods: We studied 6329 adults (45% women) referred for exercise testing, with test duration of 6-14 min, maximal effort and valid SBP measurements at the end of exercise. The two last SBPs were indexed to work rate (mmHg/Watt), defining responses as: drop (negative change), plateau (no change), slow (lower tertile of increase), intermediate (middle tertile) and steep (upper tertile). Data were cross-linked with nationwide disease and mortality registries. Associations with all-cause mortality and incident CVD were analysed using Cox proportional hazards regression (hazard ratio (HR), 95% confidence interval), using slow SBP increase as reference, adjusted for sex, age, body mass index, baseline CVD (mortality analysis only), beta-blockers and exercise capacity (peak Watt)., Results: The prevalence of SBP responses at the end of exercise were drop (1.1%), plateau (15.0%), slow (30.4%), intermediate (25.2%) and steep increase (28.3%). Follow-up was 8.8±3.4 years. Compared with a slow increase, the adjusted all-cause mortality risks were not statistically different for a drop (HR 1.16 (0.50-2.65)), plateau (HR 1.19 (0.85-1.66)), intermediate (HR 1.24 (0.93-1.66)) or steep SBP increase (HR 1.16 (0.89-1.52)). CVD risk was increased in those with a SBP drop (HR 3.10 (1.85-5.19), but not significantly for plateau (HR 1.17 (0.92-1.48)), intermediate or steep SBP increases (HRs 0.99-1.00)., Conclusion: Subjects with a slow SBP increase at the end of exercise tended to have the lowest mortality risk, although no SBP response pattern predicted all-cause mortality independently. CVD risk was strongly increased in patients with a drop in SBP and tended to be increased (non-significantly) also in patients with a plateau in SBP at the end of exercise, in comparison with increasing SBP., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Almost 40 million people worldwide are living with human immunodeficiency virus (HIV) infection. With treatment advances, HIV infection is now a manageable chronic disease for those with access to medical therapy. People living with HIV have a significantly higher risk and earlier onset of cardiovascular disease (CVD) owing to chronic inflammation and other biochemical factors, as well as overlapping social determinants of health and nonbiologic risk factors. Knowing that patients living with HIV develop coronary artery disease much earlier than the general population, careful attention must be given to assessment and management of their cardiovascular risk., (Copyright © 2025 The Cleveland Clinic Foundation. All Rights Reserved.)

Substances leached from a medical device during its clinical use are important due to the patient health-related effects they may have. Thus, medical devices are profiled for leachables (and/or extractables as probable leachables) by screening extracts or leachates of the medical device for released organic substances via a non-target analysis (NTA) employing chromatographic methods coupled with mass spectrometric detection. Chromatographic mass spectral response factors for extractables and leachables vary significantly from compound to compound, complicating the application of assessment strategies such as the analytical evaluation threshold (AET), which is the concentration threshold at or above which an extractable or leachable must be reported for quantitative toxicological risk assessment. The analytical uncertainty resulting from response variation can make interpretation of the AET difficult, potentially leading to both false positive and false negative outcomes. Furthermore, response factor variation complicates the estimation of leachables' and extractables' concentrations (quantification). This Correspondence discusses practices for the calculation and application of the AET and for performing quantification, including a discussion of accuracy versus protectiveness., (© PDA, Inc. 2025.)

Background and Aims: Robust and convenient risk stratification of patients with paediatric and adult congenital heart disease (CHD) is lacking. This study aims to address this gap with an artificial intelligence-enhanced electrocardiogram (ECG) tool across the lifespan of a large, diverse cohort with CHD., Methods: A convolutional neural network was trained (50%) and tested (50%) on ECGs obtained in cardiology clinic at the Boston Children's Hospital to detect 5-year mortality. Temporal validation on a contemporary cohort was performed. Model performance was evaluated using the area under the receiver operating characteristic and precision-recall curves., Results: The training and test cohorts composed of 112 804 ECGs (39 784 patients; ECG age range 0-85 years; 4.9% 5-year mortality) and 112 575 ECGs (39 784 patients; ECG age range 0-92 years; 4.6% 5-year mortality from ECG), respectively. Model performance (area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.77-0.81; area under the precision-recall curve 0.17, 95% confidence interval 0.15-0.19) outperformed age at ECG, QRS duration, and left ventricular ejection fraction and was similar during temporal validation. In subgroup analysis, artificial intelligence-enhanced ECG outperformed left ventricular ejection fraction across a wide range of CHD lesions. Kaplan-Meier analysis demonstrates predictive value for longer-term mortality in the overall cohort and for lesion subgroups. In the overall cohort, precordial lead QRS complexes were most salient with high-risk features including wide and low-amplitude QRS complexes. Lesion-specific high-risk features such as QRS fragmentation in tetralogy of Fallot were identified., Conclusions: This temporally validated model shows promise to inexpensively risk-stratify individuals with CHD across the lifespan, which may inform the timing of imaging/interventions and facilitate improved access to care., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Most studies found that apolipoprotein B (apo B)-100 is a superior marker for coronary risk to non-high-density lipoprotein (HDL) cholesterol (C). Usually, studies use multivariant analysis with single-point odds/risk ratios. In multivariant analysis, when variables are highly correlated they are difficult to interpret. Effects cannot be well discriminated., Methods: Brief review and examination of diagnostic sensitivity and specificity by receiver operator characteristic (ROC) curves at decision levels so that discrimination can be well compared. Since apo B has additional expense, clinical value should be compared in an appropriate format. Apo B and cholesterols were measured in 382 angiographically defined patients., Results: Non-HDLC and apo B were stronger markers than low-density lipoprotein (LDL)C, when examined by logistic regression, but as a result of strong collinearity, non-HDLC appeared weaker than LDLC in the presence of apo B, based on P values. This was true when analyzed with and without nonlipid risk factors. On ROC analysis, apo B and non-HDLC showed stronger C statistics than LDLC and total C. When analyzed alone apo B showed about 6.1% greater sensitivity than non-HDLC. After adjustment for nonlipid risk factors, the C statistics for apo B and non-HDLC were 0.74 and 0.73, and there was little difference in diagnostic specificity., Conclusions: Risk is calculated from an algorithm that includes nonlipid risk factors similar to those examined here along with cholesterols. When assessed by the 10-year screening algorithm, these data support the view that non-HDLC would be less expensive than apo B with similar clinical efficacy., (© Association for Diagnostics & Laboratory Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background and Aims: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk., Methods: An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed., Results: The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), -.1 (6.0), 4.7 (8.7), and -1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24-2.78), 1.89 (1.46-2.43), 1.90 (1.55-2.33), and 1.76 (1.67-1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47-3.37), 1.94 (1.39-2.70), 1.58 (1.06-2.35), and 1.79 (1.62-1.97) in these cohorts compared with the normal group., Conclusions: The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Evidence on the longitudinal association of serum uric acid (SUA) with the risk of heart failure (HF) was limited and controversial. This study aimed to investigate the associations of cumulative SUA (cumSUA), incorporating its time course of accumulation, with the risk of HF., Methods and Results: This prospective study enrolled 54 606 participants from the Kailuan study. The magnitude of SUA accumulation was expressed as cumSUA, exposure duration, and cumulative burden from baseline to the third survey, with cumSUA, calculated by multiplying mean values between consecutive examinations by time intervals between visits, as the primary exposure.During a median follow-up of 10 years, 1260 cases of incident HF occurred. A higher risk of HF was observed in participants with the highest vs. the lowest quartile of cumSUA [adjusted hazard ratio (aHR), 1.54; 95% confidence interval (CI), 1.29-1.84], 6-year vs. 0-year exposure duration (aHR, 1.87; 95% CI, 1.43-2.45), cumulative burden >0 vs. = 0 (aHR, 1.55; 95 CI, 1.29-1.86), and those with a negative vs. positive SUA slope (aHR, 1.12; 95% CI, 1.02-1.25). When cumSUA was incorporated with its time course, those with cumSUA ≥median and a negative SUA slope had the highest risk of HF (aHR, 1.55; 95% CI, 1.29-1.86)., Conclusions: Incident HF risk was associated with the magnitude and time course of cumSUA accumulation. Early accumulation resulted in a greater risk of HF compared with later accumulation, indicating the importance of optimal SUA control earlier in life., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Objectives: To elucidating the linkage between obesity-associated body fat indicators and atrial fibrillation (AF) using Mendelian Randomization (MR) and mediation analysis., Methods: The study utilized three independent genome-wide association study (GWAS) datasets, with containing over 450 000 individuals each, to represent body fat indicators as the exposure variable. Additionally, two summary genetic datasets of AF were utilized as the clinical outcome. Single nucleotide polymorphisms (SNPs) with p-values less than 5 × 10 -10 were identified as instrumental variables (IVs) for MR analysis. The primary analysis method employed was the inverse-variance weighting (IVW) model, supplemented by three additional models: MR-Egger regression, weighted median, and maximum likelihood. Sensitivity analysis was conducted, encompassing tests for heterogeneity and horizontal pleiotropy, utilizing Cochran's Q, MR-Egger intercept, and MR-PRESSO tests to validate the reliability of the findings. Furthermore, a mediation analysis was conducted to explore potential mediators involved in the pathogenesis of AF., Results: The IVW model demonstrated that per 1-SD increase in body fat indicators (body fat percentage, whole body fat mass, and trunk fat mass) is associated with an elevated risk of AF, with values of 63.1%, 55.0%, and 55.8% respectively. All three supplementary models arrived comparable conclusions with IVW model. The sensitivity analysis confirmed the absence of horizontal pleiotropy, thereby validating the reliability of the findings. Additionally, the mediation study indicates that hypertension and sleep apnea syndrome are identified as significant mediators during the pathogenesis of AF., Conclusions: The study reveals that individuals with a higher body fat percentage tend to exhibit a heightened genetic predisposition for susceptibility to AF. Meanwhile, hypertension and sleep apnea syndrome have been identified as key mediators contributing to the pathogenesis of AF., (© 2025 The Author(s). Clinical Cardiology published by Wiley Periodicals LLC.)

Background: As one of the most severe occupational diseases that prevention efforts have supported for several decades, silicosis is still a public health issue that lacks a prediction model for pulmonary embolism., Methods: A total of 162 patients confirmed to have silicosis were all involved in a training cohort to construct a nomogram with the outcome diagnosed by the CTPA using logistic regression. Univariate and LASSO analyses were used to select variables for the nomogram., Result: mMRC, pectoralgia, history of VTE, active tumor, unilateral lower limb pain or edema, hormonotherapy, reduced mobility, and heart failure/respiratory failure were selected for the establishment of the nomogram for silicosis with pulmonary embolism., Conclusion: A novel nomogram was developed to predict pulmonary embolism in silicosis patients. The internal validation indicated that clinicians could utilize this predictive model to help decision-making and patient management., (© 2025 The Author(s). The Clinical Respiratory Journal published by John Wiley & Sons Ltd.)

This review examines the figure of Dr. René Favaloro, a pioneer in cardiovascular surgery and advocate for social justice, who devoted his life to making advanced medical care accessible to underserved communities. Despite the increasing incidence of coronary artery disease (CAD), particularly in Asian and Latin American countries, Favaloro envisioned a healthcare system where innovative technology benefits everyone. Building on his ideals, we explore the democratization of healthcare access through innovative tools for cardiovascular risk assessment, specifically Pulse Wave Velocity (PWV) and its association with Coronary Artery Calcium Score (CACs). PWV, a non-invasive and cost-effective method, shows promise as a practical screening tool for CAD, particularly when combined with Computational Intelligence (CI) and the Internet of Medical Things (IoMT). The integration of PWV into a Point-of-Care Testing (POCT) framework could enhance preventive care, especially in underserved populations. By aligning with Favaloro's vision of equitable healthcare, this approach seeks to support CAD screening and risk assessment in low-resource settings, aiming to overcome socio-economic barriers and improve access to preventive cardiac care.

Background: Given the importance of diagnosing intra-abdominal injury (IAI) in children with blunt abdominal trauma (BAT) and preventing radiation exposure to children by avoiding CT scans, this study aimed to evaluate a scoring criterion based on ultrasound (US) findings and laboratory data in assessing the clinical risk of IAI in children with BAT., Materials and Methods: In this retrospective study, baseline and clinical information of 180 children (under 18 years of age) with BAT including physical examination, hemodynamic parameters, and laboratory data, were extracted from medical records. US findings were considered abnormal if any report of mild free fluid or solid organ injury was noted. The presence or absence of IAI was assessed through medical records or telephone interviews to inquire about the patients' outcome within the two-week period post-discharge. The primary outcome was the identification of IAI, assessed through a combination of US findings, physical examination (abdominal tenderness), and laboratory parameters (WBC count and hematuria). The measurement methods included Chi-squared tests, Fisher's exact test, independent samples t-test, logistic regression, and ROC analysis., Results: The current study showed that 153 (85%) and 27 (15%) patients were without and with IAI, respectively. The positive US finding with sensitivity and specificity of 92.59% and 44.44%, respectively, abdominal tenderness with sensitivity and specificity of 81.48% and 87.58%, respectively, hematuria with sensitivity and specificity of 62.96% and 50.33%, respectively, and high WBC level with sensitivity and specificity of 85.19% and 76.47%, had a significant diagnostic value in detecting the presence of IAI (P value < 0.001). A cutoff point ≥ 2 from the sum of the scores of these four criteria can predict the presence of IAI with a sensitivity of 81.48% and a specificity of 94.12% (AUC = 0.94; P value < 0.001)., Conclusion: This study shows that a scoring system based on positive US findings, abdominal tenderness, hematuria, and high WBC levels effectively diagnoses IAI in BAT children. A score of 2 or more strongly indicates the presence of IAI, improving decision-making for further imaging and treatment. Implementing this system can reduce unnecessary CT scans and radiation exposure, enhancing pediatric trauma care., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Conflict of interest: The authors declare that they have no conflicts of interest. Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki in its current version (World Medical Association [WMA], 2013). The ethics committee of Isfahan University of Medical Sciences approved this study (approval number: IR.MUI.MED.REC.1403.285). Through the informed oral consent of patients or their first-degree relatives, data were collected and presented anonymously in this project. Clinical trial number: not applicable., (© 2025. The Author(s).)

Background: Currently, various risk prediction models for surgical site infection (SSI) in patients with gastrointestinal tumors have been developed, but comprehensive comparisons regarding the model construction process, performance, and data sample bias are lacking. This study conducts a systematic review of relevant research to evaluate the risk bias and clinical applicability of these models., Materials and Methods: The Web of Science, PubMed, Cochrane Library, Embase, CINAHL, CBM, CNKI, Wanfang, and VIP databases were searched for studies related to SSI prediction models in gastrointestinal cancer patients published up to August 19, 2024. Two researchers independently screened the literature, extracted the data, and evaluated the quality. A meta-analysis was conducted on the common predictive factors included in the model, using odds ratio (OR) values and 95% confidence interval (CI) as effect statistics. The Q test and heterogeneity index I 2 were used to assess heterogeneity. All the statistical analyses were performed via Stata 16.0 software. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was submitted as a supplement., Results: A total of 28 articles were included, and 39 models were constructed. The area under the receiver operating characteristic curve (AUC) for the models ranged from 0.660 to 0.950, indicating good predictive performance. Eight studies conducted internal validation, eight studies conducted external validation, and two studies used a combination of internal and external validation for model evaluation. The overall risk of bias in the literature was high, but the applicability was good. The results of the meta-analysis revealed that factors such as underlying diseases, surgical factors, demographic factors, and laboratory-related indicators are the main predictors of surgical site infections in patients with gastrointestinal tumors., Conclusions: Currently, risk prediction models for surgical site infections in patients with gastrointestinal cancer remain in the developmental phase, and there is a high risk of bias in the areas of study subjects, outcomes, and analysis. Researchers need to enhance research methodologies, conduct large-scale prospective studies, and refer to the reporting standards of the bias risk assessment tool for predictive models to construct predictive models with low bias risk and high applicability., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Informed consent: For this type of study, formal consent is not required. Consent for publication: All authors have reviewed the manuscript and agreed for the publication. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)