Risk Model Research Articles

ITGB4 is a prognostic biomarker and correlated with lung adenocarcinoma brain metastasis.

The aim of this study is to explore the prognostic value and immune signature of ITGB4 expression in lung adenocarcinoma (LUAD) brain metastasis. We comprehensively screened genes associated with LUAD brain metastasis by integrating datasets from the GEO database and TMT-based quantitative proteomics profiles. Univariable survival and Multivariate Cox analysis was used to compare several clinical characteristics with survival, and a risk model was constructed. The biological functions were explored via GO and KEGG analysis. Gene set enrichment analysis (GSEA) was performed using the TCGA dataset. In addition, we use TIMER to explore the collection of ITGB4 Expression and Immune Infiltration Level in LUAD. The ability of ITGB4 to regulate tumor metastasis was further assessed by migration, invasion assay and Western-blot in H1975-BrM4 cells. We found that ITGB4 was the only gene with high clinical diagnostic and prognostic value in LUAD. Enrichment analysis indicated that ITGB4 is associated with brain metastasis, infiltration of immune cells, and the response to immunotherapy. ITGB4 expression can effectively predict the outcomes of patients with LUAD who are receiving anti-PD-1 therapy. ITGB4 knockdown inhibited the invasion, migration of H1975-BrM4 brain metastasis cells, as well as epithelial-mesenchymal transition (EMT) abilities. The heightened expression of ITGB4 protein was shown to promote EMT and enhance the metastatic potential. ITGB4 promotes the progression in H1975-BrM4 cells via MEK/ERK signaling pathway. Our findings indicate that the expression of ITGB4 is linked to the occurrence of brain metastasis and infiltration of immune cells, suggesting that ITGB4 might be a clinical treatment target for LUAD.

Post-Liver Transplant Outcomes: A Comparative Study of 6 Predictive Models.

We compared the performance of the Liver Transplant Risk Score (LTRS) with the survival outcomes following liver transplantation (SOFT), pretransplant SOFT (P-SOFT), Balance of Risk Score (BAR), donor-age and model for end-stage liver disease (D-MELD), and Organ Procurement and Transplantation Network Risk Prediction Score (ORPS) for the prediction of 90-d mortality, 1-y mortality, and 5-y survival after first-time liver transplantation (LT). A retrospective analysis of the Scientific Registry of Transplant Recipients was conducted using data collected between 2002 and 2021. A total of 82 696 adult LT recipients with a median age of 56 y were included. The area under the curve for 90-d mortality were 0.61, 0.66, 0.65, 0.61, 0.58, and 0.56 for the LTRS, SOFT, P-SOFT, BAR, D-MELD, and ORPS, respectively (all pairwise comparisons: P < 0.05). The area under the curve for 1-y mortality were 0.60, 0.63, 0.62, 0.59, 0.60, 0.57, and 0.59 for the LTRS, SOFT, P-SOFT, BAR, D-MELD, and ORPS, respectively (all pairwise comparisons: P < 0.05). The c-statistics for 5-y survival were not statistically significant among the models. For 90-d mortality, 1-y mortality, and 5-y survival, the correlation coefficients between the LTRS and P-SOFT (the 2 models requiring only preoperative parameters) were 0.90. 0.91, and 0.81, respectively (P < 0.01). None of the predictive models demonstrated sufficient precision to reliably identify LT recipients who died within 90 d and 1 y after LT. However, all models exhibited strong capabilities in perioperative risk stratification. Notably, the P-SOFT and LTRS models, the 2 models that can be calculated using only preoperative data, proved to be valuable tools for identifying candidates at a significant risk of poor outcomes.

A Novel Prognostic Risk Score Model Based on RNA Editing Level in Lower-Grade Glioma

BackgroundLower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently. MethodsSurvival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) ResultsA risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing. ConclusionIn conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.

Assessment of the risk of Staphylococcus aureus infection associated with the consumption of braised beef meat sold on the streets in Côte d'Ivoire

Braised beef meat sold on the streets, a meat product that poses a potential risk of food poisoning to humans. This study was to assess the risk of foodborne infection by S. aureus associated with the consumption of braised beef in Côte d’Ivoire, to improve food safety in this country. A preliminary survey was first carried out in six (06) separate towns in Cȏte d'Ivoire, among consumers (n = 900) and sellers (n = 300) of braised beef meat with a view to characterizing the behavior of these actors. A microbiological analysis was then carried out in accordance with the ISO 6888-1/-3 standard (1999) on 189 samples of this braised beef meat taken from various sales outlets in these towns. A risk model was developed. The risk of infection linked to the consumption of this braised beef was estimated using the Monte Carlo simulation procedure. The results of the consumer survey showed that the percentage of the population consuming braised beef meat (BBM) was 74.4%, with an average consumption of 114.3 ± 0.5 g/person/day. Microbiological analysis revealed the isolation of 92% S. aureus with a mean load of 6.0 ± 0.19 log10∙cfu/g greater than 105 CFU/g. The probability of ingesting a dose greater than 108 S. aureus bacteria ranged from 4.2% to 4.3%. Braised beef meat sold in the streets of Côte d'Ivoire's towns and cities poses a real risk of infection. S. aureus is one of the causes. S. aureus-associated gastroenteritis is caused by failure to observe simple hygiene rules. The risk of infection should be mitigated by cleaning up the places where this product is sold and promoting good hygiene practices in the informal sector.

Credit Risk Modelling for Small and Medium Enterprises (SMEs) Using Machine Learning

This study focuses on developing a credit risk model specifically designed for Small and Medium Enterprises (SMEs) using advanced machine learning techniques. The purpose is to enhance the precision of credit risk assessments, providing financial institutions with reliable tools to gauge the likelihood of default among SMEs. The methodology includes data collection from traditional and alternative sources, followed by feature engineering and model training using algorithms such as logistic regression, decision trees, and neural networks. Results demonstrate significant improvements in predictive accuracy, enabling financial institutions to make data-driven lending decisions and manage risks more effectively. This model not only contributes to financial stability but also promotes increased access to credit for SMEs.

Access to Breast Cancer Screening: Disparities and Determinants-AJR Expert Panel Narrative Review.

While breast cancer screening reduces mortality, disparities in access continue to limit equitable care. Medically underserved groups-including American Indian or Alaska Native, Black, Hispanic, disabled, and LGBTQ+ individuals-face significant barriers in accessing screening mammography services. This AJR Expert Panel Narrative Review leverages the National Institute on Minority Health and Health Disparities Research Framework to analyze persistent inequities in screening, focusing on race, ethnicity, socioeconomic status, disabilities, health insurance, and geography. Screening guidelines vary across organizations, complicating access for underserved groups. Legislative efforts, including breast density notification laws and the Find It Early Act, aim to address disparities but remain insufficient. Traditional risk models can yield inconsistent assessments of high-risk status and disparities in appropriate referral for genetic testing and supplemental screening. Emerging technologies like artificial intelligence (AI) promise to reduce these gaps by improving risk assessment and detection accuracy, although clinical integration requires careful evaluation to avoid exacerbating existing inequities. This article underscores the importance of policies and practices that address the needs of disabled individuals and other marginalized populations, focusing on creating inclusive and effective screening systems. Recommendations are provided to guide future research, policy development, and clinical practices aimed at mitigating disparities in breast cancer screening.

Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics

The β-Lactam antibiotics represent a widely used class of antibiotics, yet the latent and often overlooked risk of coagulation dysfunction associated with their use underscores the need for proactive assessment. Machine learning methodologies can offer valuable insights into evaluating the risk of coagulation dysfunction associated with β-lactam antibiotics. This study aims to identify the risk factors associated with coagulation dysfunction related to β-lactam antibiotics and to develop machine learning models for estimating the risk of coagulation dysfunction with real-world data. A retrospective study was performed using machine learning modeling analysis on electronic health record data, employing five distinct machine learning methods. The study focused on adult inpatients discharged from 1 January 2018, to 31 December 2021, at the First Affiliated Hospital of Shandong First Medical University. The models were developed for estimating the risk of coagulation dysfunction associated with various β-lactam antibiotics based on electronic health record feasibility. The dataset was divided into training and test sets to assess model performance using metrics such as total accuracy and area under the curve. The study encompassed risk-factor analysis and machine learning model development for coagulation dysfunction in inpatients administered different β-lactam antibiotics. A total of 45,179 participants were included in the study. The incidence of coagulation disorders related to cefazolin sodium, cefoperazone/sulbactam sodium, cefminol sodium, amoxicillin/sulbactam sodium, and piperacillin/tazobactam sodium was 2.4%, 5.4%, 1.5%, 5.5%, and 4.8%, respectively. Machine learning models for estimating coagulation dysfunction associated with each β-lactam antibiotic underwent validation with 5-fold cross-validation and test sets. On the test set, the optimal models for cefazolin sodium, cefoperazone/sulbactam sodium, cefminol sodium, amoxicillin/sulbactam sodium, and piperacillin/tazobactam sodium yielded AUC values of 0.798, 0.768, 0.919, 0.783, and 0.867, respectively. The study findings suggest that machine learning classifiers can serve as valuable tools for identifying patients at risk of coagulation dysfunction associated with β-lactam antibiotics and intervening based on high-risk predictions. Enhanced access to administrative and clinical data could further enhance the predictive performance of machine learning models, thereby expanding pharmacovigilance efforts.

Quantitative modelling for dengue and Aedes mosquitoes in Africa: A systematic review of current approaches and future directions for Early Warning System development.

The rapid spread and growing number of dengue cases worldwide, alongside the absence of comprehensive vaccines and medications, highlights the critical need for robust tools to monitor, prevent, and control the disease. This review aims to provide an updated overview of important covariates and quantitative modelling techniques used to predict or forecast dengue and/or its vector Aedes mosquitoes in Africa. A systematic search was conducted across multiple databases, including PubMed, EMBASE, EBSCOhost, and Scopus, restricted to studies conducted in Africa and published in English. Data management and extraction process followed the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' (PRISMA) framework. The review identified 30 studies, with the majority (two-thirds) focused on models for predicting Aedes mosquito populations dynamics as a proxy for dengue risk. The remainder of the studies utilized human dengue cases, incidence or prevalence data as an outcome. Input data for mosquito and dengue risk models were mainly obtained from entomological studies and cross-sectional surveys, respectively. More than half of the studies (56.7%) incorporated climatic factors, such as rainfall, humidity, and temperature, alongside environmental, demographic, socio-economic, and larval/pupal abundance factors as covariates in their models. Regarding quantitative modelling techniques, traditional statistical regression methods like logistic and linear regression were preferred (60.0%), followed by machine learning models (16.7%) and mixed effects models (13.3%). Notably, only 36.7% of the models disclosed variable selection techniques, and a mere 20.0% conducted model validation, highlighting a significant gap in reporting methodology and assessing model performance. Overall, this review provides a comprehensive overview of potential covariates and methodological approaches currently applied in the African context for modelling dengue and/or its vector, Aedes mosquito. It also underscores the gaps and challenges posed by limited surveillance data availability, which hinder the development of predictive models to be used as early warning systems in Africa.

Endoplasmic reticulum stress-related signatures: a game-changer in prognostic stratification for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) has limited therapeutic options and a poor prognosis. The endoplasmic reticulum (ER) plays a crucial role in tumor progression and response to stress, making it a promising target for HCC stratification. This study aimed to develop a risk stratification model using ER stress-related signatures. We utilized transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus, which encompass whole-genome expression profiles and clinical annotations. Machine learning algorithms, including the least absolute shrinkage and selection operator, random forest, and support vector machine recursive feature elimination, were applied to the key genes associated with HCC prognosis. A prognostic system was developed using univariate Cox hazard analysis and least absolute shrinkage and selection operator Cox regression, followed by validation using Kaplan-Meier analysis and receiver operating characteristic curves. Tumor immune dysfunction and exclusion tools were used to predict immunotherapy responsiveness. Two distinct clusters associated with ER stress were identified in HCC, each exhibiting unique clinical and biological features. Using a computational approach, a prognostic risk model, namely the ER stress-related signature, was formulated, demonstrating enhanced predictive accuracy compared with that of existing prognostic models. An effective clinical nomogram was established by integrating the risk model with clinicopathological factors. Patients with lower risk scores exhibited improved responsiveness to various chemotherapeutic, targeted, and immunotherapeutic agents. The critical role of ER stress in HCC is highlighted. The ER stress-related signature developed in this study is a powerful tool to assess the risk and clinical treatment of HCC.

Risk prediction model for psoriatic arthritis: NHANES data and multi-algorithm approach.

To develop a simplified predictive model for identifying psoriatic arthritis (PsA) in psoriasis patients. Data from the National Health and Nutrition Examination Survey (NHANES) database were analyzed, including patients with psoriasis without arthritis (PsC) or PsA. The least absolute shrinkage and selection operator, Boruta algorithm, random forest, and stepwise regression were employed to select key variables from 38 potential predictors. Logistic regression models were constructed for each combination of selected variables and evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, Brier scores, and decision curve analysis (DCA). The study included 587 patients with psoriasis, 238 of whom had PsA. The variable combinations proposed by the Boruta algorithm exhibited the best overall performance. Key predictors in the Borutamodel included age, fasting glucose, education level, thyroid disease, hypertension, and chronic bronchitis. This model achieved area under the curve (AUC) of 0.781 (95% CI, 0.737-0.826) for the training set and 0.780 (95% CI, 0.712-0.848) for the testing set in the ROC curve analyses. The AUC values in the PR curves were 0.687 (95% CI, 0.611-0.757) and 0.653 (95% CI, 0.535-0.770), respectively. The Brier scores of 0.186 and 0.191 for the testing and training sets indicated a good fit, further supported by the calibration curves. DCA showed a net clinical benefit for decision thresholds ranging from 0.2 to 0.8 in both datasets. The Borutamodel represents a promising tool for early risk assessment of PsA. Key Points • National Database Utilization: This study leverages the NHANES database to predict psoriatic arthritis risk, addressing previous limitations tied to regional or ethnic constraints. • Comprehensive Variable Analyses: The research examines 38 variables, including demographics, health conditions, laboratory results, and lifestyle factors, using four distinct screening methods and thorough evaluations of model performance. • Innovative Risk Model: The study introduces a novel risk assessment model that integrates age, fasting glucose, education, and comorbidities including hypertension, thyroid disease, and chronic bronchitis, thus moving beyond traditional focus on skin lesions and joint symptoms.

Exploring osteosarcoma based on the tumor microenvironment

Osteosarcoma is a cancerous bone tumor that develops from mesenchymal cells and is characterized by early metastasis, easy drug resistance, high disability, and mortality. Immunological characteristics of the tumor microenvironment (TME) have attracted attention for the prognosis and treatment of osteosarcoma, and there is a need to explore a signature with high sensitivity for prognosis. In the present study, a total of 84 samples of osteosarcoma were acquired from the UCSC Xena database, analyzed for immune infiltration and classified into two categories depending on their immune properties, and then screened for DEGs between the two groups and analyzed for enrichment, with the majority of DEGs enriched in the immune domain. To further analyze their immune characteristics, the immune-related genes were obtained from the TIMER database. We performed an intersection analysis to identify immune-related differentially expressed genes (IR-DEGs), which were analyzed using a univariate COX regression, and LASSO analysis was used to obtain the ideal genes to construct the risk model, and to uncover the prognostic distinctions between high-risk scoring group and low-risk scoring group, a survival analysis was conducted. The risk assessment model developed in this study revealed a notable variation in survival analysis outcomes between the high-risk and low-risk scoring groups, and the conclusions reached by the model are consistent with the findings of previous scholars. They also yield meaningful results when analyzing immune checkpoints. The risk assessment model developed in this study is precise and dependable for forecasting outcomes and analyzing characteristics of osteosarcoma.

IAPP blocks anti-breast cancer function of CD8+T cells via targeting cuproptosis

BackgroundBreast cancer (BRCA) is the most prevalent type of cancer worldwide. As a highly heterogeneous cancer, it has a high recurrence rate. Since its biological behavior can be regulated by immunity and cuprotosis, so exploring potential therapeutic target to mediate immunity and cuprotosis is of great significance for BRCA therapy.MethodsThe immune-related genes and immune-cuprotosis-related deferentially expressed genes (ICR-DEGs) were identified by mining the TCGA database. Prognostic analysis, differential expression analysis, univariate and lasso regression analyses were used to determine their independent prognostic values. To evaluate the relationship between ICR-DEGs and immune scores, we constructed a prognostic risk model to evaluate immune checkpoints, and then the role of tumor immune microenvironment in BRCA was explored. Furthermore, anti-BRCA function and mechanism of islet amyloid poly-peptide (IAPP) mediated CD8+T cells were verified by means of flow cytometry, ELISA, and subcutaneous transplantation tumor model.ResultsAll results suggested that immune-cuprotosis-related genes were a potential predictor of BRCA’s response to immune checkpoint inhibitors and immunotherapy biomarkers. Thereby downregulation of IAPP reduced cuprotosis of CD8+T or Her2-CAR-T cells to promote the anti-BRCA function both in vitro and in vivo.ConclusionOur research had clarified the function and mechanism of IAPP in CD8+T cells, providing new ideas for improving the diagnosis and treatment of BRCA.

A Study of Underwriting Decision Making Based on ARIMA Modeling with Composite Poisson Processes

Facing the problem of frequent occurrence of natural disasters, how to accurately predict the scale of loss and the probability of insolvency is the key for insurance companies to reduce risk and optimize underwriting strategies. This paper explores the multiple influencing factors of catastrophe risk and works to establish a practical risk prediction model. The model is divided into two parts: the first part uses ARIMA model to predict the risk loss, considering the influence of disaster factors, vulnerability, exposure, and disaster prevention ability on the risk size; the second part establishes a positive risk model based on the bankruptcy theory, and assumes that the insurance enrollment process is a composite Poisson process, and constructs a model of the claims process. The probability of insolvency for each year is estimated by substituting the predicted loss values into the claims model and calculating the initial and annual premiums. The study conducted a five-year risk prediction for the Philippines and Australia, and the results show that there is a significant difference in disaster risk management between the two countries, which provides a theoretical basis and practical guidance for subsequent underwriting recommendations.

Exploration of the role of nutritional status scores in heart failure prognosis

Objectives: To explore the prognostic significances of geriatric nutrition risk index (GNRI), controlling nutritional status (CONUT), and prognostic nutritional index (PNI) in patients with heart failure. Methods: This was a retrospective study. Patients with heart failure admitted to the Department of Cardiology, the first affiliated hospital of Sun Yat-sen University from September 16, 2013 to December 28, 2017 were enrolled. Clinical data of patients were collected and patients were followed up. GNRI, CONUT and PNI scores of patients were calculated. Cox proportional risk model was used to evaluate the relationship between the above three nutritional status scores and the risk of all-cause death in patients with heart failure, and restricted cubic spline analysis was used to verify the relationship. Subgroup analysis was performed based on left ventricular ejection fraction, N-terminal pro-B-type natriuretic peptide, number of heart failure comorbidities, systolic blood pressure, creatinine, body mass index, use of angiotensin-converting enzyme inhibitors/angiotensin receptor antagonists, use of β-blockers, and use of spironolactone. Results: A total of 2 016 patients with heart failure aged (67.7±13.0) years old were enrolled, including 1 230 (61.0%) males. All patients were followed up until September 15, 2021. Multivariate Cox proportional hazard models showed that increased GNRI score was significantly associated with reduced risk of all-cause mortality in patients with heart failure (HR=0.989, 95%CI 0.982-0.996, P=0.001), while CONUT and PNI scores were not associated with all-cause mortality (both P>0.05). Restricted cubic spline analysis showed no U-shaped relationship between the scores and all-cause death. Subgroup analysis suggested that the prognostic value of GNRI score was predominant in patients not using angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (Pinteraction=0.024), while there was no statistically significant difference between groups in other subgroup analyses (Pinteraction>0.05). Conclusions: Increased GNRI score is associated with reduced risk of all-cause mortality in patients with heart failure and may serve as an effective tool to assess nutritional status in patients with heart failure.

Response to Roberts, Stenglein, Wydeven, and others

Abstract Human-caused mortality has been the major cause of death among wolves worldwide. In 2017, we summarized a 33-year data set of &amp;gt;933 Gray Wolf deaths from Wisconsin, United States, and estimated that poaching was the major source of mortality. Roberts et al. (2024; hereafter, just Roberts et al. unless using a direct quote from that paper) challenge our reinterpretation of data on causes of death and disappearances and urge us to use standard known-fates survival models rather than the combined time-to-event and total accounting methods that we used. They do not cite subsequent time-to-event and competing risk and incidence models that we published, raising an issue of selective citation of only their own work. Regarding reinterpretations, Roberts et al. neither present evidence for their claims nor revisit records of cause of death to argue their claims. Regarding traditional known-fate survival models, we review the violation of a critical assumption of such models. Namely, causes of death were not independent of censoring among Wisconsin collared wolves. Rates of disappearance approximating 42% of all collared animals are incompatible with the assumption that unknown-fate collared wolves died of the same causes as known-fate animals. We demonstrate that Roberts et al. made an erroneous claim that wolves frequently outlive the operational lives of their VHF collars. We present evidence of undisclosed competing interests among Roberts et al.’s coauthors. In scientific debates, the most transparent assumptions, methods, and data prevail because outside reviewers can judge for themselves. We stand by the conclusions of our combined analyses from 2017 to 2023.

Research and Optimization of Audit Risk Assessment Model Based on Regression Algorithm

Recently, auditors have used audit risk models’ conceptual tools to assess and control the many risks that could occur during an audit. The tool guides the auditor in determining the necessary evidence for each relevant allegation and the required categories of evidence. The importance of refining audit risk assessment models depends on the crucial role that these models perform in allocating resources and reducing financial disparities. The challenging characteristic of such an audit risk assessment model is that inaccurate information can lead to incorrect risk assessments, legal frameworks and failure to detect material misstatements in financial statements. Hence, in this research, BP neural network-enabled machine learning (BPNN-ML) technologies have been improved for the audit risk assessment model. In that financial disparity, the regression algorithm was used to establish the audit risk assessment for data processing and entirely monitor it. The suggested method provides a flexible framework that may be utilized by a wide range of organizations, including global enterprises and financial institutions, to optimize audit processes and ensure regulatory compliance. This research adds to advancing auditing procedures and regulatory compliance efforts in contemporary company contexts by addressing the issues inherent in traditional methods and proposing a practical approach to these concerns. The experimental analysis of BPNN-ML outperforms physical monitoring in terms of feature importance ranking analysis, contextual adaptability analysis, sensitivity analysis, performance analysis and optimized risk assessment analysis.

Calculating future 10-year breast cancer risks in risk-adapted surveillance: A method comparison and application in clinical practice.

The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) has successfully implemented risk-adapted breast cancer surveillance for women at high breast cancer risk in Germany. Women with a family history of breast and ovarian cancer, but without pathogenic germline variants in recognized breast cancer risk genes, are recommended annual breast imaging if their predicted 10-year breast cancer risk is 5% or higher, using the BOADICEA BC risk model, as outlined in the current GC-HBOC guideline. However, women who initially do not meet this risk threshold may do so later, even if there is no new cancer in their family. To determine when this threshold is crossed, one could annually repeat BOADICEA calculations using an aging pedigree: the 'prediction by aging pedigree' (AP) approach. Alternatively, we propose a simplified and more practical 'conditional probability' (CP) approach which calculates future risks based on the initial BOADICEA assessment. Using data from 6,661 women registered with GC-HBOC, both methods were compared. Initially, 74% of women aged 30 to 48 years had a 10-year breast cancer risk below 5%, but 53% exceeded this threshold at an older age based on the AP approach. Among the women with an initial risk below the threshold, the CP approach revealed that 99% of women exceeded the 5% threshold at the same or an earlier age compared with the AP approach (88% of cases were within the same year or one year earlier). The CP approach has been implemented as a user-friendly web application.

Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults.

Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventional risk factors and PS in Chinese and European populations. A nested case-cohort study measured plasma levels of 2923 proteins using Olink Explore panel in ~ 4000 Chinese adults (1976 incident IHD cases and 2001 sub-cohort controls). We used conventional and machine learning (Boruta) methods to develop proteomics-based prediction models of IHD, with discrimination assessed using area under the curve (AUC), C-statistics and net reclassification index (NRI). These were compared with conventional risk factors and PS in Chinese and in 37,187 Europeans. Overall, 446 proteins were associated with IHD (false discovery rate < 0.05) in Chinese after adjustment for conventional cardiovascular disease risk factors. Proteomic risk models alone yielded higher C-statistics for IHD than conventional risk factors or PS (0.855 [95%CI 0.841-0.868] vs. 0.845 [0.829-0.860] vs 0.553 [0.528-0.578], respectively). Addition of 446 proteins to PS improved C-statistics to 0.857 (0.843-0.871) and NRI by 109.1%; and addition to conventional risk factors improved C-statistics to 0.868 (0.854-0.882) and NRI by 86.9%. Boruta analysis identified 30 proteins accounting for ~ 90% of improvement in NRI for IHD conferred by all 2923 proteins. Similar proteomic panels yielded comparable improvements in risk prediction of IHD in Europeans. Plasma proteomics improved risk prediction of IHD beyond conventional risk factors and PS and could enhance precision medicine approaches for primary prevention of IHD.

Hepatic artery peak systolic velocity compared to non-Doppler ultrasound observations for predicting acute cholecystitis: diagnostic performance and impact on a risk categorization approach.

Compare HAv to non-Doppler ultrasound observations for diagnosing acute cholecystitis in a large consecutive cohort of emergency department (ED) patients and establish a method to combine HAv assessment with non-Doppler observations for diagnosing acute cholecystitis. Consecutive ED patients at one institution undergoing gallbladder (GB) ultrasound (US) for acute cholecystitis between 1/1/2020 and 8/31/2022 had assessments of GB diameter, GB wall thickness, GB contents, pericholecystic irregular collection, and hepatic artery peak systolic velocity (HAv). The non-Doppler observations were scored and summed. Non-Doppler risk categorization was based on rate of acute cholecystitis associated with summed scores. The impact of HAv stratification on the rate of acute cholecystitis in the non-Doppler risk categories was evaluated, with regrouping when subgroups had changes in the acute cholecystitis rate; the regrouping established the HAv-adjusted risk model. Receiver-operator curves for acute cholecystitis diagnosis for individual parameters, the non-Doppler risk categorization, and the HAv-adjusted risk model were compared using area-under-curve (AUC) calculations. Of the 885 patients in the study cohort, 117 (13.2%) had acute cholecystitis. The AUC for diagnosing acute cholecystitis using GB distention (83.8%, p < 0.001), GB wall thickness (79.1%, p < 0.001), and GB contents (75.0%, p 0.02) were higher than HAv (66.3%). HAv assessment adjusted risk for 195 patients. The non-Doppler risk categorization and the HAv-adjusted risk model had the same sensitivity (84.6%) and specificity (85.2%) for diagnosing acute cholecystitis, but the HAv-adjusted risk model showed higher AUC (91.3%, p 0.03) due to increased ability to exclude acute cholecystitis. The diagnostic performance of HAv for acute cholecystitis was lower than other assessments. A categorization scheme based on summed points assigned to each non-Doppler observation was improved with HAv assessment. This risk categorization approach using formulaic integration of non-Doppler and Doppler assessments on ED patients allows radiologists to convey one of five levels of disease probability based solely on sonographic features ranging from effectively excluding acute cholecystitis to substantially elevating the chance the patient has the condition.

Personalizing Prediction of High Opioid Use in the Neurointensive Care Unit: Development and Validation of a Stratified Risk Model for Acute Brain Injury Due to Stroke or Traumatic Brain Injury

Personalizing Prediction of High Opioid Use in the Neurointensive Care Unit: Development and Validation of a Stratified Risk Model for Acute Brain Injury Due to Stroke or Traumatic Brain Injury