Twenty-eight years of GM Food and feed without harm: why not accept them?

Since the first genetically engineered or modified crops or organisms (GMO) were approved for commercial production in 1995, no new GMO has been proven to be a hazard or cause harm to human consumers. These modifications have improved crop efficiency, reduced losses to insect pests, reduced losses to viral and microbial plant pathogens and improved drought tolerance. A few have focused on nutritional improvements producing beta carotene in Golden Rice. Regulators in the United States and countries signing the CODEX Alimentarius and Cartagena Biosafety agreements have evaluated human and animal food safety considering potential risks of allergenicity, toxicity, nutritional and anti-nutritional risks. They consider risks for non-target organisms and the environment. There are no cases where post-market surveillance has uncovered harm to consumers or the environment including potential transfer of DNA from the GMO to non-target organisms. In fact, many GMOs have helped improve production, yield and reduced risks from chemical insecticides or fungicides. Yet there are generic calls to label foods containing any genetic modification as a GMO and refusing to allow GM events to be labeled as organic. Many African countries have accepted the Cartagena Protocol as a tool to keep GM events out of their countries while facing food insecurity. The rationale for those restrictions are not rational. Other issues related to genetic diversity, seed production and environmental safety must be addressed. What can be done to increase acceptance of safe and nutritious foods as the population increases, land for cultivation is reduced and energy costs soar?

Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients

INTRODUCTION

The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.

METHODS

We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared.

RESULTS

Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression.

CONCLUSIONS

We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.

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'.

Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review

Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.

Age shock index as an early predictor of cardiovascular death in acute coronary syndrome patients

BACKGROUND

The shock index (SI), reflecting heart rate (HR) to SBP ratio, is established for predicting adverse outcomes in acute coronary syndrome (ACS) patients. Exploring the age shock index (ASI), obtained by multiplying SI with age, could offer further insights into ACS prognosis.

OBJECTIVES

Assess ASI's effectiveness in predicting in-hospital death in individuals with ACS.

METHODS

This study encompassed patients with acute myocardial infarction, drawn from a national registry spanning October 2010 to January 2022. The optimal ASI threshold was established using receiver operating characteristic (ROC) curve analysis. The primary outcome was in-hospital mortality.

RESULTS

A total of 27 312 patients were enrolled, exhibiting a mean age of 66 ± 13 years, with 72.3% being male and 47.5% having ST-elevation myocardial infarction. ROC analysis yielded an area under the curve (AUC) of 0.80, identifying the optimal ASI cutoff as 44. Multivariate regression analysis, adjusting for potential confounders, established ASI ≥ 44 as an independent predictor of in-hospital death [hazard ratio: 3.09, 95% confidence interval: 2.56-3.71, P  < 0.001]. Furthermore, ASI emerged as a notably superior predictor of in-hospital death compared to the SI (AUC ASI  = 0.80 vs. AUC SI  = 0.72, P  < 0.0001), though it did not outperform the Global Registry of Acute Coronary Events (GRACE) score (AUC ASI  = 0.80 vs. AUC GRACE  = 0.85, P  < 0.001) or thrombolysis in myocardial infarction (TIMI) risk index (AUC ASI  = 0.80 vs. AUC TIMI  = 0.84, P  < 0.001).

CONCLUSION

The ASI offers an expedient mean to promptly identify ACS patients at elevated risk of in-hospital death. Its simplicity and effectiveness could render it a valuable tool for early risk stratification in this population.

Perceived balance and self-reported falls: A retrospective cross-sectional study using the National Health and Aging Trend Study

AIMS

To examine how perceived balance problems are associated with self-reported falls in the past month after controlling for known correlates of falls among older adults.

BACKGROUND

Approximately 30% of adults age 65 and older fall each year. Most accidental falls are preventable, and older adults' engagement in fall prevention is imperative. Limited research suggest that older adults do not use the term 'fall risk' to describe their risk for falls. Instead, they commonly use the term 'balance problems'. Yet, commonly used fall risk assessment tools in both primary and acute care do not assess older adults' perceived balance.

DESIGN AND METHOD

The Health Belief Model and the concept of perceived susceptibility served as the theoretical framework. A retrospective, cross-sectional secondary analysis using data from the National Health and Aging Trends Study from year 2015 was conducted. The outcome variable was self-reported falls in the last month.

RESULTS

A subsample of independently living participants (N = 7499) was selected, and 10.3% of the sample reported a fall. Multiple logistic regression analysis revealed that the odds of reporting a fall in the past month was 3.4 times (p < .001) greater for participants who self-reported having a balance problem compared to those who did not. In contrast, fear of falling and perceived memory problems were not uniquely associated with falls. Using a mobility device, reporting pain, poor self-rated health status, depression and anxiety scores were also associated with falling.

CONCLUSION AND IMPLICATIONS

Older adults' perceived balance problem is strongly associated with their fall risk. Perceived balance may be important to discuss with older adults to increase identification of fall risk. Older adults' perceived balance should be included in nursing fall risk assessments and fall prevention interventions. A focus on balance may increase older adults' engagement in fall prevention.

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

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

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

ABSTRACT

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

Advances in pediatric perioperative care using artificial intelligence

PURPOSE OF THIS REVIEW

This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers.

RECENT FINDINGS

The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools.

SUMMARY

The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.

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

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

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.

Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

AIMS AND OBJECTIVES

To investigate whether a low Braden Skin Score (BSS), reflecting an increased risk of pressure injury, could predict the risk of delirium in older patients in the intensive care unit (ICU).

BACKGROUND

Delirium, a common acute encephalopathy syndrome in older ICU patients, is associated with prolonged hospital stay, long-term cognitive impairment and increased mortality. However, few studies have explored the relationship between BSS and delirium.

DESIGN

Multicenter cohort study.

METHODS

The study included 24,123 older adults from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and 1090 older adults from the eICU Collaborative Research Database (eICU-CRD), all of whom had a record of BSS on admission to the ICU. We used structured query language to extract relevant data from the electronic health records. Delirium, the primary outcome, was primarily diagnosed by the Confusion Assessment Method for the ICU or the Intensive Care Delirium Screening Checklist. Logistic regression models were used to validate the association between BSS and outcome. A STROBE checklist was the reporting guide for this study.

RESULTS

The median age within the MIMIC-IV and eICU-CRD databases was approximately 77 and 75 years, respectively, with 11,195 (46.4%) and 524 (48.1%) being female. The median BSS at enrollment in both databases was 15 (interquartile range: 13, 17). Multivariate logistic regression showed a negative association between BSS on ICU admission and the prevalence of delirium. Similar patterns were found in the eICU-CRD database.

CONCLUSIONS

This study found a significant negative relationship between ICU admission BSS and the prevalence of delirium in older patients.

RELEVANCE TO CLINICAL PRACTICE

The BSS, which is simple and accessible, may reflect the health and frailty of older patients. It is recommended that BSS assessment be included as an essential component of delirium management strategies for older patients in the ICU.

NO PATIENT OR PUBLIC CONTRIBUTION

This is a retrospective cohort study, and no patients or the public were involved in the design and conduct of the study.

Enhancing pre-transplant cardiac assessment: Validation and utility of the CAD-LT score with CCTA in liver transplant candidates

BACKGROUND

Coronary computed tomography angiography (CCTA) is increasingly being used in the preoperative workup for liver transplantation (LT). We sought to assess the utility of integrating CCTA with the novel CAD-LT (Coronary Artery Disease in Liver Transplantation) score and its impact on reducing the need for invasive coronary angiography prior to LT.

METHODS

We conducted a retrospective cohort study of consecutive patients (age ≥ 18 years) who underwent CCTA for LT workup between 2011 and 2018 at the Victorian Liver Transplant Unit, Melbourne, Australia. CAD-LT scores, a traditional risk factor-based criteria, were calculated, and patients stratified as low-, intermediate- or high-risk.

RESULTS

Overall, 229 patients underwent CCTA. The mean age was 66 ± 5 years (82% male) with a modest-to-high risk factor burden (diabetes, 53%; hypertension, 46%; current or former smoker, 62%). The mean CAD-LT score of our cohort was 12.4 ± 4.0. No patients were classified as low-risk, 49 patients (21.4%) were deemed intermediate-risk and 180 patients (78.6%) were deemed high-risk. A high CAD-LT score (≥ 9) showed high sensitivity (95.3% [95% CI 86-98%]) and modest specificity (27.8% [95% CI 21-35%]) for the detection of obstructive coronary artery disease on CCTA, with a negative predictive value of 94%. Following multidisciplinary discussions, only 41 patients (18%) of patients proceeded to ICA of which 27% received percutaneous coronary intervention.

CONCLUSIONS

The use of CCTA in patients deemed intermediate- to high-risk by the CAD-LT score has the potential to reduce the need for invasive coronary angiography in patients undergoing LT workup.

Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning

BACKGROUND

Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk and phenotyping score for HFrEF, using Machine Learning (ML).

METHODS

Prior to modeling, patients in training and validation HF cohorts were assigned to 1 of 5 risk categories based on the composite endpoint of death, left ventricular assist device (LVAD) implantation or transplantation (DeLvTx), and rehospitalization in 6 months of follow-up using unsupervised clustering. The goal of our novel interpretable ML modeling approach, which is robust to missing data, was to predict this risk category (1, 2, 3, 4, or 5) using either invasive hemodynamics alone or a rich and inclusive feature set that included noninvasive hemodynamics (all features). The models were trained using the ESCAPE trial and validated using 4 advanced HF patient cohorts collected from previous trials, then compared with traditional ML models. Prediction accuracy for each of these 5 categories was determined separately for each risk category to generate 5 areas under the curve (AUCs, or C-statistics) for belonging to risk category 1, 2, 3, 4, or 5, respectively.

RESULTS

Across all outcomes, our models performed well for predicting the risk category for each patient. Accuracies of 5 separate models predicting a patient's risk category ranged from 0.896 +/- 0.074 to 0.969 +/- 0.081 for the invasive hemodynamics feature set and 0.858 +/- 0.067 to 0.997 +/- 0.070 for the all features feature set.

CONCLUSION

Novel interpretable ML models predicted risk categories with a high degree of accuracy. This approach offers a new paradigm for risk stratification that differs from prediction of a binary outcome. Prospective clinical evaluation of this approach is indicated to determine utility for selecting the best treatment approach for patients based on risk and prognosis.

Minimizing early catheter failure using a risk stratification model for peritoneal dialysis

BACKGROUND

Early catheter failure is the main reason for peritoneal dialysis (PD) failure, which often causes patients to withdraw from PD. Reducing the early catheter failure is critical to increase the acceptance of PD. The purpose of our study was to establish a risk stratification model to minimize early catheter failure.

METHODS

A retrospective study with patients underwent PD catheter placement from January 2013 to March 2022 was conducted. The primary outcome event was early catheter failure. Univariate and multivariable logistic regression were performed to select potential risk predictors. A risk stratification model and a clinical procedure were established. The effectiveness of the model was evaluated by external validation.

RESULTS

A total of 432 patients were finally enrolled in the study. The risk for early catheter failure was associated with younger age (odds ratio [OR], 0.930; 95% confidence interval [95% CI], 0.884 to 0.972; p = 0.002), lower body mass index (BMI) (OR, 0.797; 95% CI, 0.629 to 0.964; p = 0.036), and lower albumin (ALB) levels (OR, 0.881; 95% CI, 0.782 to 0.985; p = 0.036). The risk stratification model was established and performed great discrimination capability with AUC of 0.832 (cut-off value: 0.061, sensitivity: 0.853, specificity: 0.812). The model proved to be effective in external validation; the rate of early catheter failure was dropped off from 4.1% to 0%.

CONCLUSIONS

We established an effective risk stratification model, by which patients with high risk of early catheter failure could be precisely identified. The clinical procedure based on the model was proved to be helpful to minimize early catheter failure.

Establishment of gestational diabetes risk prediction model and clinical verification

OBJECTIVE

The present study aimed to evaluate the risk factors for gestational diabetes mellitus (GDM) and build and validate an early risk prediction model of GDM by comparing the differences in the indicators of the first trimester of pregnancy between pregnant women with GDM and non-gestational diabetes mellitus (NGDM). Thus, this study provided a theoretical basis for early intervention of GDM.

METHODS

A total of 6000 pregnant women who underwent a routine prenatal examination in Qinhuangdao Maternal and Child Health Hospital (Qinhuangdao City, Hebei Province, China) from January 2016-2022 were retrospectively selected and randomly divided into a modeling cohort (4200 cases) and validation cohort (1800 cases) at a ratio of 3:7. According to the results of oral glucose tolerance test (OGTT), they were divided into NGDM and GDM groups. The modeling cohort consisted of 2975 NGDM and 1225 GDM cases, while the validation cohort consisted of 1281 NGDM and 519 GDM cases. The differences in general conditions and laboratory indicators between different groups were compared, and logistic regression analysis was further used to establish a risk prediction model for GDM in the first trimester. The receiver operating characteristic curve (ROC) and Hosmer-Lemeshow (HL) tests were used to evaluate the prediction of the model efficacy.

RESULTS

Age, pre-pregnancy body mass index (BMI), glycosylated hemoglobin (HbA1c), blood uric acid (UA), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) in the first trimester were independent risk factors for GDM (P < 0.05). The model equation was Y = 1/{1 + exp[- (- 18.373 + age × 0.065 + BMI × 0.030 + first-trimester HbA1c × 2.519 + UA × 0.014 + TG × 0.224-HDL-C × 0.635)]}. The area under the ROC curve (AUC) of the model cohort was 0.803 (0.788-0.817), the sensitivity was 72.0%, and the specificity was 73.5%. The AUC of the validation cohort was 0.782 (0.759-0.806), the sensitivity was 68.6%, and the specificity was 73.8%. The P values of the HL test in both the training and validation sets were > 0.05, indicating a satisfactory model fit.

CONCLUSION

Age, pre-pregnancy BMI, HbA1C in early pregnancy, blood UA, TG, and HDL-C are independent risk factors for GDM. The risk prediction model established by combining age, pre-pregnancy BMI, and laboratory indicators in the first trimester can provide a theoretical basis for early screening, monitoring, and intervention of GDM high-risk pregnant women.

Variation in risk-adjusted cardiac intensive care unit (CICU) length of stay and the association with in-hospital mortality: An analysis from the Critical Care Cardiology Trials Network (CCCTN) registry

BACKGROUND

Previous studies have suggested that there is wide variability in cardiac intensive care unit (CICU) length of stay (LOS); however, these studies are limited by the absence of detailed risk assessment at the time of admission. Thus, we evaluated inter-hospital differences in CICU LOS, and the association between LOS and in-hospital mortality.

METHODS

Using data from the Critical Care Cardiology Trials Network (CCCTN) registry, we included 22,862 admissions between 2017 and 2022 from 35 primarily tertiary and quaternary CICUs that captured consecutive admissions in annual 2-month snapshots. The primary analysis compared inter-hospital differences in CICU LOS, as well as the association between CICU LOS and all-cause in-hospital mortality using a Fine and Gray competing risk model.

RESULTS

The overall median CICU LOS was 2.2 (1.1-4.8) days, and the median hospital LOS was 5.9 (2.8-12.3) days. Admissions in the longest tertile of LOS tended to be younger with higher rates of pre-existing comorbidities, and had higher Sequential Organ Failure Assessment (SOFA) scores, as well as higher rates of mechanical ventilation, intravenous vasopressor use, mechanical circulatory support, and renal replacement therapy. Unadjusted all-cause in-hospital mortality was 9.3%, 6.7%, and 13.4% in the lowest, intermediate, and highest CICU LOS tertiles. In a competing risk analysis, individual patient CICU LOS was correlated (r2 = 0.31) with a higher risk of 30-day in-hospital mortality. The relationship remained significant in admissions with heart failure, ST-elevation myocardial infarction and non-ST segment elevation myocardial infarction.

CONCLUSIONS

In a large registry of academic CICUs, we observed significant variation in CICU LOS and report that LOS is independently associated with all-cause in-hospital mortality. These findings could potentially be used to improve CICU resource utilization planning and refine risk prognostication in critically ill cardiovascular patients.

The combined risk predictive power of frailty and hypoalbuminemia in free tissue flap reconstruction: A cohort study of 34,571 patients from the NSQIP database

INTRODUCTION

Significant morbidity and mortality are hallmarks of the functional decline seen in physically frail patients. The modified frailty index 5 (mFI-5) represents a risk predictor score that has been validated as a comorbidity-based scale in surgery. Serum albumin levels of <3.5 g/dL (hypoalbuminemia) have also been implicated with poor postoperative outcomes. However, the association between these two parameters remains to be investigated. We aimed to elucidate the interdependence of preoperative albumin levels and frailty, as evaluated by the mFI-5 score, and its reliability to prognosticate postoperative results in free flap reconstruction (FFR).

METHODS

We conducted a multicenter, retrospective cohort study and accessed the ACS National Surgical Quality Improvement Program (ACS-NSQIP) from 2008 to 2021. We identified all adult patients (≥18 years of age) who underwent a FFR. We extracted perioperative data and lab values including albumin. Multivariable linear and logistic regression analyses were performed to identify independent risk predictors. Main outcomes involved mortality, length of hospital stay, reoperation, medical and surgical complications, and discharge destination within the 30-day postoperative period.

RESULTS

A total of 34,571 patients were included in the study, with an average age of 53.9 years (standard deviation [SD] 12.2) and an average body mass index (BMI) of 28.8 (SD 6.1). Of these patients, 7484 were male (21.6%), whereas 22,363 (64.7%) had no frailty (mFI = 0). Additionally, 9466 patients had a frailty score of 1 (27.4%), 2505 had a score of 2 (7.2%), 226 had a score of 3 (0.7%), and 11 had a score of 4 or higher (0.0%). Albumin levels were available for 16,250 patients (47.0%), and among them, 1334 (8.2%) had hypoalbuminemia. Regression analyses showed that higher mFI scores were independent predictors of any, surgical, and medical complications, as well as increased rates of reoperations, unplanned readmissions, and prolonged hospital stays. Hypoalbuminemia independently predicted any, surgical, and medical complications, and higher mortality, reoperation, and longer hospital stay. When both frailty and albumin levels (mFI-5 and albumin) were considered together, this combined assessment was found to be a more accurate predictor of all major outcomes (any, medical and surgical complications, mortality, and reoperation). Further, our analysis identified a weak negative correlation between serum albumin levels and mFI scores (Spearman R: -.1; p < .0001).

CONCLUSION

In conclusion, this cohort study highlights the association of hypoalbuminemia with adverse postoperative outcomes, including those not directly related to frailty. Simultaneously, higher mFI scores independently predicted outcomes not associated with hypoalbuminemia. Stemming from these findings, we recommend considering both serum albumin levels and frailty in patients receiving FFR. This perioperative algorithm may help provide more individualized planning including multidisciplinary care and pre and posthabilitation.

Key challenges and developments in wildlife ecological risk assessment: Problem formulation

Problem formulation (PF) is a critical initial step in planning risk assessments for chemical exposures to wildlife, used either explicitly or implicitly in various jurisdictions to include registration of new pesticides, evaluation of new and existing chemicals released to the environment, and characterization of impact when chemical releases have occurred. Despite improvements in our understanding of the environment, ecology, and biological sciences, few risk assessments have used this information to enhance their value and predictive capabilities. In addition to advances in organism-level mechanisms and methods, there have been substantive developments that focus on population- and systems-level processes. Although most of the advances have been recognized as being state-of-the-science for two decades or more, there is scant evidence that they have been incorporated into wildlife risk assessment or risk assessment in general. In this article, we identify opportunities to consider elevating the relevance of wildlife risk assessments by focusing on elements of the PF stage of risk assessment, especially in the construction of conceptual models and selection of assessment endpoints that target population- and system-level endpoints. Doing so will remain consistent with four established steps of existing guidance: (1) establish clear protection goals early in the process; (2) consider how data collection using new methods will affect decisions, given all possibilities, and develop a decision plan a priori; (3) engage all relevant stakeholders in creating a robust, holistic conceptual model that incorporates plausible stressors that could affect the targets defined in the protection goals; and (4) embrace the need for iteration throughout the PF steps (recognizing that multiple passes may be required before agreeing on a feasible plan for the rest of the risk assessment). Integr Environ Assess Manag 2024;20:658-673. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Can we screen for heart disease in children at public health centres? A multicentre observational study of screening for heart disease with a risk of sudden death in children

Sudden cardiac death in children is a rare event, but of great social significance. Generally, it is related to heart disease with a risk of sudden cardiac death (SCD), which may occur with cardiovascular symptoms and/or electrocardiographic markers; thus, a primary care paediatrician (PCP) could detect them. Therefore, we proposed a study that assesses how to put into practice and conduct a cardiovascular assessment within the routine healthy-child check-ups at six and twelve years of age; that reflects cardiovascular signs and symptoms, as well as the electrocardiographic alterations that children with a risk of SCD in the selected population present; and that assesses the PCP's skill at electrocardiogram (ECG) interpretation. In collaboration with PCPs, primary care nurses, and paediatric cardiologists, an observational, descriptive, multicentre, cross-sectional study was carried out in the Balearic Islands (Spain), from April 2021 to January 2022, inclusive. The PCPs gathered patient data through forms (medical record, electrocardiogram, and physical examination) and sent them to the investigator, together with the informed consent document and electrocardiogram. The investigator passed the electrocardiogram on to the paediatric cardiologists for reading, in an identical form to those the paediatricians had filled in. The variables were collected, and a descriptive analysis performed. Three paediatric cardiologists, twelve PCPs, and nine nurses from seven public health centres took part. They collected the data from 641 patients, but 233 patients did not participate (in 81.11% due to the PCP's workload). Therefore, the study coverage was around 64%, representing the quotient of the total number of patients who participated, divided by the total number of patients who were eligible for the study. We detected 30 patients with electrocardiographic alterations compatible with SCD risk. Nine of these had been examined by a paediatric cardiologist at some time (functional murmur in 8/9), five had reported shortness of breath with exercise, and four had reported a family history of sudden death. The physical examination of all the patients whose ECG was compatible with a risk of SCD was normal. Upon analysing to what extent the ECG results of the PCP and the paediatric cardiologist agreed, the percentage of agreement in the final interpretation (normal/altered) was 91.9%, while Cohen's kappa coefficient was 31.2% (CI 95%: 13.8-48.6%). The sensitivity of the ECG interpretation by the PCP to detect an ECG compatible with a risk of SCD was 29% and the positive predictive value 45%.     Conclusions: This study lays the foundations for future SCD risk screening in children, performed by PCPs. However, previously, it would be important to optimise their training in reading and interpreting paediatric ECGs. What is Known: • In Spain at present, there is a programme in place to detect heart disease with a risk of sudden death [1], but it targets only children who are starting on or are doing a physical activity as a federated sport. Implementing such screening programmes has proven effective in several countries [2]. However, several studies showed that the incidence of sudden cardiac death is no higher in children competing in sport activities than in those who do not do any sport [3]. This poses an ethical conflict, because at present, children who do not do any federated sport are excluded from screening. According to the revised literature, so far, only in two studies did they screen the child population at schools, and in both, they successfully detected patients with heart disease associated to the risk of sudden death [4, 5]. We have found no studies where the screening of these features was included within the routine healthy-child check-ups by primary care paediatricians. What is New: • We did not know whether-in our setting, at present-the primary care paediatrician could perform a screening method within the routine healthy-child check-ups, in order to detect presumably healthy children at risk of sudden cardiac death, as they present one of the SCD risks. In this regard, we proposed our project: to assess how to put into practice and conduct a cardiovascular assessment via SCD risk screening in the healthy child population by primary care paediatricians and appraise primary care paediatricians' skills in identifying the electrocardiographic alterations associated with SCD risk. The ultimate intention of this pilot study was to make it possible, in the future, to design and justify a study aimed at universalising cardiovascular screening and achieving a long-term decrease in sudden cardiac death events in children.

Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects

Despite advances in toxicity testing and the development of new approach methodologies (NAMs) for hazard assessment, the ecological risk assessment (ERA) framework for terrestrial wildlife (i.e., air-breathing amphibians, reptiles, birds, and mammals) has remained unchanged for decades. While survival, growth, and reproductive endpoints derived from whole-animal toxicity tests are central to hazard assessment, nonstandard measures of biological effects at multiple levels of biological organization (e.g., molecular, cellular, tissue, organ, organism, population, community, ecosystem) have the potential to enhance the relevance of prospective and retrospective wildlife ERAs. Other factors (e.g., indirect effects of contaminants on food supplies and infectious disease processes) are influenced by toxicants at individual, population, and community levels, and need to be factored into chemically based risk assessments to enhance the "eco" component of ERAs. Regulatory and logistical challenges often relegate such nonstandard endpoints and indirect effects to postregistration evaluations of pesticides and industrial chemicals and contaminated site evaluations. While NAMs are being developed, to date, their applications in ERAs focused on wildlife have been limited. No single magic tool or model will address all uncertainties in hazard assessment. Modernizing wildlife ERAs will likely entail combinations of laboratory- and field-derived data at multiple levels of biological organization, knowledge collection solutions (e.g., systematic review, adverse outcome pathway frameworks), and inferential methods that facilitate integrations and risk estimations focused on species, populations, interspecific extrapolations, and ecosystem services modeling, with less dependence on whole-animal data and simple hazard ratios. Integr Environ Assess Manag 2024;20:725-748. © 2023 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Superior Pedicle Breast Reduction: Multivariate Analysis of Complication Risk Factors and Building a Predictive Score in 1306 Patients

BACKGROUND

Breast reduction surgery for hypertrophy is one of the most commonly performed procedures in plastic surgery. This surgery exposes patients to complications that are well-documented in the literature. The objective of this study is to identify risk factors to establish an estimate of the risk of developing complications. The authors propose the first predictive score of postoperative complications, including continuous preoperative variables such as body mass index (BMI) and suprasternal notch-to-nipple distance (SSN:N).

METHODS

An analytic observational retrospective cohort study was conducted including 1306 patients who underwent superior pedicle reduction mammaplasty at the Rennes University Hospital (France) between January 1, 2011, and December 31, 2016. The primary endpoint was to study the association between known preoperative risk factors and occurrence of any complications using multivariable logistic regression to identify independent risk factors. A secondary endpoint was to establish a score to estimate a probability of occurrence of complications.

RESULTS

A total of 1306 patients were analyzed. Multivariable logistic regression showed three independent risk factors: active smoking [OR, 6.10 (95% CI: 4.23, 8.78); P < 0.0001], BMI [OR, 1.16 (95% CI: 1.11, 1.22); P < 0.0001], and SSN:N [OR, 1.14 (95% CI: 1.08, 1.21); P < 0.0001]. The Rennes Plastic Surgery Score estimating the occurrence of postoperative complications was determined, integrating regression coefficients of each risk factor.

CONCLUSIONS

Active smoking, BMI, and SSN:N distance are independent preoperative risk factors for the occurrence of breast reduction complications. The Rennes Plastic Surgery Score including the continuous values of BMI and SSN:N allows us to provide our patients with a reliable estimation of the risk of occurrence of these complications.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Risk, III.

Could paramedics use the HEART Pathway to identify patients at low-risk of myocardial infarction in the prehospital setting?

In the Emergency Department, patients with suspected myocardial infarction can be risk stratified using the HEART pathway, which has recently been amended for prehospital use and modified for the incorporation of a high-sensitivity cardiac troponin test. In a prospective analysis, the performance of both HEART pathways in the prehospital setting, with a high-sensitivity cardiac troponin test using 3 different thresholds, was evaluated for major adverse cardiac events at 30 days. We found that both low-risk HEART pathways, when using the most conservative cardiac troponin thresholds, approached but did not reach accepted rule-out performance in the Emergency Department.

Cumulative risk assessment for combinations of environmental and psychosocial stressors: A systematic review

With the growing awareness of stressors, cumulative risk assessment (CRA) has been proposed as a potential method to evaluate possible additive and synergistic effects of multiple stressors on human health, thus informing environmental regulation and protecting public health. However, CRA is still in its exploratory stage due to the lack of generally accepted quantitative approaches. It is an ideal time to summarize the existing progress to guide future research. To this end, a systematic review of the literature on CRA issues dealing with combinations of environmental and psychosocial stressors was conducted in this study. Using typology and bibliometric analysis, the body of knowledge, hot topics, and research gaps in this field were characterized. It was found that research topics and objectives mainly focus on qualitative analysis and community settings; more attention should be paid to the development of quantitative approaches and the inclusion of occupational settings. Further, the roles of air pollution and vulnerability factors in CRA have attracted the most attention. This study concludes with views on future prospects to promote theoretical and practical development in this field; specifically, CRA is a multifaceted topic that requires substantial collaborations with various stakeholders and substantial knowledge from multidisciplinary fields. This study presents an overall review as well as research directions worth investigating in this field, which provides a historical reference for future study. Integr Environ Assess Manag 2024;20:602-615. © 2023 SETAC.

Intermediate-tier options in the environmental risk assessment of plant protection products for soil invertebrates-Synthesis of a workshop

The European environmental risk assessment (ERA) of plant protection products follows a tiered approach. The approach for soil invertebrates currently consists of two steps, starting with a Tier 1 assessment based on reproduction toxicity tests with earthworms, springtails, and predatory mites. In case an unacceptable risk is identified at Tier 1, field studies can be conducted as a higher-tier option. For soil invertebrates, intermediate tiers are not implemented. Hence, there is limited possibility to include additional information for the ERA to address specific concerns when the Tier 1 fails, as an alternative to, for example, a field study. Calibrated intermediate-tier approaches could help to address risks for soil invertebrates with less time and resources but also with sufficient certainty. A multistakeholder workshop was held on 2-4 March 2022 to discuss potential intermediate-tier options, focusing on four possible areas: (1) natural soil testing, (2) single-species tests (other than standard species), (3) assessing recovery in laboratory tests, and (4) the use of assembled soil multispecies test systems. The participants acknowledged a large potential in the intermediate-tier options but concluded that some issues need to be clarified before routine application of these approaches in the ERA is possible, that is, sensitivity, reproducibility, reliability, and standardization of potential new test systems. The definition of suitable assessment factors needed to calibrate the approaches to the protection goals was acknowledged. The aims of the workshop were to foster scientific exchange and a data-driven dialog, to discuss how the different approaches could be used in the risk assessment, and to identify research priorities for future work to address uncertainties and strengthen the tiered approach in the ERA for soil invertebrates. This article outlines the background, proposed methods, technical challenges, difficulties and opportunities in the ERA, and conclusions of the workshop. Integr Environ Assess Manag 2024;20:780-793. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Interspecies scaling of toxicity reference values in human health versus ecological risk assessments: A critical review

Risk assessments that focus on anthropogenic chemicals in environmental media-whether considering human health or ecological effects-often rely on toxicity data from experimentally studied species to estimate safe exposures for species that lack similar data. Current default extrapolation approaches used in both human health risk assessments and ecological risk assessments (ERAs) account for differences in body weight between the test organisms and the species of interest, but the two default approaches differ in important ways. Human health risk assessments currently employ a default based on body weight raised to the three-quarters power. Ecological risk assessments for wildlife (i.e., mammals and birds) are typically based directly on body weight, as measured in the test organism and receptor species. This review describes differences in the experimental data underlying these default practices and discusses the many factors that affect interspecies variability in chemical exposures. The interplay of these different factors can lead to substantial departures from default expectations. Alternative methodologies for conducting more accurate interspecies extrapolations in ERAs for wildlife are discussed, including tissue-based toxicity reference values, physiologically based toxicokinetic and/or toxicodynamic modeling, chemical read-across, and a system of categorical defaults based on route of exposure and toxic mode of action. Integr Environ Assess Manag 2024;20:749-764. © 2023 SETAC.

Integrating emerging science to improve estimates of risk to wildlife from chemical exposure: What are the challenges?

Many jurisdictions require ecological risk assessments for terrestrial wildlife (i.e., terrestrial vertebrates) to assess potential adverse effects from exposure to anthropogenic chemicals. This occurs, for example, at contaminated sites and when new pesticides are proposed, and it occurs for chemicals that are in production and/or proposed for wide-scale use. However, guidance to evaluate such risks has not changed markedly in decades, despite the availability of new scientific tools to do so. In 2019, the Wildlife Toxicology World Interest Group of the Society of Environmental Toxicology and Chemistry (SETAC) initiated a virtual workshop that included a special session coincident with the annual SETAC North America meeting and which focused on the prospect of improving risk assessments for wildlife and improving their use in implementing chemical regulations. Work groups continued the work and investigated the utility of integrating emerging science and novel methods for improving problem formulation (WG1), exposure (WG2), toxicology (WG3), and risk characterization (WG4). Here we provide a summary of that workshop and the follow-up work, the regulations that drive risk assessment, and the key focus areas identified to advance the ability to predict risks of chemicals to wildlife. Integr Environ Assess Manag 2024;20:645-657. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Toxicological effects assessment for wildlife in the 21st century: Review of current methods and recommendations for a path forward

Model species (e.g., granivorous gamebirds, waterfowl, passerines, domesticated rodents) have been used for decades in guideline laboratory tests to generate survival, growth, and reproductive data for prospective ecological risk assessments (ERAs) for birds and mammals, while officially adopted risk assessment schemes for amphibians and reptiles do not exist. There are recognized shortcomings of current in vivo methods as well as uncertainty around the extent to which species with different life histories (e.g., terrestrial amphibians, reptiles, bats) than these commonly used models are protected by existing ERA frameworks. Approaches other than validating additional animal models for testing are being developed, but the incorporation of such new approach methodologies (NAMs) into risk assessment frameworks will require robust validations against in vivo responses. This takes time, and the ability to extrapolate findings from nonanimal studies to organism- and population-level effects in terrestrial wildlife remains weak. Failure to adequately anticipate and predict hazards could have economic and potentially even legal consequences for regulators and product registrants. In order to be able to use fewer animals or replace them altogether in the long term, vertebrate use and whole organism data will be needed to provide data for NAM validation in the short term. Therefore, it is worth investing resources for potential updates to existing standard test guidelines used in the laboratory as well as addressing the need for clear guidance on the conduct of field studies. Herein, we review the potential for improving standard in vivo test methods and for advancing the use of field studies in wildlife risk assessment, as these tools will be needed in the foreseeable future. Integr Environ Assess Manag 2024;20:699-724. © 2023 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Advancing exposure assessment approaches to improve wildlife risk assessment

The exposure assessment component of a Wildlife Ecological Risk Assessment aims to estimate the magnitude, frequency, and duration of exposure to a chemical or environmental contaminant, along with characteristics of the exposed population. This can be challenging in wildlife as there is often high uncertainty and error caused by broad-based, interspecific extrapolation and assumptions often because of a lack of data. Both the US Environmental Protection Agency (USEPA) and European Food Safety Authority (EFSA) have broadly directed exposure assessments to include estimates of the quantity (dose or concentration), frequency, and duration of exposure to a contaminant of interest while considering "all relevant factors." This ambiguity in the inclusion or exclusion of specific factors (e.g., individual and species-specific biology, diet, or proportion time in treated or contaminated area) can significantly influence the overall risk characterization. In this review, we identify four discrete categories of complexity that should be considered in an exposure assessment-chemical, environmental, organismal, and ecological. These may require more data, but a degree of inclusion at all stages of the risk assessment is critical to moving beyond screening-level methods that have a high degree of uncertainty and suffer from conservatism and a lack of realism. We demonstrate that there are many existing and emerging scientific tools and cross-cutting solutions for tackling exposure complexity. To foster greater application of these methods in wildlife exposure assessments, we present a new framework for risk assessors to construct an "exposure matrix." Using three case studies, we illustrate how the matrix can better inform, integrate, and more transparently communicate the important elements of complexity and realism in exposure assessments for wildlife. Modernizing wildlife exposure assessments is long overdue and will require improved collaboration, data sharing, application of standardized exposure scenarios, better communication of assumptions and uncertainty, and postregulatory tracking. Integr Environ Assess Manag 2024;20:674-698. © 2023 SETAC.

Using emerging science to inform risk characterizations for wildlife within current regulatory frameworks

Many jurisdictions have regulatory frameworks that seek to reduce the effects of environmental exposures of anthropogenic chemicals on terrestrial wildlife (i.e., mammals, birds, reptiles, and amphibians). The frameworks apply for new and existing chemicals, including pesticides (prospective assessments), and to environmental contamination from releases (retrospective risk assessments). Relatively recently, there have been many scientific advances that could improve risk estimates for wildlife. Here, we briefly describe current regulations from North America (United States and Canada) and from Europe that include risk assessments for wildlife to ascertain whether they are conducive to the use of emerging science and new methods. We also provide examples where new and emerging science may be used to improve wildlife risk characterization and identify areas in need of future research. Integr Environ Assess Manag 2024;20:765-779. © 2024 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management © 2024 Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

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

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

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

BACKGROUND

[18F]FDG-PET/CT is used for staging and treatment planning in patients with locally advanced cervical cancer (LACC). We studied if a PET-based prediction model could provide additional risk stratification beyond International Federation of Gynaecology and Obstetrics (FIGO) staging in our population with LACC to aid treatment decision making.

METHODS

In total, 183 patients with LACC treated with chemoradiation between 2013 and 2018 were included. Patients were treated according to FIGO 2009 and retrospectively reclassified according to FIGO 2018 staging system. After validation of an existing PET-based prediction model, the predicted recurrent free survival (RFS), disease specific survival (DSS) and overall survival (OS) at 1, 3, and 5 years, based on metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax) and highest level of [18F]FDG-positive node was calculated. Then the observed survival was compared to the predicted survival. An area under the curve (AUC) close to or higher than 0.7 was considered adequate for accurate prediction. The Youden (J) index defined survival chance cutoff values for low and high risk groups.

RESULTS

All AUC values for the comparison between predicted and observed outcomes were > 0.7 except for 5-year RFS and for 5-year OS which were close to 0.7 (0.684 and 0.650 respectively). Cutoff values for low and high risk survival chance were 0.44 for the 3-year RFS and 0.47 for the 5-year OS. The FIGO 2009 system could not differentiate between the risk profiles. After reclassification according to FIGO 2018, all patients with stage IIIC2 and IVB fell in the high risk and almost all patients with stages IB2-IIIB and IVA in the low risk group. In patients with stage IIIC1 disease the FIGO stage cannot discriminate between the risk profiles.

CONCLUSIONS

Low and high risk patients with LACC can be identified with the PET-based prediction model. In particular patients with stage IIIC1 need additional risk stratification besides the FIGO 2018 staging. The Kidd model could be a useful tool to aid treatment decision making in these patients. Our results also support the choice of [18F]FDG-PET/CT imaging in patients with LACC.

Risk stratification and prognosis prediction using cardiac biomarkers in COVID-19: a single-centre retrospective cohort study

OBJECTIVE

There is a need for a robust tool to stratify the patient's risk with COVID-19. We assessed the prognostic values of cardiac biomarkers for COVID-19 patients.

METHODS

This is a single-centre retrospective cohort study. Consecutive laboratory-confirmed COVID-19 patients admitted to the Kobe City Medical Center General Hospital from July 2020 to September 2021 were included. We obtained cardiac biomarker values from electronic health records and institutional blood banks. We stratified patients with cardiac biomarkers as high-sensitive troponin I (hsTnI), N-terminal pro-B-type natriuretic peptide (NT-proBNP), creatine kinase (CK) and CK myocardial band (CK-MB), using the clinically relevant thresholds. Prespecified primary outcome measure was all-cause death.

RESULTS

A total of 917 patients were included. hsTnI, NT-proBNP, CK and CK-MB were associated with the significantly higher cumulative 30-day incidence of all-cause death (hsTnI: <5.0 ng/L group; 4.3%, 5.0 ng/L-99%ile upper reference limit (URL) group; 8.8% and ≥99% ile URL group; 25.2%, p<0.001. NT-proBNP: <125 pg/mL group; 5.3%, 125-900 pg/mL group; 10.5% and ≥900 pg/mL group; 31.9%, p<0.001. CK:

CONCLUSIONS

Elevation of cardiac biomarkers was associated with poor prognosis of COVID-19 patients.

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Key Words

• COVID-19
• Cardiology
• REGISTRIES
• Risk Factors

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MESH Headings

• Humans
• COVID-19/mortality
• COVID-19/blood
• COVID-19/diagnosis
• Female
• Male
• Biomarkers/blood
• Retrospective Studies
• Prognosis
• Aged
• Natriuretic Peptide, Brain/blood
• Peptide Fragments/blood
• Troponin I/blood
• Middle Aged
• Risk Assessment/methods
• Creatine Kinase, MB Form/blood
• SARS-CoV-2
• Creatine Kinase/blood
• Aged, 80 and over

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Affiliation(s)

Madoka Sano Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Toshiaki Toyota Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Takeshi Morimoto Center for Clinical Research and Innovation, Kobe City Medical Center General Hospital, Kobe, Japan
Yu Noguchi Department of Cardiovascular Medicine, Tenri Hospital, Tenri, Japan
Ryo Shigeno Department of Cardiovascular Medicine, The Sakakibara Heart Institute of Okayama, Okayama, Japan
Ryosuke Murai Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Taiji Okada Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Yasuhiro Sasaki Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Tomohiko Taniguchi Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Kitae Kim Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Atsushi Kobori Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Natsuhiko Ehara Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Makoto Kinoshita Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Asako Doi Department of Infectious disease, Kobe City Medical Center General Hospital, Kobe, Japan
Keisuke Tomii Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Yasuki Kihara Kobe City Medical Center General Hospital, Kobe, Japan
Yutaka Furukawa Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan

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

BACKGROUND

Facilities providing health- and social services for youth are commonly faced with the need for assessment and management of violent behavior. These providers often experience shortage of resources, compromising the feasibility of conducting comprehensive violence risk assessments. The Violence Risk Assessment Checklist for Youth aged 12-18 (V-RISK-Y) is a 12-item violence risk screening instrument developed to rapidly identify youth at high risk for violent behavior in situations requiring expedient evaluation of violence risk. The V-RISK-Y instrument was piloted in acute psychiatric units for youth, yielding positive results of predictive validity. The aim of the present study was to assess the interrater reliability of V-RISK-Y in child and adolescent psychiatric units and acute child protective services institutions.

METHODS

A case vignette study design was utilized to assess interrater reliability of V-RISK-Y. Staff at youth facilities (N = 163) in Norway and Sweden scored V-RISK-Y for three vignettes, and interrater reliability was assessed with the intraclass correlation coefficient (ICC).

RESULTS

Results indicate good interrater reliability for the sum score and Low-Moderate-High risk level appraisal across staff from the different facilities and professions. For single items, interrater reliability ranged from poor to excellent.

CONCLUSIONS

This study is an important step in establishing the psychometric properties of V-RISK-Y. Findings support the structured professional judgment tradition the instrument is based on, with high agreement on the overall risk assessment. This study had a case vignette design, and the next step is to assess the reliability and validity of V-RISK-Y in naturalistic settings.

Practising prioritisation: exploring variation in applying a clinical pharmacy risk stratification tool

OBJECTIVES

To evaluate the use of a risk stratification tool and explore the contributing factors to variation in practice by clinical pharmacists.

METHODS

The quantitative phase was a prospective evaluation of adherence to the risk stratification tool. Patients were selected by convenience sampling from medical wards across two hospital sites. Researchers applied the risk stratification tool to each patient, documented the code, accessed health records in subsequent days, and recorded the code assigned by the pharmacist. These codes were compared. The kappa (κ) coefficient test was performed using SPSS software as a statistical measure of agreement. The qualitative phase was designed using focus groups with clinical pharmacists. One focus group was conducted at each of the two study sites. Participants were grouped to ensure a mix of experience levels. To augment the discussion, participants completed a short survey. Focus groups were recorded and a thematic analysis undertaken.

RESULTS

The final cohort for quantitative analysis was 73. Researchers and pharmacists allocated the same code to 19 (26%) patients. The highest match rate was observed between researchers and rotational pharmacists. The κ coefficient was 0.039 (slight agreement) with p value=0.52 (not significant). Ten pharmacists participated in the focus groups: three from site 1 and seven from site 2. All participants reported using the principles of the risk stratification tool every day, but they rarely accessed the tool. Pharmacists reported using the tool as a workload management and communication system.

CONCLUSIONS

Variation in application of the risk stratification tool exists among pharmacists. Focus group participants described multiple scenarios where non-patient factors were considered in assigning a priority code for the patient. A schedule of regular review of the criteria; training and peer review; tool validation; and research identifying the relationship between structured professional judgement and risk stratification tools is recommended.

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

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

Development of the Korean construction job exposure matrix (KoConJEM) based on experts' judgment using the 60 consolidated occupations for construction workers

BACKGROUND

This study was conducted as an effort to develop a Korean construction job exposure matrix (KoConJEM) based on 60 occupations recently consolidated by the construction workers mutual aid association for use by the construction industry.

METHODS

The probability, intensity, and prevalence of exposure to 26 hazardous agents for 60 consolidated occupations were evaluated as binary (Yes/No) or four categories (1 to 4) by 30 industrial hygiene experts. The score for risk was calculated by multiplying the exposure intensity by the prevalence of exposure. Fleiss' kappa for each hazardous agent and occupation was used to determine agreement among the 30 experts. The JEM was expressed on a heatmap and a web-based dashboard to facilitate comparison of factors affecting exposure according to each occupation and hazardous agent.

RESULTS

Awkward posture, heat/cold, heavy lifting, and noise were hazardous agents regarded as exposure is probable by at least one or more experts in all occupations, while exposure to asphalt fumes was considered hazardous in the smallest number of occupations (n = 5). Based on the degree of agreement among experts, more than half of the harmful factors and most occupations showed fair to good results. The highest risk value was 16 for awkward posture for most occupations other than safety officer.

CONCLUSIONS

The KoConJEM provides information on the probability, intensity, and prevalence of exposure to harmful factors, including most occupations employing construction workers; therefore, it may be useful in the conduct of epidemiological studies on assessment of health risk for construction workers.

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

BACKGROUND

Older adults are increasingly susceptible to prolonged illness, multiple chronic diseases, and disabilities, which can lead to the coexistence of multimorbidity and frailty. Multimorbidity may result in various noncommunicable disease (NCD) patterns or configurations that could be associated with frailty and death. Mortality risk may vary depending on the presence of specific chronic diseases configurations or frailty.

METHODS

The aim was to examine the impact of NCD configurations on mortality risk among older adults with distinct frailty phenotypes. The population was analyzed from the Costa Rican Longevity and Healthy Aging Study Cohort (CRELES). A total of 2,662 adults aged 60 or older were included and followed for 5 years. Exploratory factor analysis and various clustering techniques were utilized to identify NCD configurations. The frequency of NCD accumulation was also assessed for a multimorbidity definition. Frailty phenotypes were set according to Fried et al. criteria. Kaplan‒Meier survival analyses, mortality rates, and Cox proportional hazards models were estimated.

RESULTS

Four different types of patterns were identified: 'Neuro-psychiatric', 'Metabolic', 'Cardiovascular', and 'Mixt' configurations. These configurations showed a higher mortality risk than the mere accumulation of NCDs [Cardiovascular HR:1.65 (1.07-2.57); 'Mixt' HR:1.49 (1.00-2.22); ≥3 NCDs HR:1.31 (1.09-1.58)]. Frailty exhibited a high and constant mortality risk, irrespective of the presence of any NCD configuration or multimorbidity definition. However, HRs decreased and lost statistical significance when phenotypes were considered in the Cox models [frailty + 'Cardiovascular' HR:1.56 (1.00-2.42); frailty + 'Mixt':1.42 (0.95-2.11); and frailty + ≥ 3 NCDs HR:1.23 (1.02-1.49)].

CONCLUSIONS

Frailty accompanying multimorbidity emerges as a more crucial indicator of mortality risk than multimorbidity alone. Therefore, studying NCD configurations is worthwhile as they may offer improved risk profiles for mortality as alternatives to straightforward counts.

Health risk assessment for soil radioactivity around Shidaowan nuclear power plant in Shandong, China

Monitoring radioactivity levels in the environment around nuclear power plants is of great significance to assessing environmental safety and impact. Shidaowan nuclear power plant is currently undergoing commissioning; however, the baseline soil radioactivity is unknown. The naturally occurring radionuclides 238U, 232Th, 226Ra and 40K, and artificial radionuclide (AR) 137Cs in soil samples around the Shidaowan nuclear power plant were measured to establish the baseline levels. Human health hazard indices such as external hazard indices (Hex), Radium equivalent (Raeq), outdoor absorbed dose rate (Dout), annual effective dose (AED) and excess lifetime cancer risk (ELCR) were estimated. The average concentration of 232Th, 40K, 137Cs, 238U and 226Ra were 42.6 ± 15, 581 ± 131, 0.68 ± 0.38, 40.13 ± 9.07 and 40.8 ± 12.8 Bq per kg, respectively. The average Hex, Raeq, Dout, AED and ELCR were 0.40, 146 Bq per kg, 68.8 nGy per h, 0.09 mSv per y and 3.29E-04, respectively. These data showed an acceptable level of risk to residents near the nuclear power plant and that the current radioactivity in the soil may not pose immediate harm to residents living close to the nuclear power plant. The observed lower AED and 40 K and 137Cs concentrations were comparable to other studies, whilst ELCR was higher than the world average of 2.9E-04. The commissioning of the Shidaowan nuclear power plant is potentially safe for the surrounding residents; further continuous monitoring is recommended.

Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study

BACKGROUND

In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic.

METHODS

Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically).

RESULTS

The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)).

CONCLUSIONS

The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.

Comparison of the performance of cardiovascular risk prediction tools in rural India: the Rishi Valley Prospective Cohort Study

AIMS

We compared the performance of cardiovascular risk prediction tools in rural India.

METHODS AND RESULTS

We applied the World Health Organization Risk Score (WHO-RS) tools, Australian Risk Score (ARS), and Global risk (Globorisk) prediction tools to participants aged 40-74 years, without prior cardiovascular disease, in the Rishi Valley Prospective Cohort Study, Andhra Pradesh, India. Cardiovascular events during the 5-year follow-up period were identified by verbal autopsy (fatal events) or self-report (non-fatal events). The predictive performance of each tool was assessed by discrimination and calibration. Sensitivity and specificity of each tool for identifying high-risk individuals were assessed using a risk score cut-off of 10% alone or this 10% cut-off plus clinical risk criteria of diabetes in those aged >60 years, high blood pressure, or high cholesterol. Among 2333 participants (10 731 person-years of follow-up), 102 participants developed a cardiovascular event. The 5-year observed risk was 4.4% (95% confidence interval: 3.6-5.3). The WHO-RS tools underestimated cardiovascular risk but the ARS overestimated risk, particularly in men. Both the laboratory-based (C-statistic: 0.68 and χ2: 26.5, P = 0.003) and non-laboratory-based (C-statistic: 0.69 and χ2: 20.29, P = 0.003) Globorisk tools showed relatively good discrimination and agreement. Addition of clinical criteria to a 10% risk score cut-off improved the diagnostic accuracy of all tools.

CONCLUSION

Cardiovascular risk prediction tools performed disparately in a setting of disadvantage in rural India, with the Globorisk performing best. Addition of clinical criteria to a 10% risk score cut-off aids assessment of risk of a cardiovascular event in rural India.

LAY SUMMARY

In a cohort of people without prior cardiovascular disease, tools used to predict the risk of cardiovascular events varied widely in their ability to accurately predict who would develop a cardiovascular event.The Globorisk, and to a lesser extent the ARS, tools could be appropriate for this setting in rural India.Adding clinical criteria, such as sustained high blood pressure, to a cut-off of 10% risk of a cardiovascular event within 5 years could improve identification of individuals who should be monitored closely and provided with appropriate preventive medications.

Validation of the European SCORE2 models in a Canadian primary care cohort

AIMS

Systematic Coronary Risk Evaluation Model 2 (SCORE2) was recently developed to predict atherosclerotic cardiovascular disease (ASCVD) in Europe. Whether these models could be used outside of Europe is not known. The objective of this study was to test the validity of SCORE2 in a large Canadian cohort.

METHODS AND RESULTS

A primary care cohort of persons with routinely collected electronic medical record data from 1 January 2010 to 31 December 2014, in Ontario, Canada, was used for validation. The SCORE2 models for younger persons (YP) were applied to 57 409 individuals aged 40-69 while the models for older persons (OPs) were applied to 9885 individuals 70-89 years of age. Five-year ASCVD predictions from both the uncalibrated and low-risk region recalibrated SCORE2 models were evaluated. The C-statistic for SCORE2-YP was 0.74 in women and 0.69 in men. The uncalibrated SCORE2-YP overestimated risk by 17% in women and underestimated by 2% in men. In contrast, the low-risk region recalibrated model demonstrated worse calibration, overestimating risk by 100% in women and 36% in men. The C-statistic for SCORE2-OP was 0.64 and 0.62 in older women and men, respectively. The uncalibrated SCORE2-OP overestimated risk by more than 100% in both sexes. The low-risk region recalibrated model demonstrated improved calibration but still overestimated risk by 60% in women and 13% in men.

CONCLUSION

The performance of SCORE2 to predict ASCVD risk in Canada varied by age group and depended on whether regional calibration was applied. This underscores the necessity for validation assessment of SCORE2 prior to implementation in new jurisdictions.

ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain

BACKGROUND

ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4's risk stratification of simulated patients with acute nontraumatic chest pain.

METHODS

Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency.

RESULTS

ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45-48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605).

CONCLUSION

While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients.

Identifying opportunities for high resolution pesticide usage data to improve the efficiency of endangered species pesticide risk assessment

The US pesticide registration and review process requires regular re-assessment of the risk of pesticide use to species listed under the Endangered Species Act (ESA), yet current assessment methods are inefficient when applied to hundreds of pesticides potentially impacting multiple species across a continent. Thus, many pesticides remain on the market without complete review. We assessed the value of using high resolution pesticide usage data in the risk assessment process to rapidly improve process efficiency. By using data available only in California, we found that high resolution data increased the number of species deemed not likely to be adversely affected by pesticides from <5 % to nearly 50 %. Across the contiguous US, we predicted that 48 % of species would be deemed not likely to be adversely affected using high resolution data, compared to 20 % without. However, if such data were available in just 11 states, 68 % of the available gains in efficiency could be obtained. Overall, using existing high-resolution data in California and a focused collection of such information from 11 other states could reduce risk assessment burden across the contiguous U.S. by one-quarter.

Prioritizing resource allocation to reduce adverse effects of pesticide risk for endangered species

The use of pesticides promotes food security because of the multiple benefits it brings to agriculture, such as reduction in crop losses. However, the use of pesticides can be potentially harmful to non-target species. In the U.S., the Environmental Protection Agency regulates the use of pesticides to manage the risks associated with these agents and to protect species under the Endangered Species Act. As part of these regulations, pesticides must be registered and then reviewed every 15 years to ensure the use conditions are updated with the best available data. The registration and review process can invoke corrective measures to ensure protection of endangered species. However, the registration review process is highly resource and time consuming. There is currently a backlog of unreviewed pesticides, leaving a large quantity of pesticides without updated use conditions to protect species. Identifying ways to streamline this process is urgently needed. We develop a sequencing approach to address the risk assessment bottleneck in the pesticide registration and review process and identify species that would benefit most from detailed assessments. We then demonstrate the magnitude of potential efficiencies using this sequencing process for 61 terrestrial listed species in the state of California. Our results show a consistent ranking of listed species according to their relative benefits from assessment, with 90 % of the species being robustly classified across scenarios in the sensitivity analysis. We found that prioritizing the assessment of a small group of species could potentially result in high conservation benefits, and identify species in need of more detailed data for a robust sequencing. We examine how a sequencing approach can guide decisions about what species might benefit most from different levels of assessment. Our results demonstrate the conservation benefits of employing a sequencing approach to prioritize the allocation of limited resources for endangered species.

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

OBJECTIVE

This study aims to correlate the RAPID score with the 3-month survival and surgical results of patients undergoing lung decortication with stage III pleural empyema.

METHODS

This was a retrospective study with the population of patients with pleural empyema who underwent pulmonary decortication between January 2019 and June 2022. Data were collected from the institution's database, and patients were classified as low, medium, and high risk according to the RAPID score. The primary outcome was 3-month mortality. Secondary outcomes were the length of hospital stay, readmission rate, and the need for pleural re-intervention.

RESULTS

Of the 34 patients with pleural empyema, according to the RAPID score, patients were stratified into low risk (23.5 %), medium risk (47.1 %), and high risk (29.4 %). The high-risk group had a 3-month mortality of 40 %, while the moderate-risk group had a 6.25 % and the low-risk group had no deaths within 90 days, confirming a good correlation with the RAPID score (p < 0.05). Sensitivity and specificity for the primary outcome in the high-risk score were 80.0 % and 79.3 %, respectively. The secondary outcomes did not reach statistical significance.

CONCLUSIONS

In this retrospective series, the RAPID score had a good correlation with 3-month mortality in patients undergoing lung decortication. The morbidity indicators did not reach statistical significance. The present data justifies further studies to explore the capacity of the RAPID score to be used as a selection tool for treatment modality in patients with stage III pleural empyema.

Critical Assessment of the Management of Unstable Angina in a Specialized Cardiology Emergency Room

BACKGROUND

The management of unstable angina (UA) presents a challenge due to its subjective diagnosis and limited representation in randomized clinical trials that inform current practices.

OBJECTIVES

This study aims to identify key factors associated with the indication for invasive versus non-invasive stratification in this population and to evaluate factors associated with stratification test results.

METHODS

This retrospective cohort study included patients hospitalized with UA over a consecutive 20-month period. To assess factors associated with stratification strategies, patients were divided into invasive stratification (coronary angiography) and non-invasive stratification (other methods) groups. For the analysis of factors related to changes in stratification tests, patients were categorized into groups with or without obstructive coronary artery disease (CAD) or ischemia, as per the results of the requested tests. Comparisons between groups and multiple logistic regression analyses were performed, with statistical significance set at a 5% level.

RESULTS

A total of 729 patients were included, with a median age of 63 years and a predominance of males (64.6%). Factors associated with invasive stratification included smoking (p = 0.001); type of chest pain (p < 0.001); "crescendo" pain (p = 0.006); TIMI score (p = 0.006); HEART score (p = 0.011). In multivariate analysis, current smokers (OR 2.23, 95% CI 1.13-4.8), former smokers (OR 2.19, 95% CI 1.39-3.53), and type A chest pain (OR 3.39, 95% CI 1.93-6.66) were independently associated. Factors associated with obstructive CAD or ischemia included length of hospital stay (p < 0.001); male gender (p = 0.032); effort-induced pain (p = 0.037); Diamond-Forrester score (p = 0.026); TIMI score (p = 0.001). In multivariate analysis, only chest pain (type B chest pain: OR 0.6, 95% CI 0.38-0.93, p = 0.026) and previous CAD (OR 1.42, 95% CI 1.01-2.0, p = 0.048) were independently associated.

CONCLUSION

The type of chest pain plays a crucial role not only in the diagnosis of UA but also in determining the appropriate treatment. Our results highlight the importance of incorporating pain characteristics into prognostic scores endorsed by guidelines to optimize UA management.

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

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

Repeated Measurement of the Novel Atrial Biomarker BMP10 (Bone Morphogenetic Protein 10) Refines Risk Stratification in Anticoagulated Patients With Atrial Fibrillation: Insights From the ARISTOTLE Trial

BACKGROUND

BMP10 (bone morphogenic protein 10) has emerged as a novel biomarker associated with the risk of ischemic stroke and other outcomes in patients with atrial fibrillation (AF). The study aimed to determine if repeated BMP10 measurements improve prognostication of cardiovascular events in patients with AF.

METHODS AND RESULTS

BMP10 was measured using a prototype Elecsys immunoassay in plasma samples collected at randomization and after 2 months in patients with AF randomized to apixaban or warfarin in the ARISTOTLE (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation) trial (n=2878). Adjusted Cox-regression models were used to evaluate the association between 2-month BMP10 levels and outcomes. BMP10 levels increased by 7.8% (P<0.001) over 2 months. The baseline variables most strongly associated with BMP10 levels at 2 months were baseline BMP10 levels, body mass index, sex, age, creatinine, diabetes, warfarin treatment, and AF-rhythm. During median 1.8 years follow-up, 34 ischemic strokes/systemic embolism, 155 deaths, and 99 heart failure hospitalizations occurred. Comparing the third with the first sample quartile, higher BMP10 levels at 2 months were associated with higher risk of ischemic stroke (hazard ratio [HR], 1.33 [95% CI, 0.67-2.63], P=0.037), heart failure (HR, 1.91 [95% CI, 1.17-3.12], P=0.012) and all-cause death (HR, 1.61 [95% CI, 1.17-2.21], P<0.001). Adding BMP10 levels at 2 months on top of established risk factors and baseline BMP10 levels improved the C-indices for ischemic stroke/systemic embolism (from 0.73 to 0.75), heart failure hospitalization (0.76-0.77), and all-cause mortality (0.70-0.72), all P<0.05.

CONCLUSIONS

Elevated levels of BMP10 at 2 months strengthened the associations with the risk of ischemic stroke, hospitalization for heart failure, and all-cause mortality. Repeated measurements of BMP10 may further refine risk stratification in patients with AF.