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.

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.

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.

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.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

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.

The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES.

Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.

Leveraging natural capital accounting to support businesses with nature-related risk assessments and disclosures.

Nature loss threatens businesses, the global economy and financial stability. Understanding and addressing these risks for business will require credible measurement approaches and data. This paper explores how natural capital accounting (NCA) can support business data and information needs related to nature, including disclosures aligned with the Taskforce on Nature-related Financial Disclosures recommendations. As businesses seek to measure, manage and disclose their nature-related risks and opportunities, they will need well-organized, consistent and high-quality information regarding their dependencies and impacts on nature, which few businesses currently collect or track in-house. NCA may be useful for these purposes but has not been widely used or applied by businesses. National NCA guided by the U.N. System of Environmental-Economic Accounting may provide: (i) a useful framework for businesses in conceptualizing, organizing and managing nature-related data and statistics; and (ii) data and information that can directly support business disclosures, corporate NCA and other business applications. This paper explores these opportunities as well as synergies between national and corporate natural capital accounts. In addition, the paper discusses key barriers to advancing the wider use and benefits of NCA for business, including: awareness of NCA, data access, business capabilities related to NCA, spatial and temporal scales of data, audit and assurance considerations, potential risks, and costs and incentives. This article is part of the theme issue 'Bringing nature into decision-making'.

Evaluating seismic risk by MCDM and machine learning for the eastern coast of India.

Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic induced hazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India.

Risk factors of in-hospital mortality and discriminating capacity of NIVO score in exacerbations of COPD requiring noninvasive ventilation.

BACKGROUND: Noninvasive mechanical ventilation (NIV) is recommended as the initial mode of ventilation to treat acute respiratory failure in patients with AECOPD. The Noninvasive Ventilation Outcomes (NIVO) score has been proposed to evaluate the prognosis in patients with AECOPD requiring assisted NIV. However, it is not validated in Chinese patients. METHODS: We used data from the MAGNET AECOPD Registry study, which is a prospective, noninterventional, multicenter, real-world study conducted between September 2017 and July 2021 in China. Data for the potential risk factors of mortality were collected and the NIVO score was calculated, and the in-hospital mortality was evaluated using the NIVO risk score. RESULTS: A total of 1164 patients were included in the study, and 57 patients (4.9%) died during their hospital stay. Multiple logistic regression analysis revealed that age ≥75 years, DBP <60 mmHg, Glasgow Coma Scale ≤14, anemia and BUN >7 mmol/L were independent predictors of in-hospital mortality. The in-hospital mortality was associated with an increase in the risk level of NIVO score and the difference was statistically significant (p < .001). The NIVO risk score showed an acceptable accuracy for predicting the in-hospital mortality in AECOPD requiring assisted NIV (AUC: 0.657, 95% CI: 0.584-0.729, p < .001). CONCLUSION: Our findings identified predictors of mortality in patients with AECOPD receiving NIV, providing useful information to identify severe patients and guide the management of AECOPD. The NIVO score showed an acceptable predictive value for AECOPD receiving NIV in Chinese patients, and additional studies are needed to develop and validate predictive scores based on specific populations.

Development and validation of a nomogram model for predicting the risk of MAFLD in the young population.

This study aimed to develop and validate a nomogram model that includes clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in young Chinese individuals. This study retrospectively analyzed a cohort of young population who underwent health examination from November 2018 to December 2021 at The Affiliated Hospital of Southwest Medical University in Luzhou City, Sichuan Province, China. We extracted the clinical and laboratory data of 43,040 subjects and randomized participants into the training and validation groups (7:3). Univariate logistic regression analysis, the least absolute shrinkage and selection operator regression, and multivariate logistic regression models identified significant variables independently associated with MAFLD. The predictive accuracy of the model was analyzed in the training and validation sets using area under the receiver operating characteristic (AUROC), calibration curves, and decision curve analysis. In this study, we identified nine predictors from 31 variables, including age, gender, body mass index, waist-to-hip ratio, alanine aminotransferase, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, uric acid, and smoking. The AUROC for the subjects in the training and validation groups was 0.874 and 0.875, respectively. The calibration curves show excellent accuracy of the nomogram. This nomogram which was based on demographic characteristics, lifestyle habits, anthropometrics, and laboratory data can visually and individually predict the risk of developing MAFLD. This nomogram is a quick and effective screening tool for assessing the risk of MAFLD in younger populations and identifying individuals at high risk of MAFLD, thereby contributing to the improvement of MAFLD management.

Interrater reliability of the violence risk assessment checklist for youth: a case vignette study.

BACKGROUND: Facilities providing health- and social services for youth are commonly faced with the need for assessment and management of violent behavior. These providers often experience shortage of resources, compromising the feasibility of conducting comprehensive violence risk assessments. The Violence Risk Assessment Checklist for Youth aged 12-18 (V-RISK-Y) is a 12-item violence risk screening instrument developed to rapidly identify youth at high risk for violent behavior in situations requiring expedient evaluation of violence risk. The V-RISK-Y instrument was piloted in acute psychiatric units for youth, yielding positive results of predictive validity. The aim of the present study was to assess the interrater reliability of V-RISK-Y in child and adolescent psychiatric units and acute child protective services institutions. METHODS: A case vignette study design was utilized to assess interrater reliability of V-RISK-Y. Staff at youth facilities (N = 163) in Norway and Sweden scored V-RISK-Y for three vignettes, and interrater reliability was assessed with the intraclass correlation coefficient (ICC). RESULTS: Results indicate good interrater reliability for the sum score and Low-Moderate-High risk level appraisal across staff from the different facilities and professions. For single items, interrater reliability ranged from poor to excellent. CONCLUSIONS: This study is an important step in establishing the psychometric properties of V-RISK-Y. Findings support the structured professional judgment tradition the instrument is based on, with high agreement on the overall risk assessment. This study had a case vignette design, and the next step is to assess the reliability and validity of V-RISK-Y in naturalistic settings.

[18F]FDG-PET/CT-based risk stratification in women with locally advanced uterine cervical cancer.

BACKGROUND: [18F]FDG-PET/CT is used for staging and treatment planning in patients with locally advanced cervical cancer (LACC). We studied if a PET-based prediction model could provide additional risk stratification beyond International Federation of Gynaecology and Obstetrics (FIGO) staging in our population with LACC to aid treatment decision making. METHODS: In total, 183 patients with LACC treated with chemoradiation between 2013 and 2018 were included. Patients were treated according to FIGO 2009 and retrospectively reclassified according to FIGO 2018 staging system. After validation of an existing PET-based prediction model, the predicted recurrent free survival (RFS), disease specific survival (DSS) and overall survival (OS) at 1, 3, and 5 years, based on metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax) and highest level of [18F]FDG-positive node was calculated. Then the observed survival was compared to the predicted survival. An area under the curve (AUC) close to or higher than 0.7 was considered adequate for accurate prediction. The Youden (J) index defined survival chance cutoff values for low and high risk groups. RESULTS: All AUC values for the comparison between predicted and observed outcomes were > 0.7 except for 5-year RFS and for 5-year OS which were close to 0.7 (0.684 and 0.650 respectively). Cutoff values for low and high risk survival chance were 0.44 for the 3-year RFS and 0.47 for the 5-year OS. The FIGO 2009 system could not differentiate between the risk profiles. After reclassification according to FIGO 2018, all patients with stage IIIC2 and IVB fell in the high risk and almost all patients with stages IB2-IIIB and IVA in the low risk group. In patients with stage IIIC1 disease the FIGO stage cannot discriminate between the risk profiles. CONCLUSIONS: Low and high risk patients with LACC can be identified with the PET-based prediction model. In particular patients with stage IIIC1 need additional risk stratification besides the FIGO 2018 staging. The Kidd model could be a useful tool to aid treatment decision making in these patients. Our results also support the choice of [18F]FDG-PET/CT imaging in patients with LACC.

Translating the COVID-19 epidemiological situation into policies and measures: the Belgian experience.

The COVID-19 pandemic led to sustained surveillance efforts, which made unprecedented volumes and types of data available. In Belgium, these data were used to conduct a targeted and regular assessment of the epidemiological situation. In addition, management tools were developed, incorporating key indicators and thresholds, to define risk levels and offer guidance to policy makers. Categorizing risk into various levels provided a stable framework to monitor the COVID-19 epidemiological situation and allowed for clear communication to authorities. Although translating risk levels into specific public health measures has remained challenging, this experience was foundational for future evaluation of the situation for respiratory infections in general, which, in Belgium, is now based on a management tool combining different data sources.

Cardiovascular risk screening: a 10-year prediction cross-sectional study in a Nigerian agrarian community.

Introduction: cardiovascular disease (CVD) is a major public health issue with a high global death rate and a significant death contribution from low-and middle-income countries. Modifiable and non-modifiable risk factors assessment and screening are important in their effective prevention and control. This study was designed to screen and assess cardiovascular risk factors in an agrarian community in Nigeria and to predict their 10-year CVD risk. Methods: this was a cross-sectional study carried out in the Umueri community in Anambra State, Nigeria. Each participant responded to an epidemiologic survey using the World Health Organization (WHO) cardiovascular risk factors assessment tool with point-of-care screening procedures. The risk assessment for 10-year CV risk was conducted using region-specific WHO/ISH charts. Patients´ characteristics were analyzed and presented in frequencies and percentages. Results: the mean age, systolic blood pressure, fasting plasma glucose, and total cholesterol of the study population were 54 years ± 1.27, 132 mmHg ± 2.088, 130 mg/dl ± 4.608, and 215 mg/dl ± 10.355 respectively. However, 98 (48.8%) have never had their blood pressure checked. About a quarter of the population had a high predicted risk of developing CVD within 10 years. Conclusion: most of the assessed cardiovascular risk factors in the community are on average above the normal ranges and their probability risk of developing CVD within the next 10 years is high.

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Evaluation of the RAPID score as a predictor of postoperative morbidity and mortality in patients undergoing pulmonary decortication for stage III pleural empyema.

OBJECTIVE: This study aims to correlate the RAPID score with the 3-month survival and surgical results of patients undergoing lung decortication with stage III pleural empyema. METHODS: This was a retrospective study with the population of patients with pleural empyema who underwent pulmonary decortication between January 2019 and June 2022. Data were collected from the institution's database, and patients were classified as low, medium, and high risk according to the RAPID score. The primary outcome was 3-month mortality. Secondary outcomes were the length of hospital stay, readmission rate, and the need for pleural re-intervention. RESULTS: Of the 34 patients with pleural empyema, according to the RAPID score, patients were stratified into low risk (23.5 %), medium risk (47.1 %), and high risk (29.4 %). The high-risk group had a 3-month mortality of 40 %, while the moderate-risk group had a 6.25 % and the low-risk group had no deaths within 90 days, confirming a good correlation with the RAPID score (p < 0.05). Sensitivity and specificity for the primary outcome in the high-risk score were 80.0 % and 79.3 %, respectively. The secondary outcomes did not reach statistical significance. CONCLUSIONS: In this retrospective series, the RAPID score had a good correlation with 3-month mortality in patients undergoing lung decortication. The morbidity indicators did not reach statistical significance. The present data justifies further studies to explore the capacity of the RAPID score to be used as a selection tool for treatment modality in patients with stage III pleural empyema.

Assessing the relationship between multimorbidity, NCD configurations, frailty phenotypes, and mortality risk in older adults.

BACKGROUND: Older adults are increasingly susceptible to prolonged illness, multiple chronic diseases, and disabilities, which can lead to the coexistence of multimorbidity and frailty. Multimorbidity may result in various noncommunicable disease (NCD) patterns or configurations that could be associated with frailty and death. Mortality risk may vary depending on the presence of specific chronic diseases configurations or frailty. METHODS: The aim was to examine the impact of NCD configurations on mortality risk among older adults with distinct frailty phenotypes. The population was analyzed from the Costa Rican Longevity and Healthy Aging Study Cohort (CRELES). A total of 2,662 adults aged 60 or older were included and followed for 5 years. Exploratory factor analysis and various clustering techniques were utilized to identify NCD configurations. The frequency of NCD accumulation was also assessed for a multimorbidity definition. Frailty phenotypes were set according to Fried et al. criteria. Kaplan‒Meier survival analyses, mortality rates, and Cox proportional hazards models were estimated. RESULTS: Four different types of patterns were identified: 'Neuro-psychiatric', 'Metabolic', 'Cardiovascular', and 'Mixt' configurations. These configurations showed a higher mortality risk than the mere accumulation of NCDs [Cardiovascular HR:1.65 (1.07-2.57); 'Mixt' HR:1.49 (1.00-2.22); ≥3 NCDs HR:1.31 (1.09-1.58)]. Frailty exhibited a high and constant mortality risk, irrespective of the presence of any NCD configuration or multimorbidity definition. However, HRs decreased and lost statistical significance when phenotypes were considered in the Cox models [frailty + 'Cardiovascular' HR:1.56 (1.00-2.42); frailty + 'Mixt':1.42 (0.95-2.11); and frailty + ≥ 3 NCDs HR:1.23 (1.02-1.49)]. CONCLUSIONS: Frailty accompanying multimorbidity emerges as a more crucial indicator of mortality risk than multimorbidity alone. Therefore, studying NCD configurations is worthwhile as they may offer improved risk profiles for mortality as alternatives to straightforward counts.

Replace the Linear No-threshold Model with a Risk-informed Targeted Approach to Radiation Protection.

ABSTRACT: The linear no-threshold (LNT) model may be useful as a simple basis for developing radiation protection regulations and standards, but it bears little resemblance to scientific reality and is probably overly conservative at low doses and low dose rates. This paper is an appeal for a broader view of radiation protection that involves more than just optimization of radiation dose. It is suggested that the LNT model should be replaced with a risk-informed, targeted approach to limitation of overall risks, which include radiation and other types of risks and accidents/incidents. The focus should be on protection of the individual. Limitation of overall risk does not necessarily always equate to minimization of individual or collective doses, but in some cases it might. Instead, risk assessment (hazards analysis) should be performed for each facility/and or specific job or operation (straightforward for specialized work such as radiography), and this should guide how limited resources are used to protect workers and the public. A graded approach could be used to prioritize the most significant risks and identify exposure scenarios that are unlikely or non-existent. The dose limits would then represent an acceptable level of risk, below which no further reduction in dose would be needed. Less resources should be spent on ALARA and tracking small individual and collective doses. Present dose limits are thought to be conservative and should suffice in general. Two exceptions are possibly the need for a lower (lifetime) dose limit for lens of the eye for astronauts and raising the public limit to 5 mSv y -1 from 1 mSv y -1 . This would harmonize the public limit with the current limit for the embryo fetus of the declared pregnant worker. Eight case studies are presented that emphasize how diverse and complex radiation risks can be, and in some cases, chemical and industrial risks outweigh radiation risks. More focus is needed on prevention of accidents and incidents involving a variety of types of risks. A targeted approach is needed, and commitments should be complied with until they are changed or exemptions are granted. No criticism of regulators or nuclear industry personnel is intended here. Protection of workers and the public is everyone's goal. The question is how best to accomplish that.

Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort.

Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.