Critical Assessment of the Management of Unstable Angina in a Specialized Cardiology Emergency Room. / Avaliação Crítica do Manejo da Angina Instável em Pronto-Socorro Terciário de Cardiologia.

BACKGROUND: The management of unstable angina (UA) presents a challenge due to its subjective diagnosis and limited representation in randomized clinical trials that inform current practices. OBJECTIVES: This study aims to identify key factors associated with the indication for invasive versus non-invasive stratification in this population and to evaluate factors associated with stratification test results. METHODS: This retrospective cohort study included patients hospitalized with UA over a consecutive 20-month period. To assess factors associated with stratification strategies, patients were divided into invasive stratification (coronary angiography) and non-invasive stratification (other methods) groups. For the analysis of factors related to changes in stratification tests, patients were categorized into groups with or without obstructive coronary artery disease (CAD) or ischemia, as per the results of the requested tests. Comparisons between groups and multiple logistic regression analyses were performed, with statistical significance set at a 5% level. RESULTS: A total of 729 patients were included, with a median age of 63 years and a predominance of males (64.6%). Factors associated with invasive stratification included smoking (p = 0.001); type of chest pain (p < 0.001); "crescendo" pain (p = 0.006); TIMI score (p = 0.006); HEART score (p = 0.011). In multivariate analysis, current smokers (OR 2.23, 95% CI 1.13-4.8), former smokers (OR 2.19, 95% CI 1.39-3.53), and type A chest pain (OR 3.39, 95% CI 1.93-6.66) were independently associated. Factors associated with obstructive CAD or ischemia included length of hospital stay (p < 0.001); male gender (p = 0.032); effort-induced pain (p = 0.037); Diamond-Forrester score (p = 0.026); TIMI score (p = 0.001). In multivariate analysis, only chest pain (type B chest pain: OR 0.6, 95% CI 0.38-0.93, p = 0.026) and previous CAD (OR 1.42, 95% CI 1.01-2.0, p = 0.048) were independently associated. CONCLUSION: The type of chest pain plays a crucial role not only in the diagnosis of UA but also in determining the appropriate treatment. Our results highlight the importance of incorporating pain characteristics into prognostic scores endorsed by guidelines to optimize UA management.

FUNDAMENTO: O manejo da angina instável (AI) é um desafio devido ao seu diagnóstico subjetivo e à sua escassa representação em ensaios clínicos randomizados que determinem as práticas atuais. OBJETIVOS: O objetivo deste estudo é identificar os principais fatores associados à indicação de estratificação invasiva ou não nessa população e avaliar os fatores associados às alterações nos exames de estratificação. MÉTODOS: Coorte retrospectiva de pacientes internados por AI, em um período de 20 meses consecutivos. Para avaliar os fatores associados à estratégia de estratificação, os pacientes foram divididos em estratificação invasiva (cinecoronariografia) e não invasiva (demais métodos). Para análise de fatores relacionados às alterações nos exames de estratificação, os pacientes foram divididos em grupos com ou sem doença arterial coronariana (DAC) obstrutiva ou isquemia, conforme resultados dos exames solicitados. Foram realizadas comparações entre grupos e análise de regressão logística múltipla, com significância estatística definida em um nível de 5%. RESULTADOS: 729 pacientes foram incluídos, com mediana de idade de 63 anos e predomínio do sexo masculino (64,6%). Estiveram associados à estratificação invasiva: tabagismo (p = 0,001); tipo de dor torácica (p < 0,001); dor "em crescendo" (p = 0,006); escore TIMI (p = 0,006); escore HEART (p = 0,011). Na análise multivariada, tabagistas (OR 2,23, IC 95% 1,13-4,8), ex-tabagistas (OR 2,19, IC 1,39-3,53) e dor torácica tipo A (OR 3,39, IC 95% 1,93-6,66) estiveram associados de forma independente. Estiveram associados à DAC obstrutiva ou isquemia: tempo de internação hospitalar (p < 0,001); sexo masculino (p = 0,032); dor desencadeada por esforço (p = 0,037); Diamond-Forrester (p = 0,026); escore TIMI (p = 0,001). Na análise multivariada, apenas dor torácica (dor torácica tipo B: OR 0,6, IC 95% 0,38-0,93, p = 0,026) e DAC prévia (OR 1,42, IC 95% 1,01-2,0, p = 0,048) estiveram associadas de maneira independente. CONCLUSÕES: O tipo de dor torácica desempenha um papel crucial não apenas no diagnóstico da AI, mas também na definição do tratamento adequado. Nossos resultados destacam a importância de incorporar características da dor aos escores prognósticos endossados pelas diretrizes, para otimização do manejo da AI.

A web-based pesticide risk assessment tool for drinking water protection zones in Sweden.

To protect human health, wildlife and the aquatic environment, "safe uses" of pesticides are determined at the EU level while product authorization and terms of use are established at the national level. In Sweden, extra precaution is taken to protect drinking water, and permits are therefore required for pesticide use within abstraction zones. This paper presents MACRO-DB, a tool for assessing pesticide contamination risks of groundwater and surface water, used by authorities to support their decision-making for issuing such permits. MACRO-DB is a meta-model based on 583,200 simulations of the physically-based MACRO model used for assessing pesticide leaching risks at EU and national level. MACRO-DB is simple to use and runs on widely available input data. In a qualitative comparative assessment for two counties in Sweden, MACRO-DB outputs were in general agreement with groundwater monitoring data and matched or were more protective than the national risk assessment procedure for groundwater.

Comparación entre las calculadoras de riesgo del European Randomized Study for Screening of Prostate Cancer (ERSPC) y Prostate Biopsy Collaborative Group (PBCG): predicción del riesgo de cáncer de próstata clínicamente significativo en una cohorte de pacientes de Argentina / Comparison between the European Randomized Study for Screening of Prostate Cancer (ERSPC) and Prostate Biopsy Collaborative Group (PBCG) risk calculators: Prediction of clinically significant prostate cancer risk in a cohort of patients from Argentina

Objetivo Comparar el desempeño de las calculadoras de riesgo del European Randomised Study for Screening of Prostate Cancer (ERSPC-RC) y el Prostate Biopsy Collaborative Group (PBCG-RC) en predecir el riesgo de presentar cáncer de próstata clínicamente significativo. Material y métodos Retrospectivamente, se identificó a los pacientes que fueron sometidos a biopsia prostática en el Sanatorio Allende Cerro, Ciudad de Córdoba, Argentina, desde enero de 2018 a diciembre de 2021. Se calculó la probabilidad de tener cáncer de próstata con las dos calculadoras por separado y luego se compararon los resultados para establecer cuál de las dos tuvo mejor desempeño. Para esto, se analizaron áreas bajo la curva (ABC). Resultados Se incluyeron 250 pacientes, 140 (56%) presentaron cáncer de próstata, de los cuales 92 (36,8%) tuvieron cáncer de próstata clínicamente significativo (Score de Gleason ≥7). Los pacientes que presentaron cáncer tenían mayor edad, mayor valor de antígeno prostático específico (PSA) y menor tamaño prostático. El ABC para predecir la probabilidad de tener cáncer de próstata clínicamente significativo fue de 0,79 y 0,73 para PBCG-RC y ERSPC-RC, respectivamente (p=0,0084). Conclusión En esta cohorte de pacientes, ambas calculadoras de riesgo de cáncer de próstata mostraron un buen desempeño para predecir el riesgo de cáncer de próstata clínicamente significativo, si bien el PBCG-RC mostró mejor exactitud. (AU)

Objective To compare the performance of the risk calculators of the European Randomized Study for Screening of Prostate Cancer (ERSPC) and the Prostate Biopsy Collaborative Group (PBCG) in predicting the risk of presenting clinically significant prostate cancer. Material and methods Retrospectively, patients who underwent prostate biopsy at Sanatorio Allende Cerro, Ciudad de Córdoba, Argentina, were identified from January 2018 to December 2021. The probability of having prostate cancer was calculated with the two calculators separately and then the results were compared to establish which of the two performed better. For this, areas under the curve (AUC) were analyzed. Results 250 patients were included, 140 (56%) presented prostate cancer, of which 92 (65.71%) had clinically significant prostate cancer (Gleason score ≥7). The patients who presented cancer were older, had a higher prostate-specific antigen (PSA) value, and had a smaller prostate size. The AUC to predict the probability of having clinically significant prostate cancer was 0.79 and 0.73 for PBCG-RC and ERSPC-RC respectively (p=0.0084). Conclusion In this cohort of patients, both prostate cancer risk calculators performed well in predicting clinically significant prostate cancer risk, although the PBCG-RC showed better accuracy. (AU)

Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.

BACKGROUND: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. METHODS: Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). RESULTS: The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). CONCLUSIONS: The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.

ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain.

BACKGROUND: ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4's risk stratification of simulated patients with acute nontraumatic chest pain. METHODS: Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency. RESULTS: ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45-48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605). CONCLUSION: While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients.

Evaluating of Existing VTE Risk Scales in Glioma Patients.

INTRODUCTION: Postoperative venous thromboembolism (VTE) is a frequently occurring complication among glioma patients. Several risk assessment models (RAMs), including the Caprini RAM, the IMPROVE Risk Score, the IMPROVED VTE Risk Score, and the Padua Prediction Score, have not been validated within the glioma patient population. The purpose of this study was to assess the predictive accuracy of established VTE risk scales in patients with glioma. MATERIALS AND METHODS: A single-center, retrospective, observational cohort study was conducted on 265 glioma patients who underwent surgery at the Almazov Medical and Research Centre between 2021 and 2022. VTE detection followed the current clinical guidelines. Threshold values for the Caprini, IMPROVE VTE, IMPROVEDD, and Padua scales were determined using ROC analysis methods, with cumulative weighting for sensitivity and specificity in predicting VTE development. The areas under the ROC curves (AUC) were calculated, and comparisons were made using the DeLong test. RESULTS: The area under the curve for the Caprini risk assessment model was 80.41, while the IMPROVEDD VTE risk score was 75.38, the Padua prediction score was 76.9, and the IMPROVE risk score was 72.58. No significant differences were observed in the AUC values for any of the scales. The positive predictive values of all four scales were low, with values of 50 (28-72) for Caprini, 48 (28-69) for IMPROVEDD VTE, 50 (30-70) for Padua, and 64 (35-87) for IMPROVE RAM. No significant differences were found in terms of PPV, NPV, positive likelihood ratio, and negative likelihood ratio among the analyzed scales. CONCLUSIONS: The Caprini Risk Assessment Model, the IMPROVE Risk Score, the IMPROVED VTE Risk Score, and the Padua Prediction Score exhibit acceptable specificity and sensitivity for glioma patients. However, their low positive predictive ability, coupled with the complexity of interpretation, limits their utility in neurosurgical practice.

A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment.

The increase in life expectancy, and the consequent growth of the elderly population, represents a major challenge to guarantee adequate health and social care. The proposed system aims to provide a tool that automates the evaluation of gait and balance, essential to prevent falls in older people. Through an RGB-D camera, it is possible to capture and digitally represent certain parameters that describe how users carry out certain human motions and poses. Such individual motions and poses are actually related to items included in many well-known gait and balance evaluation tests. According to that information, therapists, who would not need to be present during the execution of the exercises, evaluate the results of such tests and could issue a diagnosis by storing and analyzing the sequences provided by the developed system. The system was validated in a laboratory scenario, and subsequently a trial was carried out in a nursing home with six residents. Results demonstrate the usefulness of the proposed system and the ease of objectively evaluating the main items of clinical tests by using the parameters calculated from information acquired with the RGB-D sensor. In addition, it lays the future foundations for creating a Cloud-based platform for remote fall risk assessment and its integration with a mobile assistant robot, and for designing Artificial Intelligence models that can detect patterns and identify pathologies for enabling therapists to prevent falls in users under risk.

The combined risk predictive power of frailty and hypoalbuminemia in free tissue flap reconstruction: A cohort study of 34,571 patients from the NSQIP database.

INTRODUCTION: Significant morbidity and mortality are hallmarks of the functional decline seen in physically frail patients. The modified frailty index 5 (mFI-5) represents a risk predictor score that has been validated as a comorbidity-based scale in surgery. Serum albumin levels of <3.5 g/dL (hypoalbuminemia) have also been implicated with poor postoperative outcomes. However, the association between these two parameters remains to be investigated. We aimed to elucidate the interdependence of preoperative albumin levels and frailty, as evaluated by the mFI-5 score, and its reliability to prognosticate postoperative results in free flap reconstruction (FFR). METHODS: We conducted a multicenter, retrospective cohort study and accessed the ACS National Surgical Quality Improvement Program (ACS-NSQIP) from 2008 to 2021. We identified all adult patients (≥18 years of age) who underwent a FFR. We extracted perioperative data and lab values including albumin. Multivariable linear and logistic regression analyses were performed to identify independent risk predictors. Main outcomes involved mortality, length of hospital stay, reoperation, medical and surgical complications, and discharge destination within the 30-day postoperative period. RESULTS: A total of 34,571 patients were included in the study, with an average age of 53.9 years (standard deviation [SD] 12.2) and an average body mass index (BMI) of 28.8 (SD 6.1). Of these patients, 7484 were male (21.6%), whereas 22,363 (64.7%) had no frailty (mFI = 0). Additionally, 9466 patients had a frailty score of 1 (27.4%), 2505 had a score of 2 (7.2%), 226 had a score of 3 (0.7%), and 11 had a score of 4 or higher (0.0%). Albumin levels were available for 16,250 patients (47.0%), and among them, 1334 (8.2%) had hypoalbuminemia. Regression analyses showed that higher mFI scores were independent predictors of any, surgical, and medical complications, as well as increased rates of reoperations, unplanned readmissions, and prolonged hospital stays. Hypoalbuminemia independently predicted any, surgical, and medical complications, and higher mortality, reoperation, and longer hospital stay. When both frailty and albumin levels (mFI-5 and albumin) were considered together, this combined assessment was found to be a more accurate predictor of all major outcomes (any, medical and surgical complications, mortality, and reoperation). Further, our analysis identified a weak negative correlation between serum albumin levels and mFI scores (Spearman R: -.1; p < .0001). CONCLUSION: In conclusion, this cohort study highlights the association of hypoalbuminemia with adverse postoperative outcomes, including those not directly related to frailty. Simultaneously, higher mFI scores independently predicted outcomes not associated with hypoalbuminemia. Stemming from these findings, we recommend considering both serum albumin levels and frailty in patients receiving FFR. This perioperative algorithm may help provide more individualized planning including multidisciplinary care and pre and posthabilitation.

Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study.

BACKGROUND: Affecting 2-4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia. METHODS: We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of combined published data of 8843 patients from 11 low-income, middle-income, and high-income countries). Validation was undertaken on the unseen 25%, and an additional external validation was performed in 2901 inpatient women admitted with pre-eclampsia to two hospitals in south-east England. Predictive risk accuracy was determined by area-under-the-receiver-operator characteristic (AUROC), and risk categories were data-driven and defined by negative (-LR) and positive (+LR) likelihood ratios. FINDINGS: Of 8843 participants, 590 (6·7%) developed the composite adverse maternal outcome within 2 days, 813 (9·2%) within 7 days, and 1083 (12·2%) at any time. An 18-variable random forest-based prediction model, PIERS-ML, was accurate (AUROC 0·80 [95% CI 0·76-0·84] vs the currently used logistic regression model, fullPIERS: AUROC 0·68 [0·63-0·74]) and categorised women into very low risk (-LR <0·1; eight [0·7%] of 1103 women), low risk (-LR 0·1 to 0·2; 321 [29·1%] women), moderate risk (-LR >0·2 and +LR <5·0; 676 [61·3%] women), high risk (+LR 5·0 to 10·0, 87 [7·9%] women), and very high risk (+LR >10·0; 11 [1·0%] women). Adverse maternal event rates were 0% for very low risk, 2% for low risk, 5% for moderate risk, 26% for high risk, and 91% for very high risk within 48 h. The 2901 women in the external validation dataset were accurately classified as being at very low risk (0% with outcomes), low risk (1%), moderate risk (4%), high risk (33%), or very high risk (67%). INTERPRETATION: The PIERS-ML model improves identification of women with pre-eclampsia who are at lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, and can support provision of accurate guidance to women, their families, and their maternity care providers. FUNDING: University of Strathclyde Diversity in Data Linkage Centre for Doctoral Training, the Fetal Medicine Foundation, The Canadian Institutes of Health Research, and the Bill & Melinda Gates Foundation.

Repeated Measurement of the Novel Atrial Biomarker BMP10 (Bone Morphogenetic Protein 10) Refines Risk Stratification in Anticoagulated Patients With Atrial Fibrillation: Insights From the ARISTOTLE Trial.

BACKGROUND: BMP10 (bone morphogenic protein 10) has emerged as a novel biomarker associated with the risk of ischemic stroke and other outcomes in patients with atrial fibrillation (AF). The study aimed to determine if repeated BMP10 measurements improve prognostication of cardiovascular events in patients with AF. METHODS AND RESULTS: BMP10 was measured using a prototype Elecsys immunoassay in plasma samples collected at randomization and after 2 months in patients with AF randomized to apixaban or warfarin in the ARISTOTLE (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation) trial (n=2878). Adjusted Cox-regression models were used to evaluate the association between 2-month BMP10 levels and outcomes. BMP10 levels increased by 7.8% (P<0.001) over 2 months. The baseline variables most strongly associated with BMP10 levels at 2 months were baseline BMP10 levels, body mass index, sex, age, creatinine, diabetes, warfarin treatment, and AF-rhythm. During median 1.8 years follow-up, 34 ischemic strokes/systemic embolism, 155 deaths, and 99 heart failure hospitalizations occurred. Comparing the third with the first sample quartile, higher BMP10 levels at 2 months were associated with higher risk of ischemic stroke (hazard ratio [HR], 1.33 [95% CI, 0.67-2.63], P=0.037), heart failure (HR, 1.91 [95% CI, 1.17-3.12], P=0.012) and all-cause death (HR, 1.61 [95% CI, 1.17-2.21], P<0.001). Adding BMP10 levels at 2 months on top of established risk factors and baseline BMP10 levels improved the C-indices for ischemic stroke/systemic embolism (from 0.73 to 0.75), heart failure hospitalization (0.76-0.77), and all-cause mortality (0.70-0.72), all P<0.05. CONCLUSIONS: Elevated levels of BMP10 at 2 months strengthened the associations with the risk of ischemic stroke, hospitalization for heart failure, and all-cause mortality. Repeated measurements of BMP10 may further refine risk stratification in patients with AF.

Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review.

Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.

Twenty-eight years of GM Food and feed without harm: why not accept them?

Since the first genetically engineered or modified crops or organisms (GMO) were approved for commercial production in 1995, no new GMO has been proven to be a hazard or cause harm to human consumers. These modifications have improved crop efficiency, reduced losses to insect pests, reduced losses to viral and microbial plant pathogens and improved drought tolerance. A few have focused on nutritional improvements producing beta carotene in Golden Rice. Regulators in the United States and countries signing the CODEX Alimentarius and Cartagena Biosafety agreements have evaluated human and animal food safety considering potential risks of allergenicity, toxicity, nutritional and anti-nutritional risks. They consider risks for non-target organisms and the environment. There are no cases where post-market surveillance has uncovered harm to consumers or the environment including potential transfer of DNA from the GMO to non-target organisms. In fact, many GMOs have helped improve production, yield and reduced risks from chemical insecticides or fungicides. Yet there are generic calls to label foods containing any genetic modification as a GMO and refusing to allow GM events to be labeled as organic. Many African countries have accepted the Cartagena Protocol as a tool to keep GM events out of their countries while facing food insecurity. The rationale for those restrictions are not rational. Other issues related to genetic diversity, seed production and environmental safety must be addressed. What can be done to increase acceptance of safe and nutritious foods as the population increases, land for cultivation is reduced and energy costs soar?

[Health Risk Assessment for an Arsenic-contaminated Site Based on Monte Carlo Simulation and Parameters Optimization].

Risk assessment is a critical part of risk management for contaminated sites. However， in the specific management practice of As-contaminated sites， it is difficult to obtain realistic health risks for contaminated sites based on the total amount of pollutants and determined values of the model， thus preventing the control requirements of later remediation to be met. An increasing number of studies have recently been conducting risk assessments by considering bioavailability， modification parameters， and combined probabilistic models. To improve the accuracy of risk assessment results， taking a large As-contaminated site as a case， 432 sampling sites were set up and collected at different depths to analyze the level and distribution characteristics of As pollution， and probabilistic risk assessment was conducted with the modification of model parameters through literature research and Monte Carlo simulation. Then， the impact of traditional methods and probabilistic methods on health risk assessment was explored in comparison. The results indicated that ω（As） in the top soil of the study area ranged from 2.70-97.0 mg·kg-1， with a spatial variation coefficient of 0.61 and weaker spatial continuity. The carcinogenic risk and hazard index obtained by the traditional risk assessment method were 2.12E-4 and 8.36， respectively， which obviously overestimated the actual risk level and were not conductive to the refined management of As-contaminated sites. Combined with modification of model parameters and probabilistic risk assessment， the non-carcinogenic risk for adults and children was found to be at an acceptable level， and the carcinogenic risk was reduced by nearly an order of magnitude compared to that in the conventional method. Considering the relative biological effectiveness （RBA） of As， the 95% quantile of the total carcinogenic risk was 1.24E-5， a reduction of up to 36.41% compared to the uncorrected corresponding risk value of 1.95E-5. The carcinogenic risk of soil As for adults and children in the study area exceeded acceptable risk levels 1E-6， with oral ingestion of soil being the primary route of exposure. In addition， the results of the sensitivity analysis of the parameters showed that As concentration， daily oral ingestion rate of soils， and exposure duration of children had relatively larger effects for health risks. This work will provide a methodological and theoretical basis for achieving accurate risk assessment of As-contaminated sites and provide concepts for refined risk management.

Ecotoxicological soil risk assessment under the new soil exposure framework - an impact assessment.

For the European risk assessment (RA) for soil organisms exposed to plant protection products (PPPs) endpoints from ecotoxicological laboratory studies are compared with predicted environmental concentrations in soil (PECSOIL) at first tier. A safety margin must be met; otherwise, a higher tier RA is triggered (usually soil organism field studies). A new tiered exposure modeling guidance was published by EFSA to determine PECSOIL. This work investigates its potential impact on future soil RA. PECSOIL values for >50 active substances and metabolites were calculated and compared with the respective endpoints for soil organisms to calculate the RA failure rate. Compared to the current (FOCUS) exposure modeling, PECSOIL values for all EU regulatory zones considerably increased, e.g., resulting in active substance RA failure rates of 67%, 58% and 36% for modeling Tier-1, Tier-2 and Tier-3A, respectively. The main driving factors for elevated PECSOIL were soil bulk density, crop interception and wash-off, next to obligatory modeling and scenario adjustment factors. Spatial PECSOIL scenario selection procedures result in agronomically atypical soil characteristics (e.g., soil bulk density values in Tier-3A scenarios far below typical European agricultural areas). Consequently, exposure modeling and ecotoxicological study characteristics are inconsistent, which hinders scientifically reasonable comparison of both in the RA.

Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients.

INTRODUCTION: The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm. METHODS: We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared. RESULTS: Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression. CONCLUSIONS: We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.

Effects of kinect-based virtual reality training on bone mineral density and fracture risk in postmenopausal women with osteopenia: a randomized controlled trial.

Osteopenia is a condition characterized by low bone mineral density (BMD) that increases fracture risk, particularly among postmenopausal women (PMW). This study aimed to determine the effects of Kinect-based VRT on BMD and fracture risk in PMW with osteopenia. The study was a prospective, two-arm, parallel-design, randomized controlled trial. The study enrolled 52 participants, 26 randomly assigned to each group. In the experimental group, Kinect-based VRT was provided thrice weekly for 24 weeks for 45 min/session. Both groups were instructed to engage in a daily 30-min walk outdoors. The fracture risk assessment tool (FRAX) was used to calculate fracture risk, and dual-energy X-ray absorptiometry was used to measure lumbar spine and femur neck BMD. Both variables were assessed at baseline and 24 weeks afterwards. After 24 weeks of Kinect-based VRT, the experimental group showed significant BMD increases in the right and left femoral necks and lumbar spine (p value < 0.001). In the control group, the BMD at the right and left femoral necks showed fewer significant changes (p value < 0.022 and 0.004, respectively). In the control group, lumbar spine BMD did not change (p = 0.57). The experimental group showed significantly lower FRAX scores for hip fracture prediction (HFP) and hip prediction of major osteoporotic (HPMO) at both femoral necks (p value < 0.001) than the control group (p = 0.05 and p = 0.01, respectively), but no significant change at the left femoral neck for HFP (p = 0.66) or HPMO (p = 0.26). These findings indicate that a Kinect-based VRT intervention resulted in significantly increased BMD and a reduced fracture risk, as predicted by HFP and HPMO measurements. These improvements were more pronounced in the experimental group than in the control group. Thus, Kinect-based VRT may be utilized as an effective intervention to improve BMD and reduce fracture risk in postmenopausal women with osteopenia.

Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure.

To fulfil the promise of reducing reliance on mammalian in vivo laboratory animal studies, new approach methods (NAMs) need to provide a confident basis for regulatory decision-making. However, previous attempts to develop in vitro NAMs-based points of departure (PODs) have yielded mixed results, with PODs from U.S. EPA's ToxCast, for instance, appearing more conservative (protective) but poorly correlated with traditional in vivo studies. Here, we aimed to address this discordance by reducing the heterogeneity of in vivo PODs, accounting for species differences, and enhancing the biological relevance of in vitro PODs. However, we only found improved in vitro-to-in vivo concordance when combining the use of Bayesian model averaging-based benchmark dose modeling for in vivo PODs, allometric scaling for interspecies adjustments, and human-relevant in vitro assays with multiple induced pluripotent stem cell-derived models. Moreover, the available sample size was only 15 chemicals, and the resulting level of concordance was only fair, with correlation coefficients <0.5 and prediction intervals spanning several orders of magnitude. Overall, while this study suggests several ways to enhance concordance and thereby increase scientific confidence in vitro NAMs-based PODs, it also highlights challenges in their predictive accuracy and precision for use in regulatory decision making.

Enhancing the evaluation of acute ischemic stroke risk in individuals with non-valvular atrial fibrillation by including laboratory indicators.

To investigate the clinical significance of the CHA2DS2-VASc-60 score, lipoprotein (a) [Lp(a)], red blood cell distribution width (RDW), and their combined effect in patients with non-valvular atrial fibrillation (NVAF) who experience acute ischemic stroke (AIS). This retrospective analysis was conducted on the clinical data of hospitalized patients with NVAF at the Third Affiliated Hospital of Anhui Medical University between April 1, 2020, and April 1, 2023. Based on the diagnosis of acute ischemic stroke (AIS), the patients were divided into two groups: the AIS group (150 cases of NVAF patients with comorbid AIS) and the non-AIS group (163 cases of NVAF patients without AIS). We performed CHA2DS2-VASc-60 scoring for all patients and collected their laboratory indicators and echocardiographic indicators during hospitalization. The study comprised 313 individuals with NVAF in total. There is a statistically significant difference (P < 0.05) in the comparison of CHA2DS2-VASc-60 score (5.68 ± 1.12 vs. 3.67 ± 1.47), Lp(a) [23.98 (13.28, 42.22) vs. 14.32 (7.96, 21.91)] and RDW (13.67 ± 1.25 vs. 12.94 ± 0.76) between NVAF patients with and without concomitant AIS. The results of the Spearman correlation analysis demonstrate a positive association between Lp(a) and RDW levels and both the CHA2DS2-VASc score and the CHA2DS2-VASc-60 score in patients with NVAF. Multivariate logistic regression analysis revealed that CHA2DS2-VASc-60 score [OR = 6.549, 95% CI: 4.110-10.433, P < 0.05], Lp(a) [OR = 1.023, 95% CI: 1.005-1.041, P < 0.05], and RDW [OR = 1.644, 95% CI: 1.071-2.525, P < 0.05] were independent risk factors for AIS in patients with non-valvular atrial fibrillation (NVAF). The receiver operator characteristic (ROC) curves showed that the area under the curve of CHA2DS2-VASc-60 score, Lp(a), RDW, and CHA2DS2-VASc-60 score combined with Lp(a) and RDW predicted that NVAF patients with AIS were 0.881 [95% CI: 0.804-0.906], 0.685 [95% CI: 0.626-0.744], 0.695 [95% CI: 0.637-0.754], and 0.906 [95% CI: 0.845-0.921], respectively. The CHA2DS2-VASc-60 score, Lp(a), and RDW were significantly increased in NVAF patients with AIS, which were independent risk factors for NVAF patients with AIS. The combination of the three has a high predictive capacity for NVAF patients with AIS.

Simple scores to predict 1-year mortality in atrial fibrillation.

BACKGROUND: Traditional scores as CHADS2 and CHA2DS2-Vasc are suitable for predicting stroke and systemic embolism in patients with atrial fibrillation (AF) and have shown to be also associated with mortality. Other more complex scores have been recommended for survival prediction. The purpose of our analysis was to test the performance of different clinical scores in predicting 1-year mortality in AF patients. MATERIAL AND METHODS: CHADS2 and CHA2DS2-Vasc scores were calculated for AF patients of the BLITZ-AF register and compared to R2-CHADS2, R2-CHA2DS2-Vasc and CHA2DS2VASc-RAF scores in predicting 1-year survival. Scores including renal function were calculated both with glomerular filtration rate (GFR) and creatinine clearance. RESULTS: One-year vital status (1960 alive and 199 dead) was available in 2159 patients. Receiver-operating characteristic curves displayed an association of each score to all-cause mortality, with R2(ClCrea)-CHADS2 being the best [area under the curve (AUC) 0.734]. Differences among the AUCs of the eight scores were not so evident, and a significant difference was found only between R2(ClCrea)-CHADS2 and CHADS2, CHA2DS2VASc, (ClCrea)-CHA2DS2-VASC-RAF.All the scores showed a similar performance for cardiovascular (CV) mortality, with CHA2DS2VASc-RAF being the best (AUC 0.757), with a significant difference with respect to CHADS2, CHA2DS2VASc, and (ClCrea)CHA2DS2Vasc-RAF. CONCLUSIONS: More complex scores, even if with better statistical performance, do not show a clinically relevant higher capability to discriminate alive or dead patients at 12 months. The classical and well known CHA2DS2VASc score, which is routinely used all around the world, has a high sensitivity in predicting all-cause mortality (AUC 0.695; Sensit. 80.4%) and CV mortality (AUC 0.691; Sensit. 80.0%). GRAPHICAL ABSTRACT: http://links.lww.com/JCM/A632.

Major influencing factors identification and probabilistic health risk assessment of soil potentially toxic elements pollution in coal and metal mines across China: A systematic review.

Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02-15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.