Risk Assessment Tool Research Articles

Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments

Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments

Abstract A009: Integrative multiomic analysis of extracellular vesicle transcriptomics profiling combined with cfDNA methylation reveals improved stratification of low-risk and high- risk prostate cancer patients in urine-based liquid biopsy

Abstract Introduction: Prostate cancer (PCa) ranks as the second most frequently diagnosed cancer and the sixth most prevalent cause of cancer-related mortality in men globally. In this study, we employ a multiomic approach that utilizes both urine cfDNA and extracellular vesicle (EV) RNA-derived analytes to improve the risk stratification of individuals with prostate cancer and compare this to multi-parametric MRI performance as well as risk calculators in the same cohort. Materials and Methods: Urine was collected from 95 individuals comprising 51 benign or low-risk (Grade Group (GG) <=1), 10 intermediate-risk (GG2) and 44 high-risk PCa patients (GG 3-5). EV-RNA and cfDNA were concurrently isolated from each urine specimen to exploit complementary information. We developed an EV-RNA sequencing platform targeting mRNAs and lncRNAs and targeted 50 million reads per sample; and cfDNA methylome profiling reached an equivalent sequencing depth. Expression of EV-RNA and splice variant Differential Transcript Usage (DTU) and cfDNA methylation patterns were analyzed using Bio-Techne’s multiomic platform. Machine learning-based feature selection algorithms identified biomarker signatures from each analyte. Receiver-operator characteristic curves (ROC) were generated utilizing leave-one-out cross-validation of naïve Bayes classifier models to compute the area under the curve (AUC). Individual signatures were integrated to create a multiomic classifier. Results: Differential gene expression (DEx) analysis identified 18 DEx genes between low and high-risk patients with both known and novel implications in PCa. Splice variant analysis unveiled 60 genes previously implicated in PCa with DTU. Over 18,000 differentially methylated bases were detected between high and low-risk PCa patients. Feature selection analysis identified the top ten features of EV-RNA expression, splice variants, and cfDNA methylation, resulting in an integrative multi-analyte signature. The multiomic signature demonstrated an AUC of 0.92, surpassing any individual analyte’s performance, as well as the performance achieved by PSA (AUC = 0.52) and MRI PI RADS(AUC=0.65). Conclusions: While biomarker signatures obtained from each analyte in this study resulted in effective stratification of PCa risk, the multiomic signature further improved the discriminatory power—underscoring the complementary nature of the signals. Thus, a multiomic biomarker discovery strategy leveraging cfDNA and EV cargo exhibits significant potential as a risk assessment tool for high-grade prostate cancer. This approach can facilitate more informed decision-making in the context of biopsy procedures. Citation Format: Sudipto K Chakrabortty, Dulaney Miller, Kyle Manning, Rikky Xing, Jeff Cole, Christopher Benway, Sinead Nguyen, Allan George, Yiyuan Yao, Elena M Cortizas, Christian Ray, Siva Gowrisankar, Seth Yu, Allan Pollack, Sandra M Gaston, Sanoj Punnen, Johan Skog. Integrative multiomic analysis of extracellular vesicle transcriptomics profiling combined with cfDNA methylation reveals improved stratification of low-risk and high- risk prostate cancer patients in urine-based liquid biopsy [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A009.

An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation

Background Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete. Accurate, dynamic, individualized estimates of fall risk for admitted patients using routinely recorded data may assist in prioritizing fall prevention efforts. Objective The objective of this study was to develop and validate an accurate and dynamic prognostic model for inpatient falls among a cohort of patients using routinely recorded electronic medical record data. Methods We used routinely recorded data from 5 Australian hospitals to develop and internally-externally validate a prediction model for inpatient falls using a Cox proportional hazards model with time-varying covariates. The study cohort included patients admitted during 2018-2021 to any ward, with no age restriction. Predictors used in the model included admission-related administrative data, length of stay, and number of previous falls during the admission (updated every 12 hours up to 14 days after admission). Model calibration was assessed using Poisson regression and discrimination using the area under the time-dependent receiver operating characteristic curve. Results There were 1,107,556 inpatient admissions, 6004 falls, and 5341 unique fallers. The area under the time-dependent receiver operating characteristic curve was 0.899 (95% CI 0.88-0.91) at 24 hours after admission and declined throughout admission (eg, 0.765, 95% CI 0.75-0.78 on the seventh day after admission). Site-dependent overestimation and underestimation of risk was observed on the calibration plots. Conclusions Using a large dataset from multiple hospitals and robust methods to model development and validation, we developed a prognostic model for inpatient falls. It had high discrimination, suggesting the model has the potential for operationalization in clinical decision support for prioritizing inpatients for fall prevention. Performance was site dependent, and model recalibration may lead to improved performance.

Abstract 4136554: Comparison of short- and long-term atherosclerotic cardiovascular disease risk assessment tools in US young adults

Background: In 2023, the AHA published the PREVENT equations for estimating atherosclerotic cardiovascular disease (ASCVD) risk in adults aged 30-79 years. Research Questions: In young adults aged 20-39 years, does PREVENT improve risk prediction for 10- and 30-year ASCVD compared with existing risk assessment tools recommended in the current US guidelines (i.e., Pooled Cohort Equations [PCEs] and Pencina et al. equations)? Aims: To compare the performance of PREVENT vs. PCEs in predicting 10-year ASCVD, and PREVENT vs. Pencina equations in predicting 30-year ASCVD in young adults. Methods: We analyzed data from two complementary sources: (1) pooled data from two large cohorts: Coronary Artery Risk Development in Young Adults (CARDIA) and Framingham Heart Study (FHS; including the Offspring, Third Generation, Omni 1, and Omni 2 cohorts), and (2) electronic health records from Kaiser Permanente Southern California (KPSC). We included adults aged 20-39 years without a history of ASCVD at baseline. The outcome was incident ASCVD (defined as myocardial infarction, fatal coronary heart disease, fatal and nonfatal stroke) at 10 or 30 years. Model discrimination (Harrell’s C) and mean calibration (estimated as the ratio of predicted to observed event rates) were calculated for the overall population and stratified by sex and race/ethnicity. Results: We included 7,606 young adults (mean age 29 years, 53% female, 30% Black) from the pooled cohorts, and 284,667 (mean age 32 years, 61% female, 8% Black, 46% Hispanic) from KPSC. When predicting 10-year risk, PREVENT improved discrimination in both the pooled cohort (ΔHarrell’s C=0.052; 95% CI: 0.014, 0.095) and KPSC (ΔHarrell’s C=0.039; 95% CI: 0.028, 0.049) compared with the PCEs. PREVENT had good calibration (mean calibration ranged from 0.77 to 1.54), whereas the PCEs overestimated 10-year risk (mean calibration ranged from 1.99 to 4.82). When predicting 30-year risk, discrimination was similar for PREVENT and Pencina equations, but both algorithms underestimated 30-year risk with PREVENT showing worse calibration (mean calibration 0.61). Conclusion: PREVENT improved 10-year ASCVD risk prediction in young adults compared to the PCEs but underestimated 30-year risk.

Abstract 4114276: Identification of the heterogeneity in the risk of heart failure in those with left bundle branch block pattern using artificial intelligence

Introduction: The association between left bundle branch block (LBBB) and heart failure is well-established. However, the lack of a practical risk assessment tool for patients with LBBB poses a clinical challenge to the management despite the heterogeneity of the LBBB population. Hypothesis: Artificial intelligence (AI) can extract features to stratify the risk of heart failure (HF) within the heterogeneity of LBBB. Methods: All 12-lead ECGs diagnosed with LBBB recorded from 1/1/2015 to 12/31/2019 were identified in our institution. ECG data were analyzed as 2D matrices with a shape of 12×2500. The Uniform Manifold Approximation and Projection (UMAP) was used to visualize the heterogeneity of LBBB ECG. Additionally, a 2-dimensional convolutional neural network model was trained to detect LBBB ECGs associated with past HF diagnosis. The model was then applied to ECGs from an external cohort of patients with LBBB but without a history of HF. Cumulative incidences of HF admission were compared by stratifying according to tertiles of the model output (low, intermediate and high AI score groups) Results: We identified 15,124 LBBB ECGs in our institution (training dataset) and 2,463 individuals with LBBB ECGs in the external cohort (external test dataset). The UMAP projection of the ECGs revealed distinct heterogeneity in the data ( Fig A ). This heterogeneity was not fully explained by patient demographics, ECG features, or institutions. When the external cohort was divided into 3 groups according to the tertile of AI prediction, patients with high AI scores had a higher risk of HF admission (high vs low AI score group: hazard ratio (HR), 2.10; 95% confidence interval (CI), 1.66-2.65; intermediate vs low AI score group: HR, 1.39; 95%CI, 1.08-1.78: log-rank p <0.0001; Fig 1B ). This finding was unchanged after adjusting for multiple clinical characteristics and ECG parameters, including QRS duration. Conclusion: We observed significant heterogeneity in ECGs presenting LBBB, and the AI model excellently stratified the risk of heart failure admission in patients with LBBB from a single recording of 12-lead ECG, suggesting great potential for clinical utility in managing patients with LBBB.

Relationships Among the EmPHasis-10 Questionnaire, the Simplified Four-Strata Risk Assessment Tool, and Echocardiographic Parameters in Patients with Precapillary Pulmonary Hypertension

Background/Objectives: Pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) are complex diseases that require precise diagnosis and management. The ESC risk score has been used in both conditions. We assessed the relationship between the EmPHasis-10 questionnaire (patient subjective evaluation) and objective assessment using endorsed tools (simplified four-strata risk assessment and right ventricular imaging by transthoracic echocardiography). Methods: The present study retrospectively extracted data from 40 adult patients (27 PAH and 13 CTEPH cases) diagnosed in a single center in Romania. The EmPHasis-10 questionnaire and the four-strata risk assessment (FSRA) tool were applied to each patient. Mean pulmonary artery pressure (mPAP), tricuspid annular plane systolic excursion (TAPSE), TAPSE/systolic pulmonary artery pressure (TAPSE/sPAP) ratio, and right ventricular outflow tract acceleration time (RVOT-AT) were assessed. Results: A significant correlation was observed between the EmPHasis-10 scores and the FSRA tool, the WHO functional class, and the 6 min walking distance. Emphasis-10 score did not correlate with any of the echocardiographic parameters. The FSRA tool showed a moderate positive correlation with mPAP (r = 0.42, p = 0.01) and a negative correlation with TAPSE (r = −0.46, p = 0.003); additionally, across the entire cohort, it was moderately negatively correlated with both RVOT-AT (r = −0.42, p = 0.01) and TAPSE/sPAP ratio (r = −0.43, p = 0.005). Conclusions: Our study evidenced the alignment between EmPHasis-10 scores and prognostic risk score, with poorer health-related quality of life corresponding to higher FSRA. The EmPHasis-10 questionnaire proves to be a valuable, easy-to-use instrument, offering meaningful insights into patients’ health-related quality of life, underscoring its utility in enhancing comprehensive patient assessment and management.

Meta‐Analysis: Prevalence of Frailty and Associated Adverse Events in Inflammatory Bowel Diseases

ABSTRACTBackgroundThe number of adults aged over 60 years with inflammatory bowel disease (IBD) is increasing. Frailty, rather than chronological age, may be a better predictor of adverse health outcomes.AimsTo summarise current knowledge about frailty in adults with IBD including the prevalence and associations of frailty and IBD‐related adverse outcomes.MethodsWe performed an electronic search of MEDLINE, EMBASE and EMBASE Classic databases using search terms for IBD and frailty from inception to 14 February 2024. All studies involving adults aged ≥ 16 with a confirmed diagnosis of IBD that included a frailty assessment were eligible for inclusion.ResultsWe included 23 observational studies involving 1,893,448 adults. Risk of bias was low for 18 studies and moderate for five. Twelve methods of frailty assessment were used, the most common being the Hospital Frailty Risk Score. Pooled prevalence of frailty in IBD patients was 18% (95% confidence interval (CI) 12.4%–25.6%). Meta‐analysis of unadjusted events data demonstrated that frailty increased the risk of infection‐related admissions following treatment in two studies (relative risk (RR) 1.9; 95% CI 1.2–3.0), post‐operative morbidity in three (RR 2.0; 95% CI 1.4–2.7) and mortality in seven (RR 4.3; 95% CI 2.6–7.4).ConclusionsFrailty is common in patients with IBD and is associated with IBD‐related adverse outcomes including infection‐related admissions following treatment, post‐operative morbidity and death. Future work should focus on developing risk assessment tools to better support decision making for older people with frailty and IBD.

SURG-21. DEVELOPMENT AND VALIDATION OF A CLINICAL RISK SCORE FOR POSTOPERATIVE OUTCOME IN NEWLY DIAGNOSED GLIOBLASTOMA: A REPORT OF THE RANORESECT GROUP

Abstract BACKGROUND Following resection or biopsy, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aim to make use of our RANO classification for extent of resection (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool. METHODS The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort. RESULTS Our training cohort compromised 1003 patients with newly diagnosed IDH-wildtype glioblastoma, including 744 patients who underwent adjuvant radiochemotherapy (TMZ/RT→TMZ). Residual tumor, MGMT promotor methylation status, age, and KPS were prognostic of overall survival and forwarded into regression tree analysis. By combining eleven terminal nodes and individually weighting the prognostic factors, an additive scale (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for poor postoperative outcome (median overall survival: 24 vs. 16 vs. 6 months; p<0.01). The presence of the three risk groups was confirmed in the subgroups of patients treated with or without RT/TMZ→TMZ. The prognostic value of the risk score was verified in a external single-center validation cohort of newly diagnosed glioblastoma (n = 258, p<0.01). Compared to previously postulated models, goodness-of-fit measurements were superior for our score. CONCLUSIONS The novel RANO risk score serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The score may serve to guide patient management and reduce imbalances between study arms in prospective trials.

Integrating Building- and Site-Specific and Generic Fragility Curves into Seismic Risk Assessment: A PRISMA-Based Analysis of Methodologies and Applications

Fragility curves are fundamental tools in seismic risk assessments, providing insights into the vulnerability of structures to earthquake-induced damages. These curves, which plot the probability of a structure reaching or exceeding various damage states against earthquake intensity, are critical for developing effective modification strategies. This review aims to present the characteristics between building- and site-specific fragility curves, which incorporate detailed local characteristics, and generic fragility curves that apply broader, more generalized parameters. We utilize the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to systematically review the literature to address key research questions about the methodological differences, applications, and implications of these curve types in assessing seismic risks. The methods involved a comprehensive search and combination of existing studies on the topic, focusing on how these curves are developed and applied in real-world scenarios. The results from this review show that building- and site-specific curves, while more precise, require extensive data and are therefore more complex and costly to develop. In contrast, generic curves, though less accurate, offer a cost-effective solution for preliminary risk assessments over large areas. The conclusions drawn from this review suggest that while each type has its merits, the choice between building- and site-specific and generic fragility curves should be guided by the specific requirements of the seismic risk assessment task, including available resources and the need for precision in the vulnerability estimations.

Association of neighborhood-level disadvantage beyond individual sociodemographic factors in patients with or at risk of knee osteoarthritis

ObjectiveLower socioeconomic status (SES) is a risk factor for poorer pain-related outcomes. Further, the neighborhood environments of disadvantaged communities can create a milieu of increased stress and deprivation that adversely affects pain-related and other health outcomes. Socioenvironmental variables such as the Area Deprivation Index, which ranks neighborhoods based on socioeconomic factors could be used to capture environmental aspects associated with poor pain outcomes. However, it is unclear whether the ADI could be used as a risk assessment tool in addition to individual-level SES.MethodsThe current study investigated whether neighborhood-level disadvantage impacts knee pain-related outcomes above sociodemographic measures. Participants were 188 community-dwelling adults who self-identified as non-Hispanic Black or non-Hispanic White and reported knee pain. Area Deprivation Index (ADI; measure of neighborhood-level disadvantage) state deciles were derived for each participant. Participants reported educational attainment and annual household income as measures of SES, and completed several measures of pain and function: Short-form McGill Pain Questionnaire, Western Ontario and McMaster Universities Osteoarthritis Index, and Graded Chronic Pain Scale were completed, and movement-evoked pain was assessed following the Short Physical Performance Battery. Hierarchical linear regression analyses were used to assess whether environmental and sociodemographic measures (i.e., ADI 80/20 [80% least disadvantaged and 20% most disadvantaged]; education/income, race) were associated with pain-related clinical outcomes.ResultsLiving in the most deprived neighborhood was associated with poorer clinical knee pain-related outcomes compared to living in less deprived neighborhoods (ps < 0.05). Study site, age, BMI, education, and income explained 11.3–28.5% of the variance across all of the individual pain-related outcomes. However, the ADI accounted for 2.5–4.2% additional variance across multiple pain-related outcomes.ConclusionThe ADI accounted for a significant amount of variance in pain-related outcomes beyond the control variables including education and income. Further, the effect of ADI was similar to or higher than the effect of age and BMI. While the effect of neighborhood environment was modest, a neighborhood-level socioenvironmental variable like ADI might be used by clinicians and researchers to improve the characterization of patients’ risk profile for chronic pain outcomes.

Impact of exercise intervention with or without curcumin supplementation on body fat composition, glucose, and lipid metabolism in obese adults: A meta-analysis.

This study was carried out to systematically review and evaluate the influence of exercise with and without curcumin on body fat composition, glucose, and lipid metabolism in obese adults. Search for eligible studies through four databases, and then proceed with screening. The inclusion criteria are as follows: (1) obese adults; (2) randomized controlled trial (RCT); (3) classified the exercise intervention with curcumin supplementation as the exercise with curcumin (CU) group and without curcumin supplementation as the exercise without curcumin (EX) group; (4) Conducted pre- and post-training assessments, which include body fat composition, glucose and lipid metabolism parameters. Use the Cochrane bias risk assessment tool to evaluate the quality of the selected study. Select standardized mean difference (SMD) as the appropriate effect scale index, and use Revman 5.4 software to analyze the mean difference of the selected article data with a 95% confidence interval (CI). A total of seven studies fulfilled the inclusion criteria and were selected for the meta-analysis. The included studies involved 72 males and 111 females, where 94 belonged to the EX group and 89 from the CU group. The CU group benefited more from the reduced Fat% (SMD, 2.18 [0.12, 4.24], p < 0.05, I2 = 0%, p for heterogeneity = 0.98) than the EX group. The study demonstrated that the combined exercise intervention with curcumin supplementation significantly reduced Fat% in obese adults compared with exercise without supplementing curcumin.

Comparing predictive validity of Youth Level of Service/Case Management Inventory scores in Indigenous and non-Indigenous Canadian youth.

There is an increasing recognition of the necessity to establish the predictive validity of risk assessment scores within specific population subgroups, particularly those (including Indigenous peoples) who are overrepresented in the criminal justice system. I compared measures of discrimination and calibration of the Youth Level of Service/Case Management Inventory (YLS/CMI) in Indigenous and non-Indigenous youth probationers in Ontario, Canada. Compared with non-Indigenous youth, Indigenous youth would have higher risk scores and reoffense rates. The YLS/CMI would predict reoffending and time to reoffense significantly and comparably for Indigenous and non-Indigenous youth, but there would be group difference discrimination (sensitivity, specificity) and calibration (positive predictive value, negative predictive value). Justice ministry-supplied data on 400 Indigenous and non-Indigenous youth (330 male, 70 female) individually matched on key background variables were analyzed to provide measures of discrimination and calibration of the YLS/CMI, with 3-year recidivism as the primary outcome. Indigenous youth were assessed at significantly higher risk than non-Indigenous youth (d = .60); 70% of Indigenous youth and 46% of non-Indigenous youth reoffended (ϕ = .24). Overall measures of discrimination (area under the curve) and calibration (logistic regression) were significant and did not differ across groups. Cross-area under the curve results indicated that the YLS/CMI discriminated Indigenous recidivists from non-Indigenous nonrecidivists but differentiated Indigenous nonrecidivists from non-Indigenous recidivists at chance level. In addition, recidivism was underestimated for low-risk Indigenous youth compared with non-Indigenous youth, but specificity was also low; only 28% of Indigenous youth who did not reoffend were assessed as low risk. Results were largely consistent across male and female youth. Examining subgroup predictive validity using multiple indices provides important information that should inform policy and practice discussions regarding fair use of risk assessment tools. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Comparison of Open Ventral Hernia Repair Risk Stratification Systems: A Call for Consensus

Background/Objectives: Ventral hernia repair (VHR) is a common surgical intervention linked to specific surgical site complications. In such occurrences, the related morbidity is often substantial. Although known risk factors have long been recognized, their systematic inclusion in risk stratification systems lacks universal validation. This study evaluates the effectiveness and correspondence of three risk assessment tools—CeDAR, VHWG, and the modified VHWG—in predicting postoperative wound complications in VHR patients. Methods: We analyzed data from 203 patients who underwent VHR for incisional midline or lateral wall hernia across two surgical departments between 2019 and 2023. Each patient was scored using CeDAR, VHWG, and the modified VHWG systems. Outcomes were assessed based on surgical site occurrences (SSOs) such as seroma formation, wound infections, and recurrences. Results: The incidence of SSOs was 8.9%, with two recorded deaths (0.89%). CeDAR scores showed a statistically significant relationship with SSOs but failed to accurately predict complication rates across subgroups. The VHWG grading system effectively predicted higher complication rates for grades III and IV compared to grades I and II, though its modified version did not show significant predictive improvements. Secondary outcomes indicated a higher SSO rate in patients requiring posterior component separation (TAR) and those with larger hernia defects, though the differences were not statistically significant. Major preoperative risk factors, including smoking, diabetes, and obesity, did not show significant correlations with SSO rates in this study. Conclusions: Current risk estimation tools inadequately predict SSOs in VHR. Enhancing prediction accuracy will require incorporating both patient-specific and surgical factors, potentially through advanced algorithms and large-scale studies.

Profit enhancement and imbalance cost reduction with solar PV-fuel cell-EV hybrid system in a wind-integrated day-ahead power market

Abstract In a wind-integrated deregulated network, the wind farm must intimate the power-generating capacity bids to the market controller at least one day before it begins operations. The wind farm's bid submissions are based on the estimated wind speed (EWS); nevertheless, slight differences between the real wind speed (RWS) and the EWS will result in penalties or incentives imposed by the Independent System Operator (ISO). This occurrence is known as the power market imbalance cost, and it has a direct influence on the system's profitability. To mitigate this effect, solar PV and fuel cell storage technologies are used with the wind farm to enhance system profit by offsetting the negative effects of the imbalance cost. Here, solar PV and fuel cells are used to function in the required time i.e. operate in the charging mode when the RWS is greater than the EWS and in discharging mode when the EWS is greater than the RWS to balance the power supply in the grid as to fulfill the power bidding conditions. Furthermore, the study focuses on minimizing potential system risks, which have been assessed using several risk assessment tools such as Value-at-Risk (VaR) and Cumulative Value-at-Risk (CVaR). The work was conducted using an IEEE 14 bus test system. Initially, the solar PV-fuel cell system supplies power to meet local needs, and the remaining energy is sent to the grid to maximize the system's profit. Electric vehicles (EVs) have also been incorporated to maximize the system economy in more quantities and to reduce the system risk further as compared to the solar PV-fuel cell operation. Three different optimization methods, i.e. AGTO (Artificial Gorilla Troops Optimizer Algorithm), ABC (Artificial Bee Colony Algorithm), and SQP (Sequential Quadratic Programming), were used in a comparative analysis to assess the effectiveness of the proposed approach.

Engaging in cyber hygiene: the role of thoughtful decision-making and informational interventions

IntroductionThe effectiveness of human-centric cybersecurity largely depends on end-users’ adherence to security and privacy behaviors. Understanding and predicting variations in the adoption of these safeguards is crucial for both theoretical advancement and practical application. While existing frameworks are often adapted from health science literature, there is potential to enhance these models by incorporating criminological constructs relevant to online victimization. This study introduce rational choice theory of thoughtfully reflective decision-making (TRDM) into the information security domain. TRDM suggests that variations in cognitive decision-making capabilities influence behavioral outcomes, particularly in the context of security and privacy practices.MethodsThe study employed a field experiment to test the applicability of TRDM in predicting end-users’ engagement in security and privacy behaviors. Participants were exposed to security-related warnings, with the hypothesis that thoughtfully reflective decision-makers would be more likely to adopt robust protective behaviors. Data was collected on participants’ responses to these security warnings, as well as their overall adherence to privacy and security practices.ResultsThe findings support the theoretical framework: individuals exhibiting thoughtfully reflective decision-making tendencies demonstrated a higher likelihood of engaging in privacy and security behaviors. Specifically, participants with higher TRDM scores were more likely to adopt protective behaviors when warned of the consequences of non-compliance. These results indicate that cognitive decision-making capabilities significantly influence the likelihood of engaging in cybersecurity practices.DiscussionThe study challenges the prevailing one-size-fits-all approach to cybersecurity by highlighting the importance of individual differences in cognitive decision-making. Thoughtfully reflective decision-makers are better equipped to adopt preventive security measures, suggesting the need for more tailored interventions in cybersecurity education and risk assessment. This research contributes to the development of sophisticated risk assessment tools aimed at mitigating vulnerabilities and reducing users’ susceptibility to digital threats. Incorporating TRDM into information security models provides a more nuanced understanding of user behavior, offering insights into how cognitive processes influence cybersecurity adherence.

Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study

IntroductionTemporomandibular disorders (TMD) have a high prevalence and complex etiology. The purpose of this study was to apply a machine learning (ML) approach to identify risk factors for the occurrence of TMD in adults and to develop and validate an interpretable predictive model for the risk of TMD in adults.MethodsA total of 949 adults who underwent oral examinations were enrolled in our study. 5 different ML algorithms were used for model development and comparison, and feature selection was performed by feature importance ranking and feature decreasing methods. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The precision-recall curve (PR), calibration curve, and decision curve analysis (DCA) further assessed the accuracy and clinical utility of the model.ResultsThe performance of the random forest (RF) model was the best among the 5 ML models. An interpretable RF model was developed with 7 features (gender, malocclusion, unilateral chewing, chewing hard substances, grinding teeth, clenching teeth, and anxiety). The AUCs of the final model on the training set, internal validation set, and external test set were 0.892, 0.854, and 0.857, respectively. Calibration and DCA curves showed high accuracy and clinical applicability of the model.DiscussionAn efficient and interpretable TMD risk prediction model for adults was successfully developed using the ML method. The model not only has good predictive performance, but also enhances the clinical application value of the model through the SHAP method. This model can provide clinicians with a practical and efficient TMD risk assessment tool that can help them better predict and assess TMD risk in adults, supporting more efficient disease management and targeted medical interventions.

Development and validation of a clinical risk model for postoperative outcome in newly diagnosed glioblastoma: a report of the RANO resect group.

Following surgery, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aimed (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool. The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort. Our training cohort compromised 1003 patients with newly diagnosed isocitrate dehydrogenase-wildtype glioblastoma. Residual tumor, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, age, and postoperative KPS were prognostic for survival and incorporated into regression tree analysis. By individually weighting the prognostic factors, an additive score (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for inferior survival. The prognostic value of our risk model was retained in treatment-based subgroups and confirmed in an external validation cohort of 258 patients with glioblastoma. Compared to previously postulated models, goodness-of-fit measurements were superior for our model. The novel RANO risk model serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The model may serve to guide patient management and reduce imbalances between study arms in prospective trials.

Mass gathering events and COVID-19; lessons learnt from the 2020 European football championship.

Evidence about the impact of mass gatherings during the coronavirus disease 2019 (COVID-19) pandemic on the number of disease cases and on the health-care systems of host countries is limited. Additionally, there have been few publications on the lessons identified from the adaptation of mass gatherings held during the pandemic, including the implementation of comprehensive public health and social measures aimed at reducing viral transmission. This article describes preparations made for the 2020 Union of European Football Associations (UEFA) European Football Championship (UEFA Euro 2020) by the World Health Organization's (WHO) Regional Office for Europe, UEFA and other stakeholders after the championship had been rescheduled because of the COVID-19 pandemic. Technical guidance on preparations for the football tournament and risk assessment tools were provided by WHO. A task force established by the WHO Regional Office for Europe conducted traditional and event-based disease surveillance before and during UEFA Euro 2020, monitored public health and social measures in the 11 host countries, and developed a risk communication and community engagement strategy that involved multimedia campaigns targeting news and social media, fans, athletes, event organizers and other stakeholders. The lessons and good practices identified during UEFA Euro 2020 are described to help guide preparations for future mass gatherings in health emergencies. Sharing data and recommendations on best practice from previous mass gatherings with the organizers and countries involved in planning for a major event is particularly important.

Offshore wind project installations: an advanced approach for weather risk assessments

Abstract This paper presents the development of a sequence-based simulation tool for the weather risk assessment of offshore work processes. The approach developed at the Fraunhofer Institute for Wind Energy Systems is the Advanced Weather Time Series Scheduling (Ad-WATSS) method. For statistical significance, this method combines hindcast weather time series of many years with the weather restrictions associated with offshore activities. The method is incorporated into the software tool COAST, where a project plan schedule is simulated by placing activities according to their operational limits, constraints and relationships, and minimum-required time for each operation. A scenario of an offshore wind installation project in the North Sea is used as reference test case for a weather risk assessment using the developed tool and ERA5 weather data. The assessment is then compared with the weather window statistics approach, based on the same weather data sets. The results from both approaches are analysed and compared on a project and task level detail. The results from the sequence-based simulations show that the method is able to capture the long-term variability and extreme events in weather conditions, making it a reliable tool for the simplification of the weather data-based planning, validation, and assessment of offshore projects.

Integrated physical safety–cyber security risk assessment based on layers of protection analysis

The extensive application of information technology in process industries has increased production efficiency but has also introduced new risks. Therefore, it is necessary to systematically analyse the risks within factories to ensure the stable operation of their production systems. This study proposes an integrated risk assessment method based on layers of protection analysis (LOPA), which combines physical safety and cyber security analyses to provide comprehensive risk assessments for the process industry. The method first identifies the hazardous scenarios and protection layers relevant to a process facility. It then identifies potential cyberattack types and existing countermeasures. Subsequently, the functional impacts of attacks on protection layers and potential coupling relationships are discussed. Using common vulnerability scoring system (CVSS) and semi-quantitative methods, the probability of attack is determined to optimize the probability of failure on demand (PFD) of the protection layers. Finally, a case study of a steam separator in a catalytic cracking unit is used to quantitatively explore the potential attacks and risks of coupled protection layers. The application of Bayesian network (BN) is used for further validation of the method. This study offers a novel quantitative tool for risk assessment in the process industry, which can enhance the security and reliability of industrial production and control systems.