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Information in the electronic health record (EHR), such as diagnoses, vital signs, utilization, medications, and laboratory values, may predict fractures well without the need to verbally ascertain risk factors. In our study, as a proof of concept, we developed and internally validated a fracture risk calculator using only information in the EHR., Purpose: Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model., Methods: Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX., Results: We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX., Conclusion: Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates., Competing Interests: Declarations. Conflicts of interest: None., (© 2024. International Osteoporosis Foundation and Bone Health and Osteoporosis Foundation.)

Background: Medical outcomes of interest to clinicians may have multiple categories. Researchers face several options for risk prediction of such outcomes, including dichotomized logistic regression and multinomial logit regression modeling. We aimed to compare these methods and provide guidance needed for practice., Methods: We described dichotomized logistic regression, multinomial continuation-ratio logit regression, which is an alternative to standard multinomial logit regression for ordinal outcomes, and logistic competing risks regression. We then applied these methods to develop prediction models of survival and neurodevelopmental outcomes based on the NICHD Extremely Preterm Birth Outcome Tool model. The statistical and practical advantages and flaws of these methods were examined. Both discrimination and calibration of the estimated logistic models of dichotomized outcomes and continuation-ratio logit model were assessed., Results: The dichotomized logistic models and multinomial continuation-ratio logit model had similar discrimination and calibration in predicting death and survival without neurodevelopmental impairment. But the continuation-ratio logit model had better discrimination and calibration in predicting neurodevelopmental impairment. The sum of predicted probabilities of outcome categories from the dichotomized logistic models could deviate from 100% substantially, ranging from 87.7 to 124.0%, and the dichotomized logistic model of neurodevelopmental impairment greatly overpredicted low risks and underpredicted high risks., Conclusions: Estimating multiple logistic regression models of dichotomized outcomes may result in poorly calibrated predictions for an outcome with multiple ordinal categories. Multinomial continuation-ratio logit regression produces better calibrated predictions, constrains the sum of predicted probabilities to 100%, and has the advantages of simplicity in model interpretation, flexibility to include outcome category-specific predictors and random-effect terms for patient heterogeneity by hospital. It also accounts for mutual dependence among multiple categories and accommodates competing risks., (© 2024. The Author(s).)

Objectives: Different tools to assess the potential risk of bias (RoB) for cross-sectional studies have been developed, but it is unclear whether all pertinent bias concepts are addressed. We aimed to identify RoB concepts applicable to cross-sectional research validity and to explore coverage for each in existing appraisal tools., Study Design and Setting: This scoping review followed the Joanna Briggs Institute methodology. We included records of any study design describing or reporting methods, concepts or tools used to consider RoB in health research reported to be descriptive/prevalence survey or analytic/association (cross-sectional) study designs. Synthesis included quantitative and qualitative analysis., Results: Of the 4556 records screened, 90 were selected for inclusion; 67 (74%) described the development of, or validation process for, appraisal tools, 15 (17%) described methodological content or theory relevant to RoB for cross-sectional studies and 8 (9%) records of methodological systematic reviews. Review of methodological reports identified important RoB concepts for both descriptive/prevalence and analytic/association studies. Tools identified (n = 64 unique tools) were either intended to appraise quality or assess RoB in multiple study designs including cross-sectional studies (n = 21; 33%) or cross-sectional designs alone (n = 43; 67%). Several existing tools were modified (n = 17; 27%) for application to cross-sectional studies. The RoB items most frequently addressed in the RoB tools were validity and reliability of the exposure (53%) or outcome (65%) measurement and representativeness of the study population (59%). Most tools did not consider nonresponse or missingness appropriately or at all., Conclusion: Assessing cross-sectional studies involve unique RoB considerations. We identified RoB tools designed for broad applicability across various study designs as well as those specifically tailored for cross-sectional studies. However, none of the identified tools comprehensively address all potential biases pertinent to cross-sectional studies. Our findings indicate a need for continued improvement of RoB tools and suggest that the development of context-specific or more precise tools for this study design may be necessary., Competing Interests: Declaration of competing interest There are no competing interests for any author., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The primary objective of this study was to evaluate the performance of traffic conflict measures for real-time crash risk prediction. Drone recordings were collected from a freeway section in Nanjing, China, over a year. Twenty rear-end crashes and their associated trajectories were obtained. Vehicle trajectories preceding the crash were segmented based on different time periods to represent varying crash conditions. The Extreme Value Theory (EVT) approach combined with a block maxima sampling method was then employed to investigate the generalized extreme value (GEV) distributions of extremely risky events under non-crash and crash conditions. The prediction performance was demonstrated by the differences in GEV distributions under these two conditions. Within the proposed modeling framework, the performances of Time-to-Collision (TTC), Deceleration Rate to Avoid a Crash (DRAC), and Absolute value of Derivative of Instantaneous Acceleration (ADIA) were examined and compared. The results revealed a decreasing trend in the prediction performances as the preceding time window before a crash increased. For any given length of crash conditions, TTC consistently outperformed DRAC and ADIA. Notably, TTC's reliability in crash risk prediction became more uncertain when forecasting crashes more than 2 s in advance. This study provided the optimal thresholds for TTC and ADIA for practical application in crash early warning. The methods and results in this study have the potential to be used for crash risk assessments in autonomous vehicles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Obesity constitutes a public health problem worldwide and causes non-alcoholic fatty liver disease (MALFD), the leading cause of liver disease in developed countries, which progresses to liver cirrhosis and liver cancer. MAFLD is associated with obesity and can be evaluated by validated formulas to assess MAFLD risk using different parameters such as the body mass index (BMI) and waist circumference (WC). However, these parameters do not accurately measure body fat. As MAFLD is strongly associated with obesity, we hypothesize that measuring body and visceral fat by electrical bioimpedance is an efficient method to predict the risk of MAFLD. The objective of our work was to demonstrate that electrical bioimpedance is a more efficient method than the BMI or WC to predict an elevated risk of MAFLD., Methods: A cross-sectional, descriptive study involving 8590 Spanish workers in the Balearic Islands was carried out. The study's sample of employees was drawn from those who underwent occupational medicine examinations between January 2019 and December 2020. Five MAFLD risk scales were determined for evaluating very high levels of body fat and visceral fat. The determination of body and visceral fat was performed using bioimpedanciometry. Student's t -test was employed to ascertain the mean and standard deviation of quantitative data. The chi-square test was used to find prevalences for qualitative variables, while ROC curves were used to define the cut-off points for body and visceral fat. The calculations included the area under the curve (AUC), the cut-off points along with their Youden index, sensitivity, and specificity. Correlation and concordance between the various scales were determined using Pearson's correlation index and Cohen's kappa, respectively., Results: As both total body fat and visceral fat increase, the risk of MAFLD increases with a statistically significant result ( p < 0.001), presenting a higher risk in men. The areas under the curve (AUC) of the five scales that assess overweight and obesity to determine the occurrence of high values of the different MAFLD risk scales were very high, most of them exceeding 0.9. These AUC values were higher for visceral and body fat than for the BMI or waist circumference. FLD-high presented the best results in men and women with the AUC at around 0.97, both for visceral fat and total body fat, with a high Youden index in all cases (women body fat = 0.830, visceral fat = 0.892; men body fat = 0.780, visceral fat = 0.881)., Conclusions: In our study, all the overweight and obesity scales show a very good association with the scales assessing the risk of MAFLD. These values are higher for visceral and body fat than for waist circumference and the BMI. Both visceral fat and body fat are better associated than the BMI and waist circumference with MAFLD risk scales.

This study shows that we can use synthetic cohorts created from medical risk calculators to gain insights into how risk estimations, clinical reasoning, data-driven subgrouping, and the confidence in risk calculator scores are connected. When prediction variables aren't evenly distributed in these synthetic cohorts, they can be used to group similar cases together, revealing new insights about how cohorts behave. We also found that the confidence in predictions made by these calculators can vary depending on patient characteristics. This suggests that it might be beneficial to include a "normalized confidence" score in future versions of these calculators for healthcare professionals. We plan to explore this idea further in our upcoming research., (© 2024. The Author(s).)

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Throughput needs, costs of time and resources, and concerns about the use of animals in hazard and safety assessment studies are fueling a growing interest in adopting new approach methodologies for use in product development and risk assessment. However, current efforts to define "next-generation risk assessment" vary considerably across commercial and regulatory sectors, and an a priori definition of the biological scope of data needed to assess hazards is generally lacking. We propose that the absence of clearly defined questions that can be answered during hazard assessment is the primary barrier to the generation of a paradigm flexible enough to be used across varying product development and approval decision contexts. Herein, we propose a biological questions-based approach (BQBA) for hazard and safety assessment to facilitate fit-for-purpose method selection and more efficient evidence-based decision-making. The key pillars of this novel approach are bioavailability, bioactivity, adversity, and susceptibility. This BQBA is compared with current hazard approaches and is applied in scenarios of varying pathobiological understanding and/or regulatory testing requirements. To further define the paradigm and key questions that allow better prediction and characterization of human health hazard, a multidisciplinary collaboration among stakeholder groups should be initiated., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society of Toxicology.)

Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research., Competing Interests: Declaration of interests RG is funded by the National Institute for Health and Care Research (NIHR) for this study (NIHR302604). TC is a co-founder of and owns stock in Mortimer Health and is supported by the Wellcome Trust via a Wellcome Clinical PhD Training Fellowship (REF 22890/Z/21/Z). SMJ is supported by Cancer Research UK (CRUK; EDDCPGM\100002) and Medical Research Council (MRC; MR/W025051/1) programme grants; received fees for advisory board membership in the last three years from Bard1 Lifescience; received grant income from Owlstone and GRAIL; is an unpaid member of a GRAIL advisory board; has received lecture fees for academic meetings from Cheisi and AstraZeneca; and receives support from the CRUK Lung Cancer Centre and the CRUK City of London Centre, the Rosetrees Trust, the Roy Castle Lung Cancer foundation, the Longfonds BREATH Consortia, MRC UKRMP2 Consortia, the Garfield Weston Trust, and University College London (UCL) Hospitals Charitable Foundation where this work was partly undertaken and who also received a proportion of funding from the Department of Health's NIHR Biomedical Research Centre's funding scheme. SMJ's wife works for AstraZeneca. MvdS is a Director at the Cambridge Centre for AI in Medicine, which receives funding from AstraZeneca and GSK. JS is funded by NIHR Applied Research Collaboration North Thames, NIHR (policy research programme, programme grant, health service and delivery grant, and capacity building funding), and the UK Prevention Research Partnership. NN is supported by an MRC Clinical Academic Research Partnership (MR/T02481X/1); received a grant from the CRUK early diagnosis grant for the Real-time cancer analytics (REACT) study (EICEDAAP\100012); reports honoraria for non-promotional educational talks or advisory boards from Amgen, AstraZeneca, Boehringer ingelheim, Bristol Myers Squibb, EQRx, Fujifilm, Guardant Health, Intuitive, Janssen, Lilly, MSD, Olympus, OncLive, PeerVoice, Pfizer, Roche, and Takeda outside of the current work; holds positions as the Director of UK Lung cancer coalition, member of the Steering Committee of British Thoracic Oncology Group, and Clinical Director of National Lung Cancer Audit; and reports stock and stock options in OneWelbeck, The Physicians’ Clinic (HCA), and Pharmacierge. AJ declares no competing interests. The views expressed in this publication are those of the authors and not necessarily those of the NIHR, National Health Service, or the UK Department of Health and Social Care., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Once an external factor has been deemed likely to influence human health and a dose response function is available, an assessment of its health impact or that of policies aimed at influencing this and possibly other factors in a specific population can be obtained through a quantitative risk assessment, or health impact assessment (HIA) study. The health impact is usually expressed as a number of disease cases or disability-adjusted life-years (DALYs) attributable to or expected from the exposure or policy. We review the methodology of quantitative risk assessment studies based on human data. The main steps of such studies include definition of counterfactual scenarios related to the exposure or policy, exposure(s) assessment, quantification of risks (usually relying on literature-based dose response functions), possibly economic assessment, followed by uncertainty analyses. We discuss issues and make recommendations relative to the accuracy and geographic scale at which factors are assessed, which can strongly influence the study results. If several factors are considered simultaneously, then correlation, mutual influences and possibly synergy between them should be taken into account. Gaps or issues in the methodology of quantitative risk assessment studies include 1) proposing a formal approach to the quantitative handling of the level of evidence regarding each exposure-health pair (essential to consider emerging factors); 2) contrasting risk assessment based on human dose-response functions with that relying on toxicological data; 3) clarification of terminology of health impact assessment and human-based risk assessment studies, which are actually very similar, and 4) other technical issues related to the simultaneous consideration of several factors, in particular when they are causally linked., (© 2024. The Author(s).)

Background: Predicting disability risk in healthy older adults in China is essential for timely preventive interventions, improving their quality of life, and providing scientific evidence for disability prevention. Therefore, developing a machine learning model capable of evaluating disability risk based on longitudinal research data is crucial., Methods: We conducted a prospective cohort study of 2,175 older adults enrolled in the China Health and Retirement Longitudinal Study (CHARLS) between 2015 and 2018 to develop and validate this prediction model. Several machine learning algorithms (logistic regression, k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, and XGBoost) were used to assess the 3-year risk of developing disability. The optimal cutoff points and adjustment parameters are explored in the training set, the prediction accuracy of the models is compared in the testing set, and the best-performing models are further interpreted., Results: During a 3-year follow-up period, a total of 505 (23.22%) healthy older adult individuals developed disabilities. Among the 43 features examined, the LASSO regression identified 11 features as significant for model establishment. When comparing six different machine learning models on the testing set, the XGBoost model demonstrated the best performance across various evaluation metrics, including the highest area under the ROC curve (0.803), accuracy (0.757), sensitivity (0.790), and F1 score (0.789), while its specificity was 0.712. The decision curve analysis (DCA) indicated showed that XGBoost had the highest net benefit in most of the threshold ranges. Based on the importance of features determined by SHAP (model interpretation method), the top five important features were identified as right-hand grip strength, depressive symptoms, marital status, respiratory function, and age. Moreover, the SHAP summary plot was used to illustrate the positive or negative effects attributed to the features influenced by XGBoost. The SHAP dependence plot explained how individual features affected the output of the predictive model., Conclusion: Machine learning-based prediction models can accurately evaluate the likelihood of disability in healthy older adults over a period of 3 years. A combination of XGBoost and SHAP can provide clear explanations for personalized risk prediction and offer a more intuitive understanding of the effect of key features in the model., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Han and Wang.)

Introduction: Surveys are commonly used to assess effectiveness of FDA-required risk evaluation and mitigation strategies (REMS) for drugs and biologics in the United States., Objective: The aim of this study was to assess the scientific rigor of REMS knowledge survey protocols submitted to FDA and compare protocols before and after FDA's 2012 public workshop and 2019 draft guidance., Method: A content analysis of index survey protocols submitted to FDA (2007-2020) for single-product REMS with elements to assure safe use (39 programs, 78 protocols) was conducted. Each protocol was scored against 52 core essential elements (CEE), abstracted from FDA's guidance and grouped into six domains: study objective (n = 5), study design (n = 18), survey instrument (n = 9), participant recruitment (n = 7), survey administration (n = 9), and statistical analysis plan (n = 4). Scores were collected by time periods: (A) Oct 2007 to Jul 2012; (B) Aug 2012 to Feb 2019; (C) Mar 2019 to Dec 2020; and compared using logistic generalized linear mixed models adjusting for domain, survey population, vendor, program, and protocol., Results: There were 30 (38.5%), 40 (51.3%), and 8 (10.3%) protocols submitted in time period A, B, and C, respectively. Adjusted marginal means of elements present (on the probability scale) by time period were 0.5816 (SE = 0.0242), 0.6429 (SE = 0.0229), and 0.7543 (SE = 0.0394). The likelihood of missing a CEE declined over time (adjusted p-value = 0.0094, time period A vs C). The statistical analysis plan domain had the most improvement; study design remained the weakest domain with the scientific justification CEE particularly underrepresented., Conclusion: The rigor of REMS knowledge survey protocols improved over time consistent with FDA's efforts to advance regulatory science, but gaps remain., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors., Methods: The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error., Results: Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level., Conclusion: In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level., Competing Interests: Conflicts of interest: The authors declare that they have no conflicts of interest related to the subject matter or materials discussed in this article., (Copyright © 2023, the Chinese Medical Association.)

To develop a risk assessment scale for pathological scarring and validate its psychometric properties. This was a methodological study. Researchers developed the scale based on a literature review, qualitative study and Delphi expert consultation. Subsequently, 409 patients participated in the study to test the psychometric properties of the scale. We evaluated construct validity, content validity, internal consistency reliability, and interrater reliability. The researchers developed a scale consisting of three dimensions and 12 items. Factor analysis extracted a total of four common factors that accounted for 62.22% of the total variance. The results revealed that the item-content validity index (I-CVI) ranged from 0.67 to 1, while the scale-content validity index (S-CVI) was 0.82. Internal consistency reliability: Cronbach's α of the items ranged from 0.67 to 0.76, while Cronbach's α of the whole scale was 0.74. Interrater reliability: the Kappa number was 0.73. The final scale showed adequate construct validity, content validity, and reliability. It is appropriate for use in research and clinical practice settings to identify patients with a risk of pathological scarring. Further study is needed to confirm the scale's validity and reliability in other settings and populations., (© 2023 The Authors. International Wound Journal published by Medicalhelplines.com Inc and John Wiley & Sons Ltd.)

Objective: Develop a stakeholder-informed ethical framework to provide practical guidance to health systems considering implementation of suicide risk prediction models., Methods: In this multi-method study, patients and family members participating in formative focus groups ( n  = 4 focus groups, 23 participants), patient advisors, and a bioethics consultant collectively informed the development of a web-based survey; survey results ( n  = 1,357 respondents) and themes from interviews with stakeholders (patients, health system administrators, clinicians, suicide risk model developers, and a bioethicist) were used to draft the ethical framework., Results: Clinical, ethical, operational, and technical issues reiterated by multiple stakeholder groups and corresponding questions for risk prediction model adopters to consider prior to and during suicide risk model implementation are organized within six ethical principles in the resulting stakeholder-informed framework. Key themes include: patients' rights to informed consent and choice to conceal or reveal risk (autonomy); appropriate application of risk models, data and model limitations and consequences including ambiguous risk predictors in opaque models (explainability); selecting actionable risk thresholds (beneficence, distributive justice); access to risk information and stigma (privacy); unanticipated harms (non-maleficence); and planning for expertise and resources to continuously audit models, monitor harms, and redress grievances (stewardship)., Conclusions: Enthusiasm for risk prediction in the context of suicide is understandable given the escalating suicide rate in the U.S. Attention to ethical and practical concerns in advance of automated suicide risk prediction model implementation may help avoid unnecessary harms that could thwart the promise of this innovation in suicide prevention. HIGHLIGHTSPatients' desire to consent/opt out of suicide risk prediction models.Recursive ethical questioning should occur throughout risk model implementation.Risk modeling resources are needed to continuously audit models and monitor harms.

Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not employed in countries with high patient volumes. Meanwhile, the Japanese government conducted this operation, thereby contributing to the control of infections, at the cost of arduous manual labor by public health officials. To ease the burden of the officials, this study attempted to automate the assessment of each person's infection risk through an ontology, called COVID-19 Infection Risk Ontology (CIRO). This ontology expresses infection risks of COVID-19 formulated by the Japanese government, toward automated assessment of infection risks of individuals, using Resource Description Framework (RDF) and SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we demonstrated that the knowledge graph built could infer the risks, formulated by the government. Moreover, we conducted reasoning experiments to analyze the computational efficiency. The experiments demonstrated usefulness of the knowledge processing, and identified issues left for deployment., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Egami et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Structure activity relationship (SAR)-based read-across often is an integral part of toxicological safety assessment, and justification of the prediction presents the most challenging aspect of the approach. It has been established that structural consideration alone is inadequate for selecting analogues and justifying their use, and biological relevance must be incorporated. Here we introduce an approach for considering biological and toxicological related features quantitatively to compute a similarity score that is concordant with suitability for a read-across prediction for systemic toxicity. Fingerprint keys for comparing metabolism, reactivity, and physical chemical properties are presented and used to compare these attributes for 14 case study chemicals each with a list of potential analogues. Within each case study, the sum of these nonstructural similarity scores is consistent with suitability for read-across established using an approach based on expert judgment. Machine learning is applied to determine the contributions from each of the similarity attributes revealing their importance for each structure class. This approach is used to quantify and communicate the differences between a target and a potential analogue as well as rank analogue quality when more than one is relevant. A numerical score with easily interpreted fingerprints increases transparency and consistency among experts, facilitates implementation by others, and ultimately increases chances for regulatory acceptance.

As influenza A viruses (IAV) continue to cross species barriers and cause human infection, the establishment of risk assessment rubrics has improved pandemic preparedness efforts. In vivo pathogenicity and transmissibility evaluations in the ferret model represent a critical component of this work. As the relative contribution of in vitro experimentation to these rubrics has not been closely examined, we sought to evaluate to what extent viral titer measurements over the course of in vitro infections are predictive or correlates of nasal wash and tissue measurements for IAV infections in vivo . We compiled data from ferrets inoculated with an extensive panel of over 50 human and zoonotic IAV (inclusive of swine-origin and high- and low-pathogenicity avian influenza viruses associated with human infection) under a consistent protocol, with all viruses concurrently tested in a human bronchial epithelial cell line (Calu-3). Viral titers in ferret nasal wash specimens and nasal turbinate tissue correlated positively with peak titer in Calu-3 cells, whereas additional phenotypic and molecular determinants of influenza virus virulence and transmissibility in ferrets varied in their association with in vitro viral titer measurements. Mathematical modeling was used to estimate more generalizable key replication kinetic parameters from raw in vitro viral titers, revealing commonalities between viral infection progression in vivo and in vitro . Meta-analyses inclusive of IAV that display a diverse range of phenotypes in ferrets, interpreted with mathematical modeling of viral kinetic parameters, can provide critical information supporting a more rigorous and appropriate contextualization of in vitro experiments toward pandemic preparedness. IMPORTANCE Both in vitro and in vivo models are employed for assessing the pandemic potential of novel and emerging influenza A viruses in laboratory settings, but systematic examinations of how well viral titer measurements obtained in vitro align with results from in vivo experimentation are not frequently performed. We show that certain viral titer measurements following infection of a human bronchial epithelial cell line are positively correlated with viral titers in specimens collected from virus-inoculated ferrets and employ mathematical modeling to identify commonalities between viral infection progression between both models. These analyses provide a necessary first step in enhanced interpretation and incorporation of in vitro -derived data in risk assessment activities and highlight the utility of employing mathematical modeling approaches to more closely examine features of virus replication not identifiable by experimental studies alone.

The assessment of human health hazards posed by chemicals traditionally relies on toxicity studies in experimental animals. However, most chemicals currently in commerce do not meet the minimum data requirements for hazard identification and dose-response analysis in human health risk assessment. Previously, we introduced a read-across framework designed to address data gaps for screening-level assessment of chemicals with insufficient in vivo toxicity information (Wang et al., 2012). It relies on inference by analogy from suitably tested source analogues to a target chemical, based on structural, toxicokinetic, and toxicodynamic similarity. This approach has been used for dose-response assessment of data-poor chemicals relevant to the U.S. EPA's Superfund program. We present herein, case studies of the application of this framework, highlighting specific examples of the use of biological similarity for chemical grouping and quantitative read-across. Based on practical knowledge and technological advances in the fields of read-across and predictive toxicology, we propose a revised framework. It includes important considerations for problem formulation, systematic review, target chemical analysis, analogue identification, analogue evaluation, and incorporation of new approach methods. This work emphasizes the integration of systematic methods and alternative toxicity testing data and tools in chemical risk assessment to inform regulatory decision-making., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Purpose: This study aimed to adapt the Wilson-Sims Fall Scale to Turkish and assess the levels of sensitivity and selectivity., Design and Methods: The scale consisting of two sections and 15 items including age, gender, mental and physical status, elimination, impairments, gait/balance, falls' history, medications, and detox protocol was administered to 750 patients in psychiatric clinic., Findings: The kappa coefficient of the study (K: 0.44, p < .000) showed a moderate agreement. The sensitivity and selectivity rates of the scale were 78.5% and 77.6%, respectively. Positive and negative predictive values were 6.25% and 99.4%., Practice Implications: The scale has acceptable sensitivity and selectivity values. It is best practice to use both Wilson-Sims and Itaki Scale simultaneously for identification of patients at risk of falling., (© 2021 Wiley Periodicals LLC.)

Background: Older people (≥65 yr) are at increased risk of morbidity and mortality after emergency general surgery. Risk prediction models are needed to guide decision making in this high-risk population. Existing models have substantial limitations and lack external validation, potentially limiting their applicability in clinical use. We aimed to derive and validate, both internally and externally, a multivariable model to predict 30-day mortality risk in older patients undergoing emergency general surgery., Methods: After protocol publication, we used the National Surgical Quality Improvement Program (NSQIP) database (2012-6; estimated to contain 90% data from the USA and 10% from Canada) to derive and internally validate a model to predict 30-day mortality for older people having emergency general surgery using logistic regression with elastic net regularisation. Internal validation was done with 10-fold cross-validation. External validation was done using a temporally separate health administrative database exclusively from Ontario, Canada., Results: Overall, 6012 (12.0%) of the 50 221 patients died within 30 days. The model demonstrated strong discrimination (area under the curve [AUC]=0.871) and calibration across the spectrum of observed and predicted risks. Ten-fold internal cross-validation demonstrated minimal optimism (AUC=0.851, optimism 0.019 [standard deviation=0.06]) with excellent calibration. External validation demonstrated lower discrimination (AUC=0.700) and degraded calibration., Conclusion: A multivariable mortality risk prediction model was strongly discriminative and well calibrated internally. However, poor external validation suggests the model may not be generalisable to non-NSQIP data and hospitals. The findings highlight the importance of external validation before clinical application of risk models., Competing Interests: Declarations of interest There are no conflicts of interest to declare., (Copyright © 2022. Published by Elsevier Ltd.)

Considering the chemical complexity and toxicity data gaps of environmental mixtures, most studies evaluate the chemical risk individually. However, humans are usually exposed to a cocktail of chemicals in real life. Mixture health assessment remains to be a research area having significant knowledge gaps. Characterization of chemical composition and bioactivity/toxicity are the two critical aspects of mixture health assessments. This review seeks to introduce the recent progress and tools for the chemical and biological characterization of environmental mixtures. The state-of-the-art techniques include the sampling, extraction, rapid detection methods, and the in vitro, in vivo, and in silico approaches to generate the toxicity data of an environmental mixture. Application of these novel methods, or new approach methodologies (NAMs), has increased the throughput of generating chemical and toxicity data for mixtures and thus refined the mixture health assessment. Combined with computational methods, the chemical and biological information would shed light on identifying the bioactive/toxic components in an environmental mixture., (Copyright © 2022 Elsevier B.V. All rights reserved.)

There is increasing interest in using modern 'omics technologies, such as whole transcriptome sequencing, to inform decisions about human health safety and chemical toxicity hazard. High throughput methodologies using in vitro assays offer a path forward in reducing or eliminating animal testing. However, many aspects of these technologies need assessment before they will gain the trust of regulators and the public as viable alternative test methods for human health and safety. We used a high throughput whole transcriptome sequence assay (TempO-Seq) to assess the use of three widely used cancer cell lines (HepG2, MCF7, and Ishikawa cells) as in vitro systems for determination of cellular modes of action for two well studied compounds with canonical liver responses: ketoconazole and phenobarbital. We evaluated transcriptomic data to infer points of departure for use in risk analyses of compounds. Both compounds displayed shortcomings in evidence for canonical liver-related responses in any cell line, despite a strong dose response in all three. This raises questions about the competence of simple, mono-cultured cancer cell lines as appropriate surrogates for some adverse effects or toxic endpoints. Points of departure derived from benchmark doses were highly consistent across all three cell lines however, indicating the use of transcriptomic BMD analyses for such purposes would be a reliable and consistent approach., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Exposure to different nanoforms (NFs) via the dermal route is expected in occupational and consumer settings and thus it is important to assess their dermal toxicity and the contribution of dermal exposure to systemic bioavailability. We have formulated four grouping hypotheses for dermal toxicity endpoints which allow NFs to be grouped to streamline and facilitate risk assessment. The grouping hypotheses are developed based on insight into how physicochemical properties of NFs (i.e. composition, dissolution kinetics, size, and flexibility) influence their fate and hazard following dermal exposure. Each hypothesis is accompanied by a tailored Integrated Approach to Testing and Assessment (IATA) that is structured as a decision tree and tiered testing strategies (TTS) for each relevant question (at decision nodes) that indicate what information is needed to guide the user to accept or reject the grouping hypothesis. To develop these hypotheses and IATAs, we gathered and analyzed existing information on skin irritation, skin sensitization, and dermal penetration of NFs from the published literature and performed experimental work to generate data on NF dissolution in sweat simulant fluids. We investigated the dissolution of zinc oxide and silicon dioxide NFs in different artificial sweat fluids, demonstrating the importance of using physiologically relevant conditions for dermal exposure. All existing and generated data informed the formulation of the grouping hypotheses, the IATAs, and the design of the TTS. It is expected that the presented IATAs will accelerate the NF risk assessment for dermal toxicity via the application of read-across.

Background: The postoperative delirium is a common yet serious complication in elderly patients with hip fracture. We aimed to evaluate the potential risk factors of delirium in patients with hip fracture, to provide reliable evidence to the clinical management of hip fracture., Methods: This study was a retrospective design. Elderly patients who underwent hip fracture surgery in our hospital from June 1, 2019 to December 30, 2020 were selected. The characteristics and treatment data of delirium and no delirium patients were collected and compared. Multivariate logistic regression analysis was performed to analyze the influencing factors affecting postoperative delirium in elderly patients with hip fracture., Results: A total of 245 patients with hip fracture were included, the incidence of postoperative delirium in patients with hip fracture was 13.06%. There were significant differences in the age, BMI, history of delirium, estimated blood loss and duration of surgery (all p  < 0.05). There were significant differences in the albumin and TSH between delirium and no delirium group (all p < 0.05), Logistics analyses indicated that age ≥ 75 years (OR 3.112, 95% CI 1.527-5.742), BMI ≥ 24 kg/m 2 (OR 2.127, 95% CI 1.144-3.598), history of delirium (OR 1.754, 95% CI 1.173-2.347), estimated blood loss ≥ 400 mL (OR 1.698, 95% CI 1.427-1.946), duration of surgery ≥ 120 min (OR 2.138, 95% CI 1.126-3.085), preoperative albumin ≤ 40 g/L (OR 1.845, 95% CI 1.102-2.835) and TSH ≤ 2 mU/L (OR 2.226, 95% CI 1.329-4.011) were the independent risk factors of postoperative delirium in patients with hip fracture(all p < 0.05)., Conclusions: Postoperative delirium is very common in elderly patients with hip fracture, and it is associated with many risk factors, clinical preventions targeted on those risk factors are needed to reduce the postoperative delirium., (© 2022. The Author(s).)

Background: Malignant ovarian germ cell tumors (MOGCTs) are rare gynecologic neoplasms. The use of nomograms that are based on various clinical indicators to predict the prognosis of MOGCTs are currently lacking., Methods: Clinical and demographic information of patients with MOGCT recorded between 2004 and 2015 were obtained from the Surveillance, Epidemiology, and End Results database, and Cox regression analysis was performed to screen for important independent prognostic factors. Prognostic factors were used to construct predictive calculational charts for 1-year, 3-year, and 5-year overall survival (OS). The externally validated case cohort included a total of 121 MOGCT patients whose data were recorded from 2008 to 2019 from the database of the Shengjing Hospital of China Medical University., Results: A total of 1401 patients with MOGCT were recruited for the study. A nomogram was used to forecast the 1-year, 3-year, and 5-year OS using data pertaining to age, International Federation of Gynecology and Obstetrics (FIGO) staging, histological subtype and grade, and surgical type. Nomograms have a more accurate predictive ability and clinical utility than FIGO staging alone. Internal and external validation also demonstrated satisfactory consistency between projected and actual OS., Conclusions: A nomogram constructed using multiple clinical indicators provided a more accurate prognosis than FIGO staging alone. This nomogram may assist clinicians in identifying patients who are at increased risk, thus implementing individualized treatment regimens., (© 2022. The Author(s).)

Background: Increased elastase activity in α 1 -antitrypsin deficiency may affect elasticity of the arterial walls, and thereby blood pressure and susceptibility to cardiovascular disease. We hypothesized that severe α 1 -antitrypsin deficiency is associated with reduced blood pressure and susceptibility to cardiovascular disease., Methods: We genotyped 91,353 adults randomly selected from the Danish general population and 187 patients from the Danish α 1 -Antitrypsin Deficiency Registry and recorded baseline blood pressure, baseline plasma lipids and cardiovascular events during follow-up. 185 participants carried the ZZ genotype, 207 carried the SZ genotype and 91,148 carried the MM genotype., Results: α 1 -Antitrypsin deficiency was associated with decreases in blood pressure of up to 5 mmHg for systolic blood pressure and up to 2 mmHg for diastolic blood pressure, in ZZ vs SZ vs MM individuals (trend test, P's ≤ 0.01). Plasma triglycerides and remnant cholesterol were reduced in ZZ individuals compared with MM individuals (t-test, P's < 0.001). α 1 -Antitrypsin deficiency was associated with lower risk of myocardial infarction (trend test P = 0.03), but not with ischemic heart disease, ischemic cerebrovascular disease or hypertension (trend test, P's ≥ 0.59). However, when results for ischemic heart disease were summarized in meta-analysis with results from four previous studies, individuals with versus without α 1 -antitrypsin deficiency had an odds ratio for ischemic heart disease of 0.66 (95% CI:0.53-0.84)., Conclusions: Individuals with severe α 1 -antitrypsin deficiency have lower systolic and diastolic blood pressure, lower plasma triglycerides and remnant cholesterol, reduced risk of myocardial infarction, and a 34% reduced risk of ischemic heart disease., (© 2022. The Author(s).)

Background and Objective: Chronic obstructive pulmonary disease (COPD) patients with a body mass index (BMI) < 25 kg/m 2 are prone to develop adverse event of pharmacological treatment for frequent exacerbation. As chronic bronchitis (CB) is one of the strong risk factors of exacerbation, we investigated the associations between BMI and COPD exacerbations in patients with CB., Methods: Patients with COPD were included from the Korean COPD Subgroup Study (KOCOSS), a multicenter observational cohort study. CB was defined using the St. George's Respiratory Questionnaire and the participants were categorized according to BMI cut-off of 25 kg/m 2 . Exacerbations during a 1-year follow-up were compared among four groups: non-CB with BMI ≥ 25 kg/m 2 , non-CB with BMI < 25 kg/m 2 , CB with BMI ≥ 25 kg/m 2 , and CB with BMI < 25 kg/m 2 ., Results: Among the 1264 patients with COPD, 451 (35.7%) had CB and 353 (27.9%) had both CB and BMI < 25 kg/m 2 . The COPD exacerbation risk increased across the non-CB with BMI < 25 kg/m 2 , CB with BMI ≥ 25 kg/m 2 , and CB with BMI < 25 kg/m 2 groups (adjusted incidence rate ratio [95% confidence interval] 1.21 [0.89-1.62], 1.20 [0.77-1.88], and 1.41 [1.02-1.91], respectively, compared to the non-CB with BMI ≥ 25 kg/m 2 group)., Conclusions: COPD patients having both CB and a BMI < 25 kg/m 2 are at higher risk of exacerbations. Considering that a BMI < 25 kg/m 2 often limits treatment options preventing exacerbations, modified guidelines might be needed for non-obese CB patients in Asia., (© 2022. The Author(s).)

Background: Inflammation plays a significant role in tumour development, progression, and metastasis. In this study, we focused on comparing the predictive potential of inflammatory markers for overall survival (OS), recurrence-free survival (RFS), and 1- and 2-year RFS in hepatocellular carcinoma (HCC) patients., Methods: A total of 360 HCC patients were included in this study. A LASSO regression analysis model was used for data dimensionality reduction and element selection. Univariate and multivariate Cox regression analyses were performed to identify the independent risk factors for HCC prognosis. Nomogram prediction models were established and decision curve analysis (DCA) was conducted to determine the clinical utility of the nomogram model., Results: Multivariate Cox regression analysis indicated that the prognostic nutritional index (PNI) and neutrophil-to-lymphocyte ratio (NLR) were independent prognostic factors of OS, and aspartate aminotransferase-to-platelet ratio (APRI) was a common independent prognostic factor among RFS, 1-year RFS, and 2-year RFS. The systemic inflammation response index (SIRI) was an independent prognostic factor for 1-year RFS in HCC patients after curative resection. Nomograms established and achieved a better concordance index of 0.772(95% CI: 0.730-0.814), 0.774(95% CI: 0.734-0.815), 0.809(95% CI: 0.766-0.852), and 0.756(95% CI: 0.696-0.816) in predicting OS, RFS, 1-year RFS, and 2-year RFS respectively. The risk scores calculated by nomogram models divided HCC patients into high-, moderate- and low-risk groups (P < 0.05). DCA analysis revealed that the nomogram models could augment net benefits and exhibited a wider range of threshold probabilities in the prediction of HCC prognosis., Conclusions: The nomograms showed high predictive accuracy for OS, RFS, 1-year RFS, and 2-year RFS in HCC patients after surgical resection. The nomograms could be useful clinical tools to guide a rational and personalized treatment approach and prognosis judgement., (© 2022. The Author(s).)

Background: Biochemical recurrence (BCR) after initial treatment, such as radical prostatectomy, is the most frequently adopted prognostic factor for patients who suffer from prostate cancer (PCa). In this study, we aimed to construct a prognostic model consisting of gene expression profiles to predict BCR-free survival., Methods: We analyzed 70 metabolic pathways in 152 normal prostate samples and 494 PCa samples from the UCSC Xena dataset (training set) via gene set enrichment analysis (GSEA) to select BCR-related genes and constructed a BCR-related gene risk score (RS) model. We tested the power of our model using Kaplan-Meier (K-M) plots and receiver operator characteristic (ROC) curves. We performed univariate and multivariate analyses of RS using other clinicopathological features and established a nomogram model, which has stronger prediction ability. We used GSE70770 and DFKZ 2018 datasets to validate the results. Finally, we performed differential expression and quantitative real-time polymerase chain reaction analyses of the UCSC data for further verification of the findings., Results: A total of 194 core enriched genes were obtained through GSEA, among which 16 BCR-related genes were selected and a three-gene RS model based on the expression levels of CA14, LRAT, and MGAT5B was constructed. The outcomes of the K-M plots and ROC curves verified the accuracy of the RS model. We identified the Gleason score, pathologic T stage, and RS model as independent predictors through univariate and multivariate Cox analyses and constructed a nomogram model that presented better predictability than the RS model. The outcomes of the validation set were consistent with those of the training set. Finally, the results of differential expression analyses support the effectiveness of our model., Conclusion: We constructed an RS model based on metabolic genes that could predict the prognosis of PCa patients. The model can be easily used in clinical applications and provide important insights into future research on the underlying mechanism of PCa., (© 2022. The Author(s).)

The aim of this paper was to investigate the current status of read-across approaches in the Republic of Korea, Japan, and China in terms of applications and regulatory acceptance. In the Republic of Korea, over the last 6 years, approximately 8% of safety data records used for chemical registrations were based upon read-across, and a guideline published on the use of read-across results in 2017. In Japan, read-across is generally accepted for screening hazard classification of toxicological endpoints according to the Chemical Substances Control Law (CSCL). In China, read-across data, along with data from other animal alternatives are accepted as a data source for chemical registrations, but could be only considered when testing is not technically feasible. At present, read-across is not widely used for chemical registrations and regulatory acceptance of read-across may differ among countries in Asia. With consideration of the advantages and limitations of read-across, it is expected that read-across may soon gradually be employed in Asian countries. Thus, regulatory agencies need to prepare for this progression.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: KS is CEO of Lipotype GmbH. KS and CK are shareholders of Lipotype GmbH. CL and MJG are employees of Lipotype GmbH.

Aim: To clinically validate the 40-gene expression profile (40-GEP) test for cutaneous squamous cell carcinoma patients and evaluate coupling the test with individual clinicopathologic risk factor-based assessment methods. Patients & methods: In a 33-site study, primary tumors with known patient outcomes were assessed under clinical testing conditions (n = 420). The 40-GEP results were integrated with clinicopathologic risk factors. Kaplan-Meier and Cox regression analyses were performed for metastasis. Results: The 40-GEP test demonstrated significant prognostic value. Risk classification was improved via integration of 40-GEP results with clinicopathologic risk factor-based assessment, with metastasis rates near the general cutaneous squamous cell carcinoma population for class 1 and ≥50% for class 2B. Conclusion: Combining molecular profiling with clinicopathologic risk factor assessment enhances stratification of cutaneous squamous cell carcinoma patients and may inform decision-making for risk-appropriate management strategies.

There exists consensus that the traditional means by which safety of chemicals is assessed-namely through reliance upon apical outcomes obtained following in vivo testing-is increasingly unfit for purpose. Whilst efforts in development of suitable alternatives continue, few have achieved levels of robustness required for regulatory acceptance. An array of "new approach methodologies" (NAM) for determining toxic effect, spanning in vitro and in silico spheres, have by now emerged. It has been suggested, intuitively, that combining data obtained from across these sources might serve to enhance overall confidence in derived judgment. This concept may be formalised in the "tiered assessment" approach, whereby evidence gathered through a sequential NAM testing strategy is exploited so to infer the properties of a compound of interest. Our intention has been to provide an illustration of how such a scheme might be developed and applied within a practical setting-adopting for this purpose the endpoint of rat acute oral lethality. Bayesian statistical inference is drawn upon to enable quantification of degree of confidence that a substance might ultimately belong to one of five LD50-associated toxicity categories. Informing this is evidence acquired both from existing in silico and in vitro resources, alongside a purposely-constructed random forest model and structural alert set. Results indicate that the combination of in silico methodologies provides moderately conservative estimations of hazard, conducive for application in safety assessment, and for which levels of certainty are defined. Accordingly, scope for potential extension of approach to further toxicological endpoints is demonstrated., (© 2022. The Author(s).)

Background: The Trauma and Injury Severity Score (TRISS) uses anatomical and physiologic variables to predict mortality. Elderly (65 years or older) trauma patients have increased mortality and morbidity for a given TRISS, in part because of functional status and comorbidities. These factors are incorporated into the American Society of Anesthesiologists Physical Status (ASA-PS) and National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC). We hypothesized scoring tools using comorbidities and functional status to be superior at predicting mortality, hospital length of stay (LOS), and complications in elderly trauma patients undergoing operation., Methods: Four level I trauma centers prospectively collected data on elderly trauma patients undergoing surgery within 24 hours of admission. Using logistic regression, five scoring models were compared: ASA-PS, NSQIP-SRC, TRISS, TRISS-ASA-PS, and TRISS-NSQIP-SRC.Brier scores and area under the receiver operator characteristics curve were calculated to compare mortality prediction. Adjusted R2 and root mean squared error were used to compare LOS and predictive ability for number of complications., Results: From 122 subjects, 9 (7.4%) died, and the average LOS was 12.9 days (range, 1-110 days). National Surgical Quality Improvement Program Surgical Risk Calculator was superior to ASA-PS and TRISS at predicting mortality (area under the receiver operator characteristics curve, 0.978 vs. 0.768 vs. 0.903; p = 0.007). Furthermore, NSQIP-SRC was more accurate predicting LOS (R2, 25.9% vs. 13.3% vs. 20.5%) and complications (R2, 34.0% vs. 22.6% vs. 29.4%) compared with TRISS and ASA-PS. Adding TRISS to NSQIP-SRC improved predictive ability compared with NSQIP-SRC alone for complications (R2, 35.5% vs. 34.0%; p = 0.046). However, adding ASA-PS or TRISS to NSQIP-SRC did not improve the predictive ability for mortality or LOS., Conclusion: The NSQIP-SRC, which includes comorbidities and functional status, had superior ability to predict mortality, LOS, and complications compared with TRISS alone in elderly trauma patients undergoing surgery., Level of Evidence: Prognostic and Epidemiologic; Level III., (Copyright © 2021 American Association for the Surgery of Trauma.)

Organ-on-chip (OoC) technology is full of engineering and biological challenges, but it has the potential to revolutionize the Next-Generation Risk Assessment of novel ingredients for consumer products and chemicals. A successful incorporation of OoC technology into the Next-Generation Risk Assessment toolbox depends on the robustness of the microfluidic devices and the organ tissue models used. Recent advances in standardized device manufacturing, organ tissue cultivation and growth protocols offer the ability to bridge the gaps towards the implementation of organ-on-chip technology. Next-Generation Risk Assessment is an exposure-led and hypothesis-driven tiered approach to risk assessment using detailed human exposure information and the application of appropriate new (non-animal) toxicological testing approaches. Organ-on-chip presents a promising in vitro approach by combining human cell culturing with dynamic microfluidics to improve physiological emulation. Here, we critically review commercial organ-on-chip devices, as well as recent tissue culture model studies of the skin, intestinal barrier and liver as the main metabolic organ to be used on-chip for Next-Generation Risk Assessment. Finally, microfluidically linked tissue combinations such as skin-liver and intestine-liver in organ-on-chip devices are reviewed as they form a relevant aspect for advancing toxicokinetic and toxicodynamic studies. We point to recent achievements and challenges to overcome, to advance non-animal, human-relevant safety studies., (© 2022. The Author(s).)

Background: Assessing frailty in patients with an acute trauma can be challenging. To provide trustworthy results, tools should be feasible and reliable. This systematic review evaluated existing evidence on the feasibility and reliability of frailty assessment tools applied in acute in-hospital trauma patients., Methods: A systematic search was conducted in relevant databases until February 2020. Studies evaluating the feasibility and/or reliability of a multidimensional frailty assessment tool used to identify frail trauma patients were identified. The feasibility and reliability results and the risk of bias of included studies were assessed. This study was conducted and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and registered in Prospective Register of Systematic Reviews (ID: CRD42020175003)., Results: Nineteen studies evaluating 12 frailty assessment tools were included. The risk of bias of the included studies was fair to good. The most frequently evaluated tool was the Clinical Frailty Scale (CFS) (n = 5). All studies evaluated feasibility in terms of the percentage of patients for whom frailty could be assessed; feasibility was high (median, 97%; range, 49-100%). Other feasibility aspects, including time needed for completion, tool availability and costs, availability of instructions, and necessity of training for users, were hardly reported. Reliability was only assessed in three studies, all evaluating the CFS. The interrater reliability varied between 42% and >90% agreement, with a Krippendorff α of 0.27 to 0.41., Conclusion: Feasibility of most instruments was generally high. Other aspects were hardly reported. Reliability was only evaluated for the CFS with results varying from poor to good. The reliability of frailty assessment tools for acute trauma patients needs further critical evaluation to conclude whether assessment leads to trustworthy results that are useful in clinical practice., Level of Evidence: Systematic review, Level II., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: The purpose of this study was to assess the medical history of adenoviral keratoconjunctivitis (AK) and subepithelial infiltrates (SEIs) among French ophthalmologists and orthoptists and the frequency of unreported occupational diseases. We also described short-term and long-term consequences of AK and evaluated associated factors., Methods: The REDCap questionnaire was diffused online several times over 7 consecutive months, from October 2019 to May 2020, through mailing lists (French Society of Ophthalmology, residents, and hospital departments), social networks, and by word of mouth., Results: Seven hundred ten participants were included with a response rate of 6.2% for ophthalmologists, 3.8% for orthoptists, and 28.3% for ophthalmology residents. The medical history of AK was found in 24.1% (95% confidence interval 21%-27.2%) of respondents and SEI in 43.9% (36.5%-51.3%) of the AK population. In total, 87.1% (82.1%-92.1%) of AK occupational diseases were not declared. In total, 57.7% of respondents took 9.4 ± 6.2 days of sick leave, mostly unofficial, and 95.7% stopped surgeries for 13.0 ± 6.6 days. Among the AK population, 39.8% had current sequelae, with 17.5% having persistent SEIs, 19.9% using current therapy, and 16.4% experiencing continuing discomfort. SEIs were associated with wearing contact lenses (odds ratio 3.31, 95% confidence interval 1.19-9.21) and smoking (4.07, 1.30-12.8). Corticosteroid therapy was associated with a greater number of sequelae (3.84, 1.51-9.75)., Conclusions: AK and SEI affect a large proportion of ophthalmologists and orthoptists, possibly for years, with high morbidity leading to occupational discomfort. Few practitioners asked for either to be recognized as an occupational disease. Associated factors would require a dedicated study., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

The study aimed to investigate the best-performing of three risk calculators (RCs) for the Turkish population in predicting cancer-free status and high-risk prostate cancer (PCa) in patients undergoing transrectal ultrasound-guided prostate biopsy. The electronic medical records of 527 patients who underwent prostate biopsy for the first time due to PSA of 0.3-50 ng/dl and/or cancer suspicion at digital rectal examination (DRE) between January 2017 and December 2020 were retrieved retrospectively. The predictive power of the RCs in the biopsy and the surgical cohort was calculated by two urologists using European Randomised Study of Screening for Prostate Cancer (ERSPC) RC, the North American Prostate Cancer Prevention Trial-RC (PCPT-RC), and the Prostate Biopsy Collaborative Group (PBCG)-RC. All three RCs were successful in predicting PCa and high-risk disease at ROC analysis (p < 0.0001). Of these three nomograms, PBCG-RC outperformed PCPT-RC 2.0 and ERSPC-RH in predicting benign pathology outcomes at biopsy. A better performance of PBCG-RC was also observed in terms of prediction of high-risk disease at biopsy. Using any of the available RCs prior to biopsy is of greater assistance to prostate-specific antigen and DRE than examination alone. The study results show that PBCG-RC performed before biopsy has a higher predictive power than the other two RCs., (© 2021 Wiley-VCH GmbH.)

Purpose: The purpose of this study was to evaluate the incidence, clinical features, microbiology, risk factors, and treatment outcomes in cases of ocular surface squamous neoplasia (OSSN) with coexisting microbial keratitis (MK)., Methods: This was a retrospective case-control study from a cohort of 939 cases with OSSN., Results: Twenty eyes (2%) with OSSN and MK were included in the study group and 100 age-matched eyes with only OSSN as controls. Most common presentation was a combination of pain, redness, watering, and decreased vision (50%) over a median duration of 14 days. Mean corneal surface involvement by MK was 48% with corneal perforation in 6 cases (30%). Microbiology showed 10 culture positive cases for Gram-positive organisms (n = 5), fungus (n = 4), or mixed infection (n = 1). In the salvaged eyes, MK resolved in 9 eyes (90%) on medical treatment at a mean duration of 30 days and keratoplasty was performed in 1 eye. OSSN treatment included wide excisional biopsy (n = 9/18; 50%), extended enucleation (n = 7/18; 39%), and orbital exenteration in 1. Over a mean follow-up of 12 months, vision salvage was achieved in 7 of 18 (39%) and globe salvage in 10 of 18 (55%). Logistic regression analysis showed the following significant risk factors for MK in OSSN: male sex, human immunodeficiency virus seropositivity, increasing tumor diameter, limbal epicenter, temporal quadrant, noduloulcerative morphology, pigmentation, scleral invasion, keratin, and corneal component of the OSSN lesion., Conclusions: Rarely, MK can coexist with OSSN leading to a poor treatment outcome. Male sex, human immunodeficiency virus seropositivity, large limbal pigmented OSSN lesion with keratin and corneal component, noduloulcerative morphology, and scleral invasion were risk factors for this association., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

The US FDA issued a black-box warning against co-prescription of antipsychotic (AP) agents and opioids due to the risk of respiratory depression, but evidence on the comparative safety of sedating vs nonsedating APs is lacking. We classified APs as sedating (eg, quetiapine, olanzapine, and chlorpromazine) and nonsedating (eg, aripiprazole, haloperidol, and risperidone) based on their affinity to the histamine-1 neuroreceptor (Ki < or ≥20, respectively) and sought to compare the rate of overdose between patients using sedating vs nonsedating APs plus opioids. We constructed a population-based cohort nested in the IBM MarketScan database (2004-2017). Patients with concomitant use of sedating APs and prescription opioids ("exposed") were 1:1 matched to patients with concomitant use of nonsedating APs and prescription opioids ("referent") based on the propensity score (PS). The primary outcome was any hospitalization or emergency department visit due to an overdose within 30 days. The final cohort comprised 62 604 exposed and an equal number of PS-matched reference patients. Characteristics of matched exposed and reference patients were similar. There were 178 overdose events among the exposed (35.3 events per 1000 person-years [PY]) vs 133 among the reference group (26.4 events per 1000 PY), for an adjusted hazard ratio of 1.34 (95% CI: 1.07-1.68). This finding was consistent across sensitivity and subgroup analyses. Among patients receiving prescription opioids, concomitant use of sedating APs was associated with an increased risk of overdose compared with nonsedating APs. Caution is required when co-prescribing opioids and APs. If co-prescription is needed, choosing a nonsedating agent should be preferred whenever possible given the clinical context., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The impact of carotid stenosis (CS) in patients undergoing cardiac surgery remains controversial. The aim of this study was to evaluate the association between carotid stenosis and stroke and/or transient ischemic attack (TIA) in patients undergoing cardiac surgery on cardiopulmonary bypass., Methods: This was a retrospective cohort study including patients undergoing cardiac surgery on cardiopulmonary bypass from January 2006 to March 2018 at the Québec Heart and Lung Institute. Data of patients' preoperative demographic characteristics, operative and postoperative variables were taken from a computerised database and patients' charts. Univariate and multivariate analyses were performed., Results: A total of 20,241 patients were included in the study. Among those who had received preoperative carotid ultrasound, 516 (2.6% of the total population) had unilateral or bilateral CS ≥ 50%. Categorised levels of CS severity were identified as independent risk factors for postoperative stroke and/or TIA. There was an almost 3-fold increased risk of postoperative neurologic events in 80%-99% CS vs less severe 50%-79% CS (odds ratio 2.91, 95% confidence interval 1.30-6.54), suggesting that the degree of severity of CS is potentially a strong independent predictor of postoperative neurologic events., Conclusions: CS is an independent risk factor of postoperative stroke and/or TIA. This study suggests for the first time that the risk of stroke increases with the degree of severity of CS, with the greatest risk being for CS of 80%-99%. The strength of this relationship and potential causality effect should be further explored in a prospective study focusing on this population most at risk., (Copyright © 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)

The long-term investment in new approach methodologies (NAMs) within the EU and other parts of the world is beginning to result in an emerging consensus of how to use information from in silico, in vitro and targeted in vivo sources to assess the safety of chemicals. However, this methodology is being adopted very slowly for regulatory purposes. Here, we have developed a framework incorporating in silico, in vitro and in vivo methods designed to meet the requirements of REACH in which both hazard and exposure can be assessed using a tiered approach. The outputs from each tier are classification categories, safe doses, and risk assessments, and progress through the tiers depends on the output from previous tiers. We have exemplified the use of the framework with three examples. The outputs were the same or more conservative than parallel assessments based on conventional studies. The framework allows a transparent and phased introduction of NAMs in chemical safety assessment and enables science-based safety decisions which provide the same level of public health protection using fewer animals, taking less time, and using less financial and expert resource. Furthermore, it would also allow new methods to be incorporated as they develop through continuous selective evolution rather than periodic revolution., (© 2022. The Author(s).)

Aim: We aimed to examine the impact of high-risk levels of cardiovascular risk factors on the incidence of cardiovascular disease (CVD) in overweight and non-overweight individuals without treatment for the risk factors., Methods: A total of 8,051 individuals aged 40-74 years without a history of CVD and/or without treatment for hypertension, diabetes, hyperlipidemia, and kidney disease at baseline in 1995-2000 were followed up for a median of 14.1 years. We classified the participants into three risk categories (low-, intermediate-, and high-risk groups) on the basis of individual risk factors (blood pressure, serum glucose, low-density lipoprotein cholesterol [LDL-C], and urinary protein) according to the guidelines of Japanese clinical societies. The high-risk group (systolic blood pressure ≥ 160 mmHg or diastolic blood pressure ≥ 100 mmHg, fasting serum glucose ≥ 130 mg/dL or non-fasting serum glucose ≥ 180 mg/dL, LDL-C ≥ 180 mg/dL, proteinuria ≥ 2+) needed to refer to physicians or start treatment immediately. Overweight was defined as a body mass index of ≥ 25 kg/m 2 ., Results: Compared with those in the non-overweight low-risk group, the hazard ratios (HRs) (95% confidence intervals, population-attributable fractions [PAFs]) of CVD in the high-risk categories of blood pressure were 2.0 (1.4-2.9, 7.0%) in the non-overweight high-risk group and 2.9 (1.9-4.3, 6.8%) in the overweight high-risk group. The corresponding HRs (95% confidence intervals, PAFs) of serum glucose were 2.0 (1.2-3.4, 2.5%) and 2.2 (1.1-4.3, 1.5%) in the non-overweight and overweight high-risk groups, respectively. Such associations were not observed for the high-risk group of LDL-C and proteinuria., Conclusions: The present long-term observational study implies that targeting persons with non-treated severe hypertension and diabetes is prioritized to prevent CVD regardless of overweight status.

The clinical high-risk period before a first episode of psychosis (CHR-P) has been widely studied with the goal of understanding the development of psychosis; however, less attention has been paid to the 75%-80% of CHR-P individuals who do not transition to psychosis. It is an open question whether multivariable models could be developed to predict remission outcomes at the same level of performance and generalizability as those that predict conversion to psychosis. Participants were drawn from the North American Prodrome Longitudinal Study (NAPLS3). An empirically derived set of clinical and demographic predictor variables were selected with elastic net regularization and were included in a gradient boosting machine algorithm to predict prodromal symptom remission. The predictive model was tested in a comparably sized independent sample (NAPLS2). The classification algorithm developed in NAPLS3 achieved an area under the curve of 0.66 (0.60-0.72) with a sensitivity of 0.68 and specificity of 0.53 when tested in an independent external sample (NAPLS2). Overall, future remitters had lower baseline prodromal symptoms than nonremitters. This study is the first to use a data-driven machine-learning approach to assess clinical and demographic predictors of symptomatic remission in individuals who do not convert to psychosis. The predictive power of the models in this study suggest that remission represents a unique clinical phenomenon. Further study is warranted to best understand factors contributing to resilience and recovery from the CHR-P state., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aim: To develop and validate a new risk prediction model for predicting the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) in Japanese adults., Methods: A total of 2,454 participants aged 40-84 years without a history of cardiovascular disease (CVD) were prospectively followed up for 24 years. An incident ASCVD event was defined as the first occurrence of coronary heart disease or atherothrombotic brain infarction. A Cox proportional hazards regression model was used to construct the prediction model. In addition, a simplified scoring system was translated from the developed prediction model. The model performance was evaluated using Harrell's C statistics, a calibration plot with the Greenwood-Nam-D'Agostino test, and a bootstrap validation procedure., Results: During a median of a 24-year follow-up, 270 participants experienced the first ASCVD event. The predictors of the ASCVD events in the multivariable Cox model included age, sex, systolic blood pressure, diabetes, serum high-density lipoprotein cholesterol, serum low-density lipoprotein cholesterol, proteinuria, smoking habits, and regular exercise. The developed models exhibited good discrimination with negligible evidence of overfitting (Harrell's C statistics: 0.786 for the multivariable model and 0.789 for the simplified score) and good calibrations (the Greenwood-Nam-D'Agostino test: P=0.29 for the multivariable model, 0.52 for the simplified score)., Conclusion: We constructed a risk prediction model for the development of ASCVD in Japanese adults. This prediction model exhibits great potential as a tool for predicting the risk of ASCVD in clinical practice by enabling the identification of specific risk factors for ASCVD in individual patients.

Aims: Hemodialysis vintage and serum phosphorus levels adversely affect outcomes in patients on hemodialysis. Whether these factors have a similar prognostic impact on patients who are on hemodialysis and have chronic limb-threatening ischemia (CLTI) has not been systematically studied. We aimed to explore the risk factors, including hemodialysis vintage and serum phosphorus levels, on clinical outcomes after endovascular therapy (EVT) in hemodialysis patients with CLTI., Methods: The current study rerospectively analyzed 374 hemodialysis patients with CLTI presenting with ischemic tissue loss (age: 72.3±9.0 years, male: 73.3%, diabetes mellitus: 68.2%, Rutherford 5: 75.9%, 6: 24.1%, WIfI stage 4: 50.0%) primarily treated with EVT between April 2007 and December 2016. The primary outcome measure was 1-year amputation-free survival (AFS), while the secondary outcome measure was 1-year wound healing. Predictors for each outcome were evaluated by Cox proportional hazards model., Results: Multivariate analysis significantly associated longer hemodialysis vintages with higher serum phosphorus levels (hazard ratio [HR], 0.599; 95% confidence interval [CI], 0.394-0.910; p=0.016) with 1-year AFS. Longer vintages for hemodialysis with higher serum phosphorus levels were marginally, but not significantly, associated with 1-year wound healing. (HR, 0.684; 95% CI, 0.467-1.000; p=0.050)., Conclusion: Longer hemodialysis vintages with higher serum phosphorus levels adversely affect outcomes after EVT for hemodialysis patients with CLTI presenting with ischemic tissue loss.