Underestimation Of Risk Research Articles

Risks of Powder Flow Obstruction in Hopper and Bin Discharge in Solid Dosage Form Manufacture should be Predicted Under The Active Stress State

Discharge of powder from a hopper or bin is a common operation in solid dosage form manufacture. Powder flow obstruction during hopper/bin discharge, such as arching or ratholing, remains an outstanding risk and cannot be reliably diagnosed using the existing flow function coefficient-based method. In this study, we showed that the major principal stress (σ1) at the bin outlet is required for an accurate prediction of powder flow obstruction risks. We noted that powder is susceptible to flow obstruction when the unconfined yield strength exceeds the stress facilitating powder failure. We presented a complete model to calculate the stress conditions and subsequently predict flow obstruction risks in hopper/bin discharge based on this criterion. The method was experimentally verified by hopper/bin discharge experiments encompassing 10 powder blends and 2 equipment systems. Importantly, we showed that the active stress state assumption should be employed for the powder flow obstruction prediction because σ1 is high and powder is more susceptible to flow obstruction. Prediction under the passive stress state can lead to significant under-estimation of flow obstruction risks. Therefore, the hopper design protocol, which assumes the passive stress state in arching prediction, should not be indiscriminately used toward pharmaceutical powder flow applications.

Perspective of primary care physicians on the management of dyslipidaemia in patients without atherosclerotic cardiovascular disease: a cross-sectional study

Evidence suggests many dyslipidemic patients do not reach target low-density lipoprotein and cholesterol (LDL-C) levels in primary health care. We aimed to describe the pharmacologic therapeutic management of dyslipidemia in patients without established atherosclerotic cardiovascular diseases (ASCVD) from the primary care physician's perspective in Spain. We conducted a cross-sectional study through an online survey directed to primary care physicians to explore their therapeutic management of dyslipidemia in patients without ASCVD, focusing on their knowledge and adherence to the 2019 European Society of Cardiology/ European Atherosclerosis Society (ESC/EAS) guidelines and their perspective concerning the barriers to achieving LDL-C therapeutic targets. In total, 279 primary care physicians completed the survey. Most interviewees (80.65%) stated they had already adopted the 2019 ESC/EAS guidelines in their clinical practice. Nevertheless, around 30% adhered to therapeutic targets by previous ESC/EAS guidelines (2016), and most treated their patients mainly with statins in monotherapy, prescribing doses below the maximum tolerated. Additionally, 50.18% were classified as low adherence to the 2019 ESC/EAS guidelines, especially to the treatment algorithm. According to the physicians, the underestimation of patients' cardiovascular risk and the reluctance to increase doses or use combined therapy were the most critical barriers to achieving LDL-C targets. Although primary care physicians in our survey reported adherence to the 2019 ESC/EAS guidelines recommendations, our observations indicate they need to integrate them better into their clinical practice.

Communicating Uncertainty and Risk in Air Quality Maps.

Environmental sensors provide crucial data for understanding our surroundings. For example, air quality maps based on sensor readings help users make decisions to mitigate the effects of pollution on their health. Standard maps show readings from individual sensors or colored contours indicating estimated pollution levels. However, showing a single estimate may conceal uncertainty and lead to underestimation of risk, while showing sensor data yields varied interpretations. We present several visualizations of uncertainty in air quality maps, including a frequency-framing "dotmap" and small multiples, and we compare them with standard contour and sensor-based maps. In a user study, we find that including uncertainty in maps has a significant effect on how much users would choose to reduce physical activity, and that people make more cautious decisions when using uncertainty-aware maps. Additionally, we analyze think-aloud transcriptions from the experiment to understand more about how the representation of uncertainty influences people's decision-making. Our results suggest ways to design maps of sensor data that can encourage certain types of reasoning, yield more consistent responses, and convey risk better than standard maps.

Multimodal climate change prediction in a monsoon climate

Abstract The uncertainty in the climate projection arising from various climate models is very common, and averaging such results poses a risk of underestimation or sometimes overestimation of impact in magnitude and frequency. Further, the performance of various climate models in monsoon degrades drastically due to the skewed nature. Under these circumstances, the performance of the climate model in the monsoon and non-monsoon periods is critical for accurate assessment. A multimodal approach has been used in the present work to quantify the uncertainty involved in the climate model using reliability ensemble averaging (REA). Based on AR6 of IPCC, the ensemble of 26 global climate models (GCMs) was used to evaluate the model performance and possible change in seasonal precipitation in four cities with distinct climate conditions, namely, Coimbatore, Rajkot, Udaipur, and Siliguri. The results show that non-monsoon and monsoon rainfall are expected to increase in all the regions. Most of the models perform poorly in simulating monsoon climate, especially in the monsoon period and are highly inconsistent spatially. The study also finds that the model performance is largely linked to the ratio of natural variability and mean.

Cyclical underreporting of seizures in patient-based seizure documentation.

Circadian and multidien cycles of seizure occurrence are increasingly discussed as to their biological underpinnings and in the context of seizure forecasting. This study analyzes if patient reported seizures provide valid data on such cyclical occurrence. We retrospectively studied if circadian cycles derived from patient-based reporting reflect the objective seizure documentation in 2003 patients undergoing in-patient video-EEG monitoring. Only 24.1% of more than 29000 seizures documented were accompanied by patient notifications. There was cyclical underreporting of seizures with a maximum during nighttime, leading to significant deviations in the circadian distribution of seizures. Significant cyclical deviations were found for focal epilepsies originating from both, frontal and temporal lobes, and for different seizure types (in particular, focal unaware and focal to bilateral tonic-clonic seizures). Patient seizure diaries may reflect a cyclical reporting bias rather than the true circadian seizure distributions. Cyclical underreporting of seizures derived from patient-based reports alone may lead to suboptimal treatment schemes, to an underestimation of seizure-associated risks, and may pose problems for valid seizure forecasting. This finding strongly supports the use of objective measures to monitor cyclical distributions of seizures and for studies and treatment decisions based thereon.

Predicting Postpartum Haemorrhage: A Systematic Review of Prognostic Models

(Aust N Z J Obstet Gynaecol. 2022;62:813–825) The impact of postpartum hemorrhage (PPH, loss of ≥500 mL blood within 24 h of birth) on women, families, and communities is significant. PPH has been reported with increasing incidence and severity in well-resourced countries and is a major contributor to global maternal morbidity and mortality. Diagnosis can be challenging due to variability of risk factors and underestimation of blood loss leading to delayed response and treatment. Prognostic models to identify patients at risk and to provide resources and facilitate early intervention are not commonly integrated into clinical practice. Few, if any, are considered validated for use in the general obstetric population. This systematic review provides a comprehensive evaluation of current PPH prognostic models to identify best practices for prediction, describe model characteristics, and compare performance.

Determinants of liquidity risk: Empirical evidence from Indian commercial banks

Liquidity risk is a significant financial threat that must be handled carefully. Underestimation or mismanagement of liquidity risk may lead to severe financial losses or even bank failures. Therefore, timely and adequately estimating liquidity risk and examining factors that affect liquidity risk are essential. On that account, this paper aims to examine the determinants of liquidity risk for Indian commercial banks from 2013 to 2022. For this purpose, the study has employed a panel data regression model with pooled OLS, fixed effect, and random effect methods and has considered bank-specific and macroeconomic variables. The findings show that liquidity risk is affected by both bank-specific variables and macroeconomic variables. Bank-specific variables, such as bank age, have a negative impact on liquidity risk at the 1 percent significance using pooled OLS, FE, and RE models. In contrast, bank size and bank capitalization positively impacted liquidity risk. However, the operational efficiency of banks was found to have no significant impact on liquidity risk using both the liquid asset to total assets ratio and the loan to deposit ratio. In addition, the results show that macroeconomic variables such as GDP and inflation have a positive impact on liquidity risk. The study’s findings are expected to assist various stakeholders in making appropriate policies, decisions and managing their liquidity risk.

Behavioural Biases in Equity, Futures, and Options Trading: Implications for Market Efficiency

This research paper presents a comprehensive analysis of behavioural biases among traders operating in the equity, futures, and options segments of financial markets. The study aims to understand the psychological factors influencing trading decisions, risk perception, and trading strategies. By exploring the impact of behavioural biases on market efficiency and stability, this research contributes to the growing field of behavioural finance. Using a mixed-method approach, the study collected data through surveys, interviews, and behavioural experiments from a diverse group of traders, including retail investors and institutional participants. The analysis focused on identifying common behavioural biases such as overconfidence, loss aversion, herding behaviour, anchoring, and the disposition effect. The findings reveal that behavioural biases are prevalent among traders in all three segments. Overconfidence leads to excessive trading and underestimation of risks, while loss aversion influences risk aversion and reluctance to exit losing positions. Herding behaviour results in the formation of market bubbles and crashes, and anchoring affects traders' reliance on past information to make decisions. The disposition effect influences traders to hold onto losing positions longer than profitable ones. The study also highlights the impact of behavioural biases on trading strategies and risk management practices. Traders tend to exhibit risk-seeking behaviour during periods of market euphoria and become risk-averse during times of uncertainty and volatility. Moreover, behavioural biases can lead to suboptimal portfolio allocation and performance outcomes. The implications of these findings for market efficiency and stability are significant. Behavioural biases can contribute to market inefficiencies, distorted price patterns, and increased market volatility. As a result, market participants and regulators must address these biases to foster more informed decision-making and improve market outcomes. To mitigate the impact of behavioural biases, the study recommends trader education programs focusing on behavioural finance principles and risk management techniques. Additionally, market regulators should implement measures to promote transparency and discourage excessive speculative trading. In conclusion, this research underscores the importance of understanding behavioural biases in equity, futures, and options trading. By shedding light on the psychological drivers of trading behaviour, this study provides valuable insights for traders, financial institutions, and policymakers to enhance market efficiency and stability in the face of behavioural challenges.

Contraceptive use among women seeking an early induced abortion in Sweden.

In spite of societal efforts to strengthen women's sexual and reproductive health in Sweden, many women have unmet contraceptive needs and the abortion rate remains high. The aim of this study was to investigate contraceptive use among abortion-seeking women. Swedish-speaking women seeking an induced abortion up to the end of gestational week 12 at seven hospitals filled out an anonymous paper questionnaire between January and June 2021. Data were analyzed using frequencies and cross-tabulations, and the Chi-square test was used to compare age-groups. Valid percentages are presented. In total, 623 women participated. Median age was 29 years and 13% were born outside the Nordic countries. In the year preceding the abortion, condoms (37%, n = 228) were the most commonly used contraceptive method, followed by short-acting reversible contraception (SARC) (35%, n = 213) and withdrawal (25%, n = 152). Around one in five (n = 113) had not used any method in the year preceding the abortion. Sixteen percent (n = 96) had changed contraceptive method in the last year. At the time around conception, 15% (n = 90) reported use of SARC and 2% (n = 12) of long-acting reversible contraception (LARC). Four out of 10 women (n = 268) reported non-use of contraception at the time around conception, with a higher proportion among adolescents (70%, n = 30, P = 0.001). Among the women who responded to why they had not used any method (n = 387), the main reasons were that they did not believe they could become pregnant at that time (37%, n = 144) or had negative experiences from using contraceptives (32%, n = 123). A majority (88%, n = 527) planned to use contraception after the abortion. Of the women who had decided on method, 55% (n = 271) planned to use LARC, and 38% (n = 188) planned to use SARC. The unmet need for contraception appears to be high among abortion-seeking women in Sweden. Many had discontinued contraception use during the last year, and the main reasons for avoidance were beliefs that one could not become pregnant and negative experiences of contraceptives. The underestimation of pregnancy risk indicates limited fertility awareness, thus our recommendation would be to strengthen the sexual and reproductive knowledge among this group.

EFFECT OF MORTGAGE FINANCING ON FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN KENYA

<p>The financial performance of commercial banks has been unstable as evidenced in Annual Supervision Report of 2011 to 2020, where the Return on Assets (ROA) rose to 6.2% in 2012 from 3% in 2011; and to below 3% in the years 2016 to 2020. Literature reveals that commercial banks’ lending criteria are pro-cyclical, implying a less strict lending criteria during the real estate boom, and very strict during burst; resulting in likely underestimation of the default risk on loans during periods of high demand by the commercial banks. The objective of the study was to establish the effect of mortgage financing on the n financial performance of commercial banks in Kenya for the period 2015 to 2022. Using secondary balanced panel data from 27 mortgage-offering banks in Kenya, with 189 data points and employing moderated multiple regression to achieve the study objectives. The regression analysis revealed that the independent variables explained 86.69% (R<sup>2 </sup>= 0.8669, p-value = 0004) of variance in of financial performance of commercial banks in Kenya, the coefficient of mortgage financing is 0.004434, (p=0.0004); implying that a unit increase in mortgage loan would result to significant increase in the return on assets by 0.004434 units. Therefore, the null hypothesis was rejected. The study concluded that an increase in the amount of mortgage loans offered as well as other activities that augment the total value of mortgage loans extended by the commercial banks leads to a significant improvement in the financial performance of commercial banks in Kenya. The study recommends that commercial banks in Kenya should target to increase the amount of mortgage offered as well as other activities that augment the total value of mortgage loans extended in order to improve their financial performance.</p><p><strong> </strong></p><p><strong>JEL</strong>: D10; D14; G21</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0081/a.php" alt="Hit counter" /></p>

Atlas of probabilistic extreme precipitation based on the early 21st century records in the United States

Study regionThe Conterminous United States Study focusExtreme precipitation property defined by the National Oceanic and Atmospheric Administration’s Precipitation-Frequency Atlas of the United States (NOAA Atlas 14) is losing its representativeness as the standard measure due to the impacts of climate change. This study aims to spatially and quantitatively assess the changes in extreme precipitation properties by comparing the NOAA Atlas 14 with a new atlas of precipitation frequency estimates (PFE) developed in this study from early 21st-century records New hydrological insights for the regionThe NOAA Atlas 14 has quantitatively diverged from the recent 20-year records due to precipitation non-stationarity across the United States. The NOAA Atlas 14 is characterized by more spatially smoothed PFEs compared to the new PFEs, suggesting that employing the NOAA Atlas 14 could lead to an underestimation of physical flood risk at a local scale. As the “new normal” in the 21st century, the majority of the United States faces three times more occurrences of extreme storms corresponding to a 1-in-100-year return period of the 20th century. The recent 20-year extreme events should be treated as the new normal. Until the NOAA Atlas 14 is updated to account for climate-driven non-stationarity and can be shown to resolve local effects better, it is recommended to employ new values between the upper and middle bounds of PFE that capture the non-stationarity of precipitation for the regions where the underestimation of PFEs is highlighted in this study

Consistency between the subjective and objective flood risk and willingness to purchase natural disaster insurance among farmers: Evidence from rural areas in Southwest China

Floods pose significant threats to farmers, and engaging in effective disaster avoidance behaviors can mitigate some of these risks for farmers in flood risk areas (FRA). However, due to farmers' bounded rationality characteristics, they often have a cognitive bias of blindly overestimating or underestimating flood risk, thus failing to perform correct disaster avoidance behaviors. Based on survey data from 180 farmers in FRA in southwest China, this study constructs an assessment method for farmers' subjective and objective flood risk and analyzes the characteristics of farmers' subjective and objective flood risk. A probit regression model explores the correlation between subjective and objective flood risk consistency and farmers' willingness to purchase natural disaster insurance (WPNDI). Results showed that of the 180 farmers surveyed, 63.89% had inconsistent subjective and objective flood risks, mainly reflected in an underestimation of objective flood risk. Farmers' subjective and objective flood risk consistency was found to be positively and significantly correlated with their WPNDI. When other conditions remain unchanged, for every unit increase in the consistency of farmers' subjective and objective flood risk, their WPNDI increased by 0.601 units on average. This paper originally proposed the hypothesis that the consistency of farmers' subjective and objective flood risk is positively and significantly correlated with their WPNDI, and proved the reliability of this hypothesis through research. These findings can provide policy references for disaster risk management and resilient disaster prevention system construction in FRA.

Heart failure risk scores in advanced heart failure patients: insights from the LEVO-D registry.

The prevalence of advanced heart failure (HF) is increasing due to the growing number of patients with HF and their better treatment and survival. There is a scarcity of data on the accuracy of HF web-based risk scores in this selected population. This study aimed to assess mortality prediction performance of the Meta-Analysis Global Group in Chronic HF (MAGGIC-HF) risk score and the model of the Barcelona Bio-HF Risk Calculator (BCN-Bio-HF) containing N terminal pro brain natriuretic peptide in HF patients receiving intermittent inotropic support with levosimendan as destination therapy. Four hundred and three advanced HF patients from 23 tertiary hospitals in Spain receiving intermittent inotropic support with levosimendan as destination therapy were included. Discrimination for all-cause mortality was compared by area under the curve (AUC) and Harrell's C-statistic at 1year. Calibration was assessed by calibration plots comparing observed versus expected events based on estimated risk by each calculator. The included patients were predominantly men, aged 71.5 [interquartile range 64-78] years, with reduced left ventricular ejection fraction (27.5±9.4%); ischaemic heart disease was the most prevalent aetiology (52.5%). Death rate at 1year was 26.8%, while the predicted 1-year mortality by BCN-Bio-HF and MAGGIC-HF was 17.0% and 22.1%, respectively. BCN-Bio-HF AUC was 0.66 (Harrell's C-statistic 0.64), and MAGGIC-HF AUC was 0.62 (Harrell's C-statistic 0.61). The two evaluated risk scores showed suboptimal discrimination and calibration with an underestimation of risk in advanced HF patients receiving levosimendan as destination therapy. There is a need for specific scores for advanced HF.

Validation of the NELA risk prediction model in emergency abdominal surgery.

Risk prediction models are frequently used to identify high-risk patients undergoing emergency laparotomy. The National Emergency Laparotomy Audit (NELA) developed a risk prediction model specifically for emergency laparotomy patients, which was recently updated. In this study, we validated the updated NELA model in an external population. Furthermore, we compared it with three other risk prediction models: the original NELA model, the Portsmouth Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (P-POSSUM) model, and the American Society of Anesthesiologists Physical Status (ASA-PS). We included adult patients undergoing emergency laparotomy at Zealand University Hospital, from March 2017 to January 2019, and Herlev Hospital, from November 2017 to January 2020. Variables included in the risk prediction models were collected retrospectively from the electronic patient records. Discrimination of the risk prediction models was evaluated with area under the curve (AUC) statistics, and calibration was assessed with Cox calibration regression. The primary outcome was 30-day mortality. Out of 1226 included patients, 146 patients (11.9%) died within 30 days. AUC (95% confidence interval) for 30-day mortality was 0.85 (0.82-0.88) for the updated NELA model, 0.84 (0.81-0.87) for the original NELA model, 0.81 (0.77-0.84) for the P-POSSUM model, and 0.76 (0.72-0.79) for the ASA-PS model. Calibration showed underestimation of mortality risk for both the updated NELA, original NELA and P-POSSUM models. The updated NELA risk prediction model performs well in this external validation study and may be used in similar settings. However, the model should only be used to discriminate between low- and high-risk patients, and not for prediction of individual risk due to underestimation of mortality.

Changes in COVID-19 vaccine hesitancy at different times among residents in Guangzhou, China.

Vaccination as a fundamental pillar of promoting public health and interest is critical to limiting the COVID-19 pandemic. However, many citizens are still hesitant about this epidemic prevention measure. This article aimed to understand the COVID-19 vaccination and hesitancy rates among Guangzhou residents at different points in time as well as to explore the relevant factors that cause vaccination hesitancy. We conducted a total of nine cross-sectional surveys by enrolling 12,977 questionnaires among Guangzhou residents through the online survey software called "WenJuanXing" between April 2021 and December 2022, and residents made their choices by judging their willingness to vaccinate. These surveys collected data on the participants' sociodemographic characteristics, vaccination status, vaccine hesitancy, and factors influencing this hesitancy. The Chi-squared test was used for univariate analysis and the multivariate logistic regression model was used to further adjust the influence of the confounding factors to evaluate the main factors affecting the hesitancy of the COVID-19 vaccine at different periods. Over the course of 2021-2022, a total of 12,977 residents in the study area were surveyed. The vaccine hesitancy rates fluctuated over time. From April to June 2021, the vaccine hesitancy rate decreased from 30% to 9.1% and then increased to 13.7% in November. However, from April to December 2022, the hesitancy rate continued to rise from 13.4% to 30.4%. Vaccination rates, the epidemic waves of COVID-19, and changes in policies may all be possible factors that contributed to these fluctuations in vaccine hesitancy rates. We found statistically significant correlations between factors, such as residence, education, and occupation, and vaccine hesitancy at certain points of time. The results of the surveys in April and June 2021 showed that rural residents showed higher vaccine hesitancy rate than urban residents. Their lower education level was associated with higher vaccine hesitancy. Workers and farmers are more likely to have vaccine hesitancy than people with other occupations. The univariate analysis showed that people with underlying medical conditions and lower perceived health status were more likely to experience vaccine hesitation. Logistic regression analysis revealed that the health status of individuals is the most important factor leading to vaccine hesitancy, and residents' underestimation of domestic risks and overconfidence in personal protection measures were also contributing factors. At different stages, vaccine hesitancy among residents was related to vaccine side effects, safety and efficacy, convenience fluctuation, and various factors. In the present study, we found that vaccine hesitancy did not display a consistent downward trend but it fluctuated over time. Higher education, residing in urban areas, lower perceived disease risk, and concerns about the vaccine's safety and side effects were risk factors for vaccine hesitancy. Implementing appropriate interventions and educational programs tailored to address these risk factors may prove to be effective in enhancing public confidence on vaccination.

Production Facilities Safety Assessment According to the Maximum Values of Machines Reliability

Introduction. At the stage of designing technical devices and performing appropriate strength calculations of metal structures, sufficiently large reserves of strength are taken, which, theoretically, exclude any failures of parts. In fact, the machines work with frequent failures. Of interest are undiagnosed failures that lead to a critical decrease in safety, especially at hazardous production facilities. It is assumed that the previously applied approaches of selective determination of the maximum (minimum) reliability value, based on point estimates of the distribution parameters of the two-parameter Weibull law, lead to an overestimation of the calculated indicators of the probability of failure-free operation, i.e. underestimation of risk. Therefore, the work objective is to consider an approach to assessing the risk of operating production facilities in a situation of accidental occurrence of dangerous and undiagnosed failures in systems.Materials and Methods. Methods for technical devices safety assessment based on probability theory were used in the work, and the probability of machine failure was determined based on the well-known method of reliability theory. This method consists in calculating and constructing distribution functions of random variables (load-bearing capacity and loading) that influence the occurrence of failure. The level of increase in the reliability index was determined, leading to frequent unpredictable failures of technical devices (machines) and a decrease in the safety of their operation.Results. The signs of inconsistency of strength calculations based on overestimated safety margins, which in theory exclude failures of parts and machines in general, are identified and substantiated. A new approach to risk assessment of operating production facilities in a situation of accidental occurrence of dangerous and undiagnosed failures by safety systems has been developed and implemented. An algorithm for determining the three parameters of Weibull's law for a population based on sample data has been developed. The resource distribution densities of the boom of the single-bucket excavator EK-14 are constructed. The recommendations are given to increase the probability of failure-free operation to 0.9989.Discussion and Conclusion. The results of the conducted research allow us to substantiate a new approach to risk assessment of operating production facilities in the event of dangerous and undiagnosed failures of basic parts by safety systems, leading to negative consequences.

Co-occurrence of pluvial and fluvial floods exacerbates inundation and economic losses: evidence from a scenario-based analysis in Longyan, China

Hydrodynamics and socio-economic impacts of compound floods as the result of co-occurring pluvial and fluvial floods, have not been well studied, which challenges conventional urban flood risk management that treats different types of floods separately. This study generates a high-resolution land surface digital surface model based on images from unmanned aerial vehicles, and incorporates it into a combined flooding model that integrates the 1D river hydrodynamic model, 1D urban drainage hydrodynamic model and 2D overland flow model, in order to simulate the inundation characteristics of extreme floods when local pluvial floods coincide with upstream fluvial floods, and in a further step to quantify the direct economic losses on urban physical systems. As demonstrated by an empirical study of Longyan, China, the combination of pluvial and fluvial floods leads to substantial exacerbation in inundation extent and depth. Consequently, the joining of pluvial and fluvial floods greatly amplifies economic losses to urban physical systems, indicating that ignoring co-occurrence of different types of floods will potentially lead to underestimation of flood risk and insufficient adaptation. Our study emphasizes the need to account for the co-occurrence of multiple types of floods in flood risk assessment and management, to avoid the shortcomings of fragmented responses.

Invited Perspective: PFAS and the Childhood Obesity Phenotype-Challenges and Opportunities.

Invited Perspective: PFAS and the Childhood Obesity Phenotype-Challenges and Opportunities.

Obesity and breast cancer in women under age 40.

10536 Background: Obesity is a well-established risk factor for postmenopausal hormone-receptor positive breast cancer (BC). The role of obesity in premenopausal BC, especially in women under the age of 40, is not well understood. Multiple studies have shown a decreased rate of premenopausal BC with increasing body mass index (BMI), notably for estrogen receptor positive tumors. Even though there is an inverse association between obesity and premenopausal BC, it has been suggested that obesity has a negative prognostic role in premenopausal BC. This may be explained if premenopausal obese women develop more aggressive tumors. Obesity is associated with triple negative and Luminal B cancers in premenopausal women. We sought to evaluate the relationship between premenopausal breast cancer and obesity in a diverse patient population. Methods: We assembled a 10-year database of women diagnosed with breast cancer before age 40 in a single health system. Data collected included age at diagnosis, self-identified race, breast cancer subtype, BMI, and stage at diagnosis. The control group was all women under the age of 40 presenting to our health system in the same time period. Stratification analysis was performed using Mantel-Haenszel Risk Estimation. Results: The risk ratio between the standardized estimates of BC patients under 40 with BMI ≤ 30 to BMI > 30 is 0.7924, 95% CI (0.683 – 0.920), P = 0.0022. This implies that young BC patients are more likely to be obese than all young women presenting to our health system. There are more obese Black patients compared to White patients. The effect of obesity on BC diagnosis was more pronounced in Black patients, with a risk ratio of 0.59 compared to 0.96. Conclusions: The data presented here challenge previous research which had shown that obesity seemed to be protective against breast cancer in premenopausal women. Here, we see a statistically significant increase in obesity among young women < 40 years presenting with breast cancer. The increased risk of breast cancer diagnosis was particularly pronounced in obese Black women. Previous studies have had a very low proportion of Black women represented, which may cause underestimation of risk in this patient population. These data suggest that obesity is a risk factor for breast cancer in young women, especially in Black patients. [Table: see text]

Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: A multicentre study.

e13542 Background: The study aimed to develop and optimize a radiomics model with features extracted from breast intra- and peri-tumoral region in dynamic contrast-enhanced (DCE) MRI to distinguish benign from malignant breast lesions of Breast Imaging Reporting and Data System (BI-RADS) 4, thereby obviating unnecessary biopsies. Methods: We retrospectively analyzed data of women with BI-RADS 4 or 5 lesions from three hospitals. The regions of interest (ROIs) of intra-tumoral regions were delineated manually on each slice of image. The peri-tumoral regions were then obtained by equidistant 3-dimensinal dilation of the tumor border with 1, 2 and 3mm, respectively. Seven classification models were built with features extracted from the intra- and peri-tumoral regions, including the Intra, Peri1mm, Peri2mm, Peri3mm, Intra+peri1mm, Intra+peri2mm and Intra+peri3mm. Diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC) and compared by DeLong test. Subgroup analysis was performed after stratifying all lesions by enhancement pattern. To assess the rate of avoidable biopsies, three exploratory cutoff values on the ROC curve were examined. Results: The Comb2 model, built with features from peri-tumoral 2mm and intra-tumoral region, demonstrated the best performance with AUCs of 0·901 and 0·863 in the development and external test cohort, respectively. The Comb2 model was robust in both mass and non-mass enhancement lesions. At the three exploratory cutoff values on the ROC curve, the model identified 11·2% (sensitivity of C1≥90%), 49·5% (sensitivity of C2≥95%) and 63·3% (sensitivity of C3≥98%) of the benign lesions in the development cohort. Applying the identified cutoff values in the external test cohort showed the potential to lower the number of unnecessary biopsies of benign lesions. Conclusions: An MRI-based radiomics model built with features extracted from the intra-tumoral region combined with the peri-tumoral 2mm showed the best potential to reduce false-positive diagnoses and may avoid unnecessary biopsies with a low underestimate risk.