Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients.

RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In order to study the connection between RNA editing and CESC patients' prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group. An RNA editing-based risk model for CESC patients was established by Cox regression analysis and least absolute shrinkage and selection operator (LASSO). According to the median score generated by this RNA editing-based risk model, patients were categorized into subgroups with high and low risks. We further constructed the nomogram by risk scores and clinical characteristics and analyzed the impact of RNA editing levels on host gene expression levels and adenosine deaminase acting on RNA. Finally, we also compared the biological functions and pathways of differentially expressed genes (DEGs) between different subgroups by enrichment analysis. In this risk model, we screened out 6 RNA editing sites with significant prognostic value. The constructed nomogram performed well in forecasting patients' prognoses. Furthermore, the level of RNA editing at the prognostic site exhibited a strong correlation with host gene expression. In the high-risk subgroup, we observed multiple biological functions and pathways associated with immune response, cell proliferation, and tumor progression. This study establishes an RNA editing-based risk model that helps forecast patients' prognoses and offers a new understanding of the underlying mechanism of RNA editing in CESC.

Do nutritional assessment tools (PNI, CONUT, GNRI) predict adverse events after spinal surgeries? A systematic review and meta-analysis.

BACKGROUND: Nutritional assessment tools are used to predict outcomes in cancer. However, their utility in patients undergoing spinal surgery is unclear. This review examined if prognostic nutritional index (PNI), controlling nutritional status (CONUT), and geriatric nutritional risk index (GNRI) can predict adverse events after spinal surgeries. METHODS: PubMed, CENTRAL, Scopus, and Embase were screened by two reviewers for relevant studies up to 26th January 2024. The primary outcome of interest was total adverse events after spinal surgery. Secondary outcomes were surgical site infections (SSI) and mortality. RESULTS: 14 studies were included. Meta-analysis showed that while reduced PNI was not associated with an increased risk of SSI there was a significant association between PNI and higher risk of adverse events. Meta-analysis showed that high CONUT was not associated with an increased risk of complications after spinal surgeries. Pooled analysis showed that low GNRI was associated with an increased risk of both SSI and adverse events. Data on mortality was scarce. CONCLUSIONS: The PNI and GNRI can predict adverse outcomes after spinal surgeries. Limited data shows that high CONUT is also associated with a non-significant increased risk of adverse outcomes. High GNRI was predictive of an increased risk of SSI. Data on mortality is too scarce for strong conclusions.

Precision in Prevention: Tailoring Single-Use Negative Pressure Wound Therapy Utilization Through Artificial Intelligence-Based Surgical Site Complications Risk and Cost Modeling.

Background: Surgical site complications (SSCs) are common, yet preventable hospital-acquired conditions. Single-use negative pressure wound therapy (sNPWT) has been shown to be effective in reducing rates of these complications. In the era of value-based care, strategic allocation of sNPWT is needed to optimize both clinical and financial outcomes. Materials and Methods: We conducted a retrospective analysis using data from the Premier Healthcare Database (2017-2021) for 10 representative open procedures in orthopedic, abdominal, cardiovascular, cesarean delivery, and breast surgery. After separating data into training and validation sets, various machine learning algorithms were used to develop pre-operative SSC risk prediction models. Model performance was assessed using standard metrics and predictors of SSCs were identified through feature importance evaluation. Highest-performing models were used to simulate the cost-effectiveness of sNPWT at both the patient and population level. Results: The prediction models demonstrated good performance, with an average area under the curve of 76%. Prominent predictors across subspecialities included age, obesity, and the level of procedure urgency. Prediction models enabled a simulation analysis to assess the population-level cost-effectiveness of sNPWT, incorporating patient and surgery-specific factors, along with the established efficacy of sNPWT for each surgical procedure. The simulation models uncovered significant variability in sNPWT's cost-effectiveness across different procedural categories. Conclusions: This study demonstrates that machine learning models can effectively predict a patient's risk of SSC and guide strategic utilization of sNPWT. This data-driven approach allows for optimization of clinical and financial outcomes by strategically allocating sNPWT based on personalized risk assessments.

Risk Assessment Models for Venous Thromboembolism in Medical Inpatients.

Importance: Thromboprophylaxis is recommended for medical inpatients at risk of venous thromboembolism (VTE). Risk assessment models (RAMs) have been developed to stratify VTE risk, but a prospective head-to-head comparison of validated RAMs is lacking. Objectives: To prospectively validate an easy-to-use RAM, the simplified Geneva score, and compare its prognostic performance with previously validated RAMs. Design, Setting, and Participants: This prospective cohort study was conducted from June 18, 2020, to January 4, 2022, with a 90-day follow-up. A total of 4205 consecutive adults admitted to the general internal medicine departments of 3 Swiss university hospitals for hospitalization for more than 24 hours due to acute illness were screened for eligibility; 1352 without therapeutic anticoagulation were included. Exposures: At admission, items of 4 RAMs (ie, the simplified and original Geneva score, the Padua score, and the IMPROVE [International Medical Prevention Registry on Venous Thromboembolism] score) were collected. Patients were stratified into high and low VTE risk groups according to each RAM. Main Outcomes and Measures: Symptomatic VTE within 90 days. Results: Of 1352 medical inpatients (median age, 67 years [IQR, 54-77 years]; 762 men [55.4%]), 28 (2.1%) experienced VTE. Based on the simplified Geneva score, 854 patients (63.2%) were classified as high risk, with a 90-day VTE risk of 2.6% (n = 22; 95% CI, 1.7%-3.9%), and 498 patients (36.8%) were classified as low risk, with a 90-day VTE risk of 1.2% (n = 6; 95% CI, 0.6%-2.6%). Sensitivity of the simplified Geneva score was 78.6% (95% CI, 60.5%-89.8%) and specificity was 37.2% (95% CI, 34.6%-39.8%); the positive likelihood ratio of the simplified Geneva score was 1.25 (95% CI, 1.03-1.52) and the negative likelihood ratio was 0.58 (95% CI, 0.28-1.18). In head-to-head comparisons, sensitivity was highest for the original Geneva score (82.1%; 95% CI, 64.4%-92.1%), while specificity was highest for the IMPROVE score (70.4%; 95% CI, 67.9%-72.8%). After adjusting the VTE risk for thromboprophylaxis use and site, there was no significant difference between the high-risk and low-risk groups based on the simplified Geneva score (subhazard ratio, 2.04 [95% CI, 0.83-5.05]; P = .12) and other RAMs. Discriminative performance was poor for all RAMs, with an area under the receiver operating characteristic curve ranging from 53.8% (95% CI, 51.1%-56.5%) for the original Geneva score to 58.1% (95% CI, 55.4%-60.7%) for the simplified Geneva score. Conclusions and Relevance: This head-to-head comparison of validated RAMs found suboptimal accuracy and prognostic performance of the simplified Geneva score and other RAMs to predict hospital-acquired VTE in medical inpatients. Clinical usefulness of existing RAMs is questionable, highlighting the need for more accurate VTE prediction strategies.

Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness.

BACKGROUND: The Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries. This study focuses on total hip, knee, and shoulder arthroplasty procedures as they account for most joint replacement surgeries. Apart from being a burden to healthcare systems, readmissions are also troublesome for patients. There are several studies which only utilized structured data from Electronic Health Records (EHR) without considering any gender and payor bias adjustments. METHODS: For this study, dataset of 38,581 total knee, hip, and shoulder replacement surgeries performed from 2015 to 2021 at Novant Health was gathered. This data was used to train a random forest machine learning model to predict the combined endpoint of emergency department (ED) visit or unplanned readmissions within 30 days of discharge or discharge to Skilled Nursing Facility (SNF) following the surgery. 98 features of laboratory results, diagnoses, vitals, medications, and utilization history were extracted. A natural language processing (NLP) model finetuned from Clinical BERT was used to generate an NLP risk score feature for each patient based on their clinical notes. To address societal biases, a feature bias analysis was performed in conjunction with propensity score matching. A threshold optimization algorithm from the Fairlearn toolkit was used to mitigate gender and payor biases to promote fairness in predictions. RESULTS: The model achieved an Area Under the Receiver Operating characteristic Curve (AUROC) of 0.738 (95% confidence interval, 0.724 to 0.754) and an Area Under the Precision-Recall Curve (AUPRC) of 0.406 (95% confidence interval, 0.384 to 0.433). Considering an outcome prevalence of 16%, these metrics indicate the model's ability to accurately discriminate between readmission and non-readmission cases within the context of total arthroplasty surgeries while adjusting patient scores in the model to mitigate bias based on patient gender and payor. CONCLUSION: This work culminated in a model that identifies the most predictive and protective features associated with the combined endpoint. This model serves as a tool to empower healthcare providers to proactively intervene based on these influential factors without introducing bias towards protected patient classes, effectively mitigating the risk of negative outcomes and ultimately improving quality of care regardless of socioeconomic factors.

Addition of High-Sensitivity Troponin to Perioperative Risk Assessment Improves the Predictive Ability of Death in Non-Cardiac Surgery Patients. / Adição da Troponina Ultrassensível à Avaliação de Risco Perioperatório Melhora a Capacidade Preditiva de Morte em Pacientes Submetidos à Cirurgia Não Cardíaca.

BACKGROUND: Risk stratification is an important step in perioperative evaluation. However, the main risk scores do not incorporate biomarkers in their set of variables. OBJECTIVE: Evaluate the incremental power of troponin to the usual risk stratification. METHODS: A total of 2,230 patients admitted to the intensive care unit after non-cardiac surgery were classified according to three types of risk: cardiovascular risk (CVR), Revised Cardiac Risk Index (RCRI); and inherent risk of surgery (IRS). The main outcome was all-cause mortality. Cox regression was used as well as c-statistics before and after addition of high-sensitivity troponin (at least one measurement up to three days after surgery). Finally, net reclassification index and integrated discrimination improvement were used to assess the incremental power of troponin for risk stratification. Significance level was set at 0.05. RESULTS: Mean age of patients was 63.8 years and 55.6% were women. The prevalence of myocardial injury after non-cardiac surgery (MINS) was 9.4%. High CVR-patients had a higher occurrence of MINS (40.1 x 24.8%, p<0.001), as well as high IRS-patients (21.3 x 13.9%, p=0.004) and those with a RCRI≥3 (3.0 x 0.7%, p=0.009). Patients without MINS, regardless of the assessed risk, had similar mortality rate. The addition of troponin to the risk assessment improved the predictive ability of death at 30 days and at 1 year in all risk assessments. CONCLUSION: The prevalence of MINS is higher in the high-risk population. However, its prevalence in lower-risk population is not negligible and causes a higher risk of death. The addition of high-sensitivity troponin increased the predictive ability of risk assessment in all groups.

FUNDAMENTO: A estratificação ode risco é uma importante etapa na avaliação perioperatória. No entanto, os principais escores de risco não incorporam biomarcadores em seus conjuntos de variáveis. OBJETIVO: Avaliar o poder incremental da troponina à estratificação de risco tradicional. MÉTODOS: Um total de 2230 pacientes admitidos na unidade de terapia intensiva após cirurgia não cardíaca foram classificados de acordo com três tipos de risco: Risco Cardiovascular (RCV), Índice de Risco Cardíaco Revisado (IRCR), e Risco Inerente da Cirurgia (RIC). O principal desfecho foi mortalidade por todas as causas. A regressão de Cox foi usada, assim como a estatística C antes e após a adição de troponina ultrassensível (pelo menos uma medida até três dias após a cirurgia). Finalmente, o índice de reclassificação líquida e a melhoria de discriminação integrada foram usadas para avaliar o poder incremental da troponina para a estratificação de risco. O nível de significância usado foi de 0,05. RESULTADOS: A idade média dos pacientes foi 63,8 anos e 55,6% eram do sexo feminino. A prevalência de lesão miocárdica após cirurgia não cardíaca (MINS) foi 9,4%. Pacientes com um RCV elevado apresentaram uma maior ocorrência de MINS (40,1% x 24,8%, p<0,001), bem como pacientes com alto RIC (21,3 x 13,9%, p=0,004) e aqueles com IRCR≥3 (3,0 x 0,7%, p=0,009). Pacientes sem MINS, independentemente do risco avaliado, apresentaram taxa de mortalidade similar. A adição de troponina à avaliação de risco melhorou a capacidade preditiva de mortalidade em 30 dias e de mortalidade em um ano em todas as avaliações de risco. CONCLUSÃO: A prevalência de MINS é mais alta na população de alto risco. No entanto, sua prevalência na população de risco mais baixo não é desprezível e causa um maior risco de morte. A adição da troponina ultrassensível melhorou a capacidade preditiva da avaliação de risco em todos os grupos.

Guidelines for fracture risk assessment and management of osteoporosis in postmenopausal women and men above the age of 50 in Qatar.

We present comprehensive guidelines for osteoporosis management in Qatar. Formulated by the Qatar Osteoporosis Association, the guidelines recommend the age-dependent Qatar fracture risk assessment tool for screening, emphasizing risk-based treatment strategies and discouraging routine dual-energy X-ray scans. They offer a vital resource for physicians managing osteoporosis and fragility fractures nationwide. PURPOSE: Osteoporosis and related fragility fractures are a growing public health issue with an impact on individuals and the healthcare system. We aimed to present guidelines providing unified guidance to all healthcare professionals in Qatar regarding the management of osteoporosis. METHODS: The Qatar Osteoporosis Association formulated guidelines for the diagnosis and management of osteoporosis in postmenopausal women and men above the age of 50. A panel of six local rheumatologists who are experts in the field of osteoporosis met together and conducted an extensive review of published articles and local and international guidelines to formulate guidance for the screening and management of postmenopausal women and men older than 50 years in Qatar. RESULTS: The guidelines emphasize the use of the age-dependent hybrid model of the Qatar fracture risk assessment tool for screening osteoporosis and risk categorization. The guidelines include screening, risk stratification, investigations, treatment, and monitoring of patients with osteoporosis. The use of a dual-energy X-ray absorptiometry scan without any risk factors is discouraged. Treatment options are recommended based on risk stratification. CONCLUSION: Guidance is provided to all physicians across the country who are involved in the care of patients with osteoporosis and fragility fractures.

Sex Differences in Cardiovascular Outcomes in Patients With Kidney Failure.

BACKGROUND: Cardiovascular disease is the leading cause of mortality in patients with kidney failure, and their risk of cardiovascular events is 10 to 20 times higher as compared with the general population. METHODS AND RESULTS: We evaluated 508 822 patients who initiated dialysis between January 1, 2005 and December 31, 2014 using the United States Renal Data System with linked Medicare claims. We determined hospitalization rates for cardiovascular events, defined by acute coronary syndrome, heart failure, and stroke. We examined the association of sex with outcome of cardiovascular events, cardiovascular death, and all-cause death using adjusted time-to-event models. The mean age was 70±12 years and 44.7% were women. The cardiovascular event rate was 232 per thousand person-years (95% CI, 231-233), with a higher rate in women than in men (248 per thousand person-years [95% CI, 247-250] versus 219 per thousand person-years [95% CI, 217-220]). Women had a 14% higher risk of cardiovascular events than men (hazard ratio [HR], 1.14 [95% CI, 1.13-1.16]). Women had a 16% higher risk of heart failure (HR, 1.16 [95% CI, 1.15-1.18]), a 31% higher risk of stroke (HR, 1.31 [95% CI, 1.28-1.34]), and no difference in risk of acute coronary syndrome (HR, 1.01 [95% CI, 0.99-1.03]). Women had a lower risk of cardiovascular death (HR, 0.89 [95% CI, 0.88-0.90]) and a lower risk of all-cause death than men (HR, 0.96 [95% CI, 0.95-0.97]). CONCLUSIONS: Among patients undergoing dialysis, women have a higher risk of cardiovascular events of heart failure and stroke than men. Women have a lower adjusted risk of cardiovascular mortality and all-cause mortality.

Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review.

BACKGROUND: Respiratory tract infections are major contributors to the global disease burden. Quantitative microbial risk assessment (QMRA) holds potential as a rapidly deployable framework to understand respiratory pathogen transmission and inform policy on infection control. OBJECTIVES: The goal of this paper was to evaluate, motivate, and inform further development of the use of QMRA as a rapid tool to understand the transmission of respiratory pathogens and improve the evidence base for infection control policies. METHODS: We conducted a literature review to identify peer-reviewed studies of complete QMRA frameworks on aerosol inhalation or contact transmission of respiratory pathogens. From each of the identified studies, we extracted and summarized information on the applied exposure model approaches, dose-response models, and parameter values, including risk characterization. Finally, we reviewed linkages between model outcomes and policy. RESULTS: We identified 93 studies conducted in 16 different countries with complete QMRA frameworks for diverse respiratory pathogens, including SARS-CoV-2, Legionella spp., Staphylococcus aureus, influenza, and Bacillus anthracis. Six distinct exposure models were identified across diverse and complex transmission pathways. In 57 studies, exposure model frameworks were informed by their ability to model the efficacy of potential interventions. Among interventions, masking, ventilation, social distancing, and other environmental source controls were commonly assessed. Pathogen concentration, aerosol concentration, and partitioning coefficient were influential exposure parameters as identified by sensitivity analysis. Most (84%, n=78) studies presented policy-relevant content including a) determining disease burden to call for policy intervention, b) determining risk-based threshold values for regulations, c) informing intervention and control strategies, and d) making recommendations and suggestions for QMRA application in policy. CONCLUSIONS: We identified needs to further the development of QMRA frameworks for respiratory pathogens that prioritize appropriate aerosol exposure modeling approaches, consider trade-offs between model validity and complexity, and incorporate research that strengthens confidence in QMRA results. https://doi.org/10.1289/EHP12695.

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

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

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

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

Public views of animal testing and alternatives in chemical risk assessment.

Next-Generation Risk Assessment (NGRA) aims to implement New Approach Methodologies (NAMs) into risk assessment and to rely on new in vivo testing in animals only as a last resort. However, various technical and regulatory hurdles impede their regulatory implementation. Assumptions about the public's expectations could act as barriers to the acceptance of NAMs. This study aimed at investigating public views of animal testing and potential alternatives, namely in vitro and in silico testing. An online survey was conducted (N = 965). The results suggest that people make trade-offs, as they experience negative affect regarding in vivo testing, which partly might explain their openness regarding certain alternatives. In vitro tests were attributed the highest ability to determine harmful effects of chemicals for different endpoints, followed by in vivo and in silico tests. Our results further showed that many people accept chemicals to be only tested with alternatives, with highest acceptance for household consumer products, food contact material or building materials and less accepting for medicines and foods. This article addresses potential challenges that might arise from public perceptions and thus, contributes to the bottom-up initiatives to overcome the hurdles to the implementation of NAMs in regulatory risk assessment.

Current uncertainties and challenges of publicly available pharmaceutical environmental risk assessment data.

Pharmaceutical residues are widely detected in aquatic environment worldwide mainly arising from human excretions in sewage systems. Presently, publicly available, high quality environmental risk assessment (ERA) data for pharmaceuticals are limited. However, databases like the Swedish Fass offer valuable resources aiding healthcare professionals and environmental scientists in identifying substances of significant concern. In this review, we provide a concise overview of the regulatory ERA process for medicinal products intended for human use. We explore its key assumptions and uncertainties using a subset of 37 pharmaceuticals. First, we compare the consistency of their predicted no-effect concentrations reported in the Fass database with those by marketing authorisation holders. Second, we compare the predicted environmental concentrations (PEC) calculated based on sales data between European and national drug consumption statistics as well as with measured environmental concentrations (MEC), to demonstrate their impact on the regional risk quotients. Finally, we briefly discuss the prevailing uncertainties and challenges of current ecotoxicity testing, especially outcomes of chronic and nonlethal effects.

Verification of 2021 IDF-DAR risk assessment tool for fasting Ramadan in patients with diabetes attending primary health care in The Kingdom of Bahrain: The DAR-BAH study.

BACKGROUND: There is a high prevalence of diabetes mellitus among Muslim adult patients. Those Muslims are required to fast the holy month of Ramadan. However, the Islam religion exempted some people with medical issues. It was not clear if all the patients with diabetes were considered medically unfit to fast Ramadan. Therefore, IDF-DAR group created a new risk calculator to categorize the patients with diabetes to advise with or against fasting accordingly. OBJECTIVE: This study is validating the IDF-DAR tool in assessing the accuracy of IDF-DAR risk calculator tool on adult patients with diabetes visiting primary health care in the Kingdom of Bahrain. The study will facilitate the health care professionals' decision to exempt the patients from fasting. METHODS: It is a prospective, randomized study for Ramadan 1444/2023 to assess the new IDF-DAR risk score tool that predicts the complications and the negative outcome of fasting during Ramadan. It included pre- and post-Ramadan questionnaires. 757 patients were selected randomly from the patients list of the central diabetes clinics in primary health care for pre-Ramadan risk assessment scoring. Post-Ramadan a phone a questionnaire was done to evaluate the ability of fasting and the occurrence of adverse events. RESULTS: Out of the 757 participants, 611 were included in the study. The mean age of the studied population was 59.8 years and 52.8 % of them were female. 630 (95.3 %) had type 2 diabetes. According to the new IDF-DAR risk calculator, 184 (27.8 %) were categorized as low risk (≤3 score), 252 (38.1 %) as moderate risk (3-6 score), and 225 (34 %) as high risk (>6 score). The percentage of patients completed their 30 days fasting successfully without reporting adverse events in the low, moderate, and high-risk groups were 92.4 %, 89.3 %, 74.7 % respectively. There was significant increased risk in breaking the fast between the low-risk group and high-risk group with a p-value of <0.001. Similarly, there was significant increased risk in breaking the fast between the moderate-risk group and high-risk group with p-value of <0.001. The main reason of breaking the fast was hypoglycemic attacks. The leading factors that play a significant role in increasing the risk of adverse events during fasting Ramadan were type 1 diabetes mellitus, the presence of previous hypoglycemia attacks, the presence of renal impairment, and negative previous Ramadan experience. CONCLUSION: The new IDF-DAR risk calculator is a good tool to predict both the ability to fast Ramadan and the probability of experiencing adverse events (mainly hypoglycemia) in people with diabetes mellitus in Kingdom of Bahrain.

Ten-year fracture risk in Japanese patients with myasthenia gravis: A comprehensive assessment using the fracture risk assessment tool.

BACKGROUND: Myasthenia gravis (MG) is an immune disorder that causes muscle weakness with an increasing prevalence, particularly among the elderly in Japan. Glucocorticoid treatment for MG is problematic for bone health because of reduced bone density and increased fracture risk. The fracture risk assessment tool (FRAX®) can estimate fracture risk, but its applicability in patients with MG remains uncertain. METHODS: A prospective cohort study was conducted on 54 patients with MG between April and July 2012. Bone mineral density (BMD) was measured, and FRAX® scores were calculated with and without BMD. We also adjusted FRAX® scores based on glucocorticoid dosage. Patients were monitored for major osteoporotic fractures (MOF) until June 2022. Statistical analyses included Kaplan-Meier curves and Cox proportional hazards models. RESULTS: The study group included 12 men and 42 women with a mean age of 62 years. Higher FRAX® scores correlated with increased fracture risk, particularly in the hip and lumbar regions. The 10-year fracture-free rate was significantly lower in the high-FRAX® score group. The FRAX® score using BMD is a significant predictor of MOF risk. The hazard ratio for FRAX® scores was 1.17 (95% CI 1.10-1.26). CONCLUSION: We demonstrated the effectiveness of the FRAX® tool in assessing fracture risk among patients with MG. High FRAX® scores correlated with increased fracture risk, emphasizing its importance. These findings support the incorporation of FRAX® assessment into clinical management to enhance patient care and outcomes. However, the small sample size and observational nature suggest a need for further research.

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

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

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA 2 DS 2 -VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.