Overestimation Of Risk Research Articles

Improvement of a clinical colorectal cancer risk prediction model integrating polygenic risk.

81 Background: Improving colorectal cancer risk prediction and stratification is pivotal for implementing better screening and prevention programs in public health and for enabling a personalised approach for assessing patients’ colorectal cancer risk. Methods: In this study, we used the UK Biobank to compare the performance of a risk prediction model incorporating two different polygenic risk scores – one comprising 45 SNPs and the other comprising 140 SNPs. The clinical component of the risk prediction model included a simple measure of first-degree family history. We used age- and sex-specific population incidence rates to calculate full-lifetime risks. Results: The model using the 140-SNP PRS showed an improvement in discrimination, calibration and risk stratification over the model using the 45-SNP PRS for full-lifetime risk: discrimination was 0.706 (95% CI 0.697–0.715) and 0.674 (95% CI 0.664–0.683), respectively, and the P for difference was < 0.001. The 140-SNP model was well calibrated and showed a small overestimation of risk 0.951 (95% CI 0.918–0.986). Standard incidence ratios compared to population incidence rates showed that, for the 140-SNP model, the top quintile of risk shows a 27% improvement compared to the 45-SNP model. Furthermore, there was a 3-fold difference in colorectal cancer incidence between adults identified in the top quintile compared to the bottom quintile of risk using the 140-SNP model versus the 45-SNP model. Conclusions: This updated risk prediction score with a 140–SNP PRS and a simple measure of family history, improves risk prediction and risk stratification in the general population compared with a similar model with a 45-SNP PRS, and will ultimately assist in colorectal cancer disease prevention in the clinic.

Exposure Factors vs. Bioaccessibility in the Soil-and-Dust Ingestion Pathway: A Comparative Assessment of Uncertainties Using MC2D Simulations in an Arsenic Exposure Scenario

Human Health Risk Assessment (HHRA) is a widely applied method to make decisions about the environmental status of sites affected by toxic substances. Its conclusions are affected by the variability and uncertainty of the input variables in the HHRA model. The aim of this work is to apply an algorithm based on 2D Monte Carlo simulations to integrate the variability and uncertainty of exposure factors, concentration, and bioaccessibility, reported by various information sources, to assess and compare their influence on the risk outcome. The method is applied to a specific case study of exposure of children to arsenic from accidental soil ingestion in a residential setting in the city of Madrid (Spain) by combining information from 12 studies. The consideration of the variability and uncertainty of the exposure parameters in the Baseline Risk Assessment (BRA, deterministic) resulted in a greater reduction in the numerical value of risk estimations than that produced by considering only the bioaccessibility factor. The results of the Probabilistic Risk Assessment (PRA) showed that the risk distribution was more sensitive to the variabilities of the accidental soil intake rate and the total arsenic concentration than to other variables such as bioaccessibility. In this case study, the uncertainty introduced by using the "default" reasonable maximum exposure factors in the HHRA model and the variability of the concentration term produce overestimates of risk that are at least in the range of those produced by omitting the bioaccessibility term. Thus, the inclusion of bioaccessibility is, alone, insufficient to improve the HHRA since the selection of the exposure factors can significantly affect the estimates of risk for the soil ingestion pathway. In other sites or for other contaminants, however, the role of the uncertainties associated with the bioaccesible fraction could be more pronounced. The method applied in this work may be useful in updating exposure factors to reduce uncertainties in HHRAs.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups

Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

Predicting 10-Year Risk of Pancreatic Cancer Using a Combined Genetic and Clinical Model.

Pancreatic cancer has the poorest 5-year survival rate of any major solid tumor, but when diagnosed at an early stage, survival rates improve. Population screening is impractical because pancreatic cancer is rare with a lifetime risk of 1.7%, but accurate risk stratification in the general population could enable health care providers to focus early detection strategies to at-risk individuals. Here, we validate a combined risk prediction model that integrates a polygenic risk score and a clinical risk model. Using the UK Biobank, we conducted a prospective cohort study assessing 10-year pancreatic cancer risks based on a polygenic risk score, a clinical risk score, and a combined risk score. We assessed the association, discrimination, calibration, cumulative hazards, and standardized incidence ratios compared to population incidence rates for the risk scores. We also conducted net reclassification analyses. While all of the risk scores discriminated well between affected and unaffected participants, the combined risk score - with a Harrell's C-index of 0.714 (95% confidence interval [CI]= 0.698, 0.730) - discriminated better than both the polygenic risk score (P= .001) and the clinical risk score (P= .02). In terms of calibration, there was no problem with dispersion for the combined risk score (β= 0.952, 95% CI= 0.865-1.039, P= .3) and overall there was a small overestimation of risk (α=-0.089, 95% CI=-0.156 to-0.021, P= .009). Participants in the top decile of 10-year risk were at 1.413 (95% CI= 1.242-1.607) times population risk. The combined risk score was able to identify individuals at substantially increased risk of pancreatic cancer and to whom targeted screening could be useful.

A survey of the psychosocial impact of the COVID-19 pandemic on pregnant women attending a tertiary care centre of Eastern India - a cross-sectional study.

The COVID-19 pandemic presented an unprecedented challenge, and, as during the SARS outbreak in 2002, there was scope of overestimation of risk by pregnant women, leading to increased fear and anxiety. The aim of this study was to assess the psychosocial impact of the pandemic on pregnant women so better-informed decisions can be taken to spread awareness and alleviate their anxieties. This was a cross-sectional study in a single tertiary care centre of Eastern India including 292 participants. The inclusion criteria were all pregnant women of any gestational age attending the All India Institute of Medical Sciences, Bhubaneswar for antenatal check-up or delivery. The exclusion criteria were patients with severe morbidities and patients not willing to participate. The primary outcome was to determine the psychosocial impact of the COVID-19 pandemic on pregnant women and to measure the anxiety level of pregnant women during the COVID-19 crisis. The secondary outcome was to determine the correlation between demographic aspects and psychosocial impact of COVID-19 pandemic on pregnant women. A self-designed questionnaire was used. Anxiety was scored using the generalised anxiety disorder scale. More than half the participants (57.9%) were not at all worried about acquiring the infection. Of all, 99.3% were scored to have no anxiety and 0.7% had moderate anxiety. Significant association of the level of anxiety was found with living with people at high risk of contracting the disease (p = 0.002). The widespread awareness activities were effective and successful, as the pregnant women at the grassroots level faced minimal anxiety and were aware and assured. Studies like this help to provide feedback and formulate educational activities in future pandemics. The change in the effect on people, from panic in the previous pandemics to assurance in the current one, as found in our study, indicates the commendable work done to spread well founded information far and wide by the government, health care institutions, and workers.

The effect of the number of subintervals upon the quantification of the seismic probabilistic safety assessment of a nuclear power plant

Seismic risk has received increased attention since the 2011 Fukushima accident in Japan. The seismic risk of a nuclear power plant is evaluated via seismic probabilistic safety assessment (PSA), for which several methods are available. Recently, the discrete approach has become widely used. This approximates the seismic risk by discretizing the ground motion level interval into a small number of subintervals with the expectation of providing a conservative result. The present study examines the effect of the number of subintervals upon the results of seismic risk quantification. It is demonstrated that a small number of subintervals may lead to either an underestimation or overestimation of the seismic risk depending on the ground motion level. The present paper also provides a method for finding the boundaries between overestimation and underestimation regions, and illustrates the effect of the number of subintervals upon the seismic risk evaluation with an example. By providing a method for determining the effect of a small number of subintervals upon the results of seismic risk quantification, the present study will assist seismic PSA analysts to determine the appropriate number of subintervals and to better understand seismic risk quantification.

Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling.

The extensive use of logistic regression models in analytical epidemiology as well as in randomized clinical trials, often creates inflated estimates of the relative risk (RR). Particularly, in cases where a binary outcome has a high or moderate incidence in the studied population (&gt;10%), the bias in assessing the relative risk may be very high. Meta-analysis studies have estimated that about 40% of the relative risk estimates in prospective investigations, through binary logistic models, lead to extensive bias of the population parameters. The problem of risk inflation also appears in cross-sectional studies with binary outcomes, where the parameter of interest is the prevalence ratio. As an alternative to the use of logistic regression models in both longitudinal and cross-sectional studies, the modified Poisson regression model is proposed.

Comparison of Value at Risk (VaR) Multivariate Forecast Models.

We investigate the performance of VaR (Value at Risk) forecasts, considering different multivariate models: HS (Historical Simulation), DCC-GARCH (Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity) with normal and Student's t distribution, GO-GARCH (Generalized Orthogonal-Generalized Autoregressive Conditional Heteroskedasticity), and copulas Vine (C-Vine, D-Vine, and R-Vine). For copula models, we consider that marginal distribution follow normal, Student's t and skewed Student's t distribution. We assessed the performance of the models using stocks belonging to the Ibovespa index during the period from January 2012 to April 2022. We build portfolios with 6 and 12 stocks considering two strategies to form the portfolio weights. We use a rolling estimation window of 500 and 1000 observations and 1%, 2.5%, and 5% as significance levels for the risk estimation. To evaluate the quality of the risk forecasts, we compute the realized loss and cost. Our results show that the performance of the models is sensitive to the use of different significance levels, rolling windows, and strategies to determine portfolio weights. Furthermore, we find that the model that presents the best trade-off between the costs from risk overestimation and underestimation does not coincide with the model suggested by the realized loss.

Performance of the ABC-bleeding risk score for assessing major bleeding risk in Chinese patients with atrial fibrillation on oral anticoagulation therapy: A real-world study.

To evaluate performance of the ABC (Age, Biomarkers, Clinical history)-bleeding risk score in estimating major bleeding risk in Chinese patients with atrial fibrillation (AF) on oral anticoagulation (OAC) therapy in real-world practice. Data were collected from the Chinese Atrial Fibrillation Registry study (CAFR). Patients were stratified into low-, medium-, and high-risk groups based on ABC-bleeding risk score with 1-year major bleeding risk (<1%, 1-2%, and > 2%) and modified HAS-BLED score (≤1, 2, and > 2 points). Cox proportional-hazards (Cox-PH) models were used to determine the association of major bleeding incidence with bleeding scores. Harrell's C-index of the two scores were compared. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) at 1 year were employed to evaluate the reclassification capacity. The calibration curve was plotted to compare the predicted major bleeding risk using ABC-bleeding risk score with the observed annualized event rate. The decision analysis curves (DCA) were performed to show the clinical utilization of two scores in identifying major bleeding events. The study included 2,892 AF patients on OAC therapy. After the follow-up of 3.0 years, 48 patients had major bleeding events; the incidence of a bleeding event in the low-, medium-, and high-risk groups according to ABC-bleeding risk score was 0.31% (reference group, HR = 1.00),0.51% (HR = 1.83, 95%CI: 0.91-3.69, P = 0.09), and 1.49% (HR = 4.92, 95%CI: 2.34-10.30, P < 0.001), respectively. Major bleeding incidence had an independent association with growth differentiation factor 15 (GDF-15) level (HR = 2.16, 95%CI: 1.27-3.68, P = 0.005) after adjusting components of the HAS-BLED score and cTnT-hs level. The ABC-bleeding score showed a Harrell's C-index of 0.67 (95%CI: 0.60-0.75) in estimating major bleeding risk, which was non-significant compared to the modified HAS-BLED score (0.67 vs. 0.63; P = 0.38). NRI and IDI also revealed comparable reclassification capacity of ABC-bleeding risk score compared with HAS-BLED score (14.6%, 95%CI: -10.2%, 39.4%, P = 0.25; 0.2%, 95%CI -0.1 to 0.9%, P = 0.64). Cross-tabulation of the two scores showed that the ABC-bleeding score outperformed the HAS-BLED score in identifying patients with a high risk of major bleeding. The calibration curve showed that the ABC-bleeding risk score overestimated the observed major bleeding risk. DCA did not show any difference in net benefit when using either of the scores. This study verified the value of the ABC-bleeding risk score in assessing major bleeding risk in Chinese patients with AF on OAC therapy in real-world practice. Despite the overestimation of major bleeding risk, ABC-bleeding score performed better in stratifying patients with a high risk than the modified HAS-BLED score. Combining the two scores could be a clinically practical strategy for precisely stratifying AF patients, especially those at a high risk of major bleeding, and further supporting the optimization of OAC treatment.

Evaluating and Optimizing Prognostic Modeling for Patients Undergoing Stereotactic Body Radiotherapy for Bone Metastases

<h3>Purpose/Objective(s)</h3> Stereotactic body radiotherapy (SBRT) holds an expanding role in the treatment of bone metastases. As compared to conventional external beam radiation (cEBRT), SBRT may be best applied to specific subsets of patients with more favorable prognosis and may prolong survival time. This calls into question the generalizability of survival estimates from prognostic models built from more general patient populations treated with cEBRT. We investigate the external validity of three such models for patients treated with bone-directed SBRT and identify approaches to optimize prognostication for this population. <h3>Materials/Methods</h3> Patients treated with SBRT for bone metastases from 1/2017-12/2019 at 3 radiation oncology facilities were identified through retrospective electronic medical record review. Demographic, disease, and treatment data were collected, including the covariates of 3 previously-validated models built in populations that excluded SBRT: the 27-item Bone Metastases Ensemble Trees for Survival (BMETS) machine-learning model and 2 Cox regression models—Chow's 3-item (C-3) and Westhoff's 2-item (W-2) tools. Individualized estimates for risk of death at various time points after consultation were obtained using each model. Time-dependent area under the curve (tAUC), calibration plots, and Brier scores evaluated model discrimination, calibration, and overall performance when each model was applied to the SBRT validation set. <h3>Results</h3> A total of 192 sites in 168 patients were included. Patients were 65.5% male; 74% white, 17% black and 8% other; with median age of 63 years (range: 18-92), and median Karnofsky Performance Status of 90 (range: 50-100). Histologies were prostate (31%), lung (15%), breast (10%), and other (44%). Estimated median overall survival was 3.3 years. BMETS tAUC was 0.73, 0.81, and 0.81 at 6, 9, and 12 months, with significant outperformance (p<0.05) of C-3 and W-2 models by the 12-month time point (tAUC 0.71 and 0.75, respectively). Calibration plots showed overestimation of risk of death in all 3 models. Brier scores for BMETS, C-3 and W-2 were respectively 0.24, 0.28 and 0.25 at 12 months; the simple baseline model from the Kaplan-Meier curve estimate (Brier score 0.19) outperformed all 3 models. Subsequent analyses identified variables absent from the 3 models strongly predictive of survival in the SBRT cohort, including number of metastatic lesions. <h3>Conclusion</h3> When applied to an SBRT cohort, BMETS maintained excellent discrimination at time points >6 months, outperforming C-2 and W-3 models. Yet, each model underestimated specific survival probabilities, suggesting that tools built from non-SBRT populations may be poorly calibrated for SBRT and supporting use of SBRT-specific prognostic tools. To address this, we have modified the BMETS web platform to permit for users to select for SBRT and non-SBRT specific survival predictions.

Hammering sound of concrete with defects and spalling risk

Inspection is important for preventing concrete spalling and maintaining the soundness of tunnels. Human inspection combining visual inspection and the hammering tests has a proven track record and is considered reliable. However, human inspection is time-consuming and the results vary depending on the inspector. Vibration measurement results obtained for areas with defects in unreinforced concrete sections of railway tunnels reveal that there are many defects that are overestimated the spalling risk. The objective of this study was to elucidate the causes of this overestimation. A concrete specimen with an inclined detachment was prepared, and the change in the hammering sound with the extension of the detachment was investigated. Numerical analyses were conducted to supplement the experimental results. The results reveal that the low-frequency vibrations of the defects are less likely to be transmitted by air as sound pressure. Moreover, low-frequency sounds are relatively hard to hear considering human auditory characteristics. Hence, low-frequency vibration may not affect the hammering sound. Although the presence or absence of defects can be distinguished by the hammering sound, the spalling risk cannot be accurately evaluated from the sound alone, which is one of the main reasons for spalling risk overestimation by human inspectors.

Fracture risk in Asian-Canadian women is significantly over-estimated by the Canadian Association of Radiologists-Osteoporosis Canada risk prediction tool: retrospective cohort study.

Health risk prediction tools based on largely White populations may lead to treatment inequity when applied to non-White populations where outcome rates differ. We examined the calibration of the Canadian Association of Radiologists-Osteoporosis Canada (CAROC) fracture risk prediction tool in self-identified Asian-Canadian women. Retrospective cohort study of women over age 50 using the Manitoba BMD Registry. At first BMD, the intake questionnaire collected self-identification of ethnicity and fracture risk factors. 10-year fracture risk was estimated using CAROC and categorized into low, medium, or high fracture risk. Linked administrative databases identified incident osteoporotic fractures. Observed fracture rates were compared between White and Asian-Canadians and compared to the original CAROC risk stratification. There were 63,632 and 1703 women who self-identified as White-Canadian or Asian-Canadian, respectively, covering approximately 600,000 patient-years follow-up. There were 6588 incident fractures; a similar percentage of patients were assigned to each risk stratum at baseline by CAROC. A progressive rise in 10-year observed fracture rates occurred for each CAROC stratum in the White-Canadian population but much lower fracture rates than predicted in Asian-Canadian patients (p &lt; 0.001). Fracture incidence rate ratios were 1.9-2.6 foldhigher in White- vs Asian-Canadian patients for all strata (p &lt; 0.001). In the CAROC moderate and high-risk categories, observed fracture rates in Asian-Canadian patients were typically lower than predicted, indicating poor model calibration. In Asian-Canadian women, observed osteoporosis fracture rates are lower than predicted when using the CAROC tool. Over-estimation of fracture risk may influence shared decision-making discussions.

Distribution of flame retardants among indoor dust, airborne particles and vapour phase from Beijing: spatial–temporal variation and human exposure characteristics

The occurrence and distribution of 10 brominated flame retardants (BFRs) and 10 organophosphate flame retardants (OPFRs) were investigated in indoor dust, total suspended particles (TSP), and vapour phase from offices (n = 10), homes (n = 9), and day-care centres (n = 10) in Beijing, China. Three types of samples were collected biweekly from one office and one home over a year to examine temporal trends. BFRs in dust significantly correlated with those in TSP, while OPFRs significantly correlated among all three matrices. In addition, BFRs in dust (ng/g) and TSP (pg/m3) exhibited similar temporal trends with higher levels in the cold season, whereas OPFRs in TSP and vapour phase (pg/m3) showed similar temporal trends with higher levels in the warm season. The geometric mean concentrations of BFRs and OPFRs in the three matrices from the above mentioned three types of indoor microenvironments were used for exposure and health risk estimation, and ∑7OPFRs showed much higher hazard index (HI) values than ∑10BFRs for all subpopulations, and inhalation of OPFRs was a major risk source. With the volatility of flame retardants (FRs) decreasing, the contribution of dust ingestion and dermal absorption showed an increasing trend, and the contribution of inhalation exhibited a gradual decreasing trend, which implied the dominant exposure pathway to FRs is strongly related to the vapour pressure (25 °C, Pa) of these substances. Using a single type of microenvironment or the collection of samples at a single point in time can lead to overestimation or underestimation of overall exposure and risk for people to some extent. The correlations of FRs in dust, TSP, and vapour phase from indoor microenvironments, as well as their temporal trends were first reported in this study, which will provide a basis for more accurate FR exposure assessments in the future.

Risk stratification in Arrhythmogenic Right Ventricular Cardiomyopathy: the impact of genotype on the 2019 ARVC risk calculator

Abstract Introduction Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) is associated with sudden cardiac death (SCD). The 2019 ARVC risk model has been proposed as a method to quantify arrhythmic risk, but the impact of genotype its performance has not been addressed. Purpose To study arrhythmic outcomes in patients with ARVC and the performance of the 2019 ARVC risk model in predefined genetic subgroups. Methods This is an international, retrospective observational cohort study on consecutively evaluated patients with ARVC recruited from 17 centres in 7 countries. Inclusion criteria were: (i) a definite diagnosis of ARVC according to the 2010 Task Force Criteria; (ii) no history of sustained ventricular arrhythmia (VA) prior to first assessment at the participating centre; (iii) a follow up period of ≥1 month; (iv) age of diagnosis ≥14 years. Sustained ventricular arrhythmia (sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator intervention, aborted SCD, or SCD) comprised the primary outcome (VA). Discriminative ability was assessed by Uno's concordance index (c-statistic) and calibration with the calibration plot slope. Fine-Gray regression was used to model the impact of clinical predictors on the arrhythmic outcome, in the context of competing risks (heart transplantation and non-arrhythmic death). The cumulative probability and 95% confidence intervals (95% CI) for the occurrence of an outcome were estimated using the Aalen-Johansen estimate in order to take into account competing risks. Results The study cohort comprised 554 ARVC patients. During a median follow-up of 6.0 [3.1,12.5] years, 100 patients (18%) experienced VA (Figure). Risk estimates for VA using the 2019 ARVC risk model showed good discriminative ability (c-statistic 0.75 (95% CI 0.70–0.81)) but with overestimation of risk (slope 0.46 (95% CI 0.33–0.63)). The ARVC risk model was compared in 4 gene groups: PKP2 (n=118, 21%); DSP (n=79, 14%); other desmosomal (n=59, 11%); and gene elusive (n=160, 29%). Discrimination and calibration were highest for PKP2 [c-statistic 0.83 (95% CI 0.75–0.91); calibration slope 0.67 (95% CI 0.40–1.04)] and lowest for the gene elusive group [c-statistic 0.65 (95% CI 0.57–0.74); calibration slope 0.26 (95% CI 0.06–0.49)]. Univariable analyses revealed variable performance of individual clinical risk markers in the different gene groups (see heatmap of hazard ratios and statistical significance in Figure). For example, RV dimensions and systolic function are significant risk markers in PKP2 but not in DSP patients and the opposite is true of LV systolic function (Figure). Conclusion The 2019 ARVC risk model performs reasonably well in gene positive ARVC, (particularly for PKP2) but is more limited in gene elusive patients. Genotype specific risk factors should be considered in ARVC patients. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): British Heart Foundation

How growers make decisions impacts plant disease control.

While the spread of plant disease depends strongly on biological factors driving transmission, it also has a human dimension. Disease control depends on decisions made by individual growers, who are in turn influenced by a broad range of factors. Despite this, human behaviour has rarely been included in plant epidemic models. Considering Cassava Brown Streak Disease, we model how the perceived increase in profit due to disease management influences participation in clean seed systems (CSS). Our models are rooted in game theory, with growers making strategic decisions based on the expected profitability of different control strategies. We find that both the information used by growers to assess profitability and the perception of economic and epidemiological parameters influence long-term participation in the CSS. Over-estimation of infection risk leads to lower participation in the CSS, as growers perceive that paying for the CSS will be futile. Additionally, even though good disease management can be achieved through the implementation of CSS, and a scenario where all controllers use the CSS is achievable when growers base their decision on the average of their entire strategy, CBSD is rarely eliminated from the system. These results are robust to stochastic and spatial effects. Our work highlights the importance of including human behaviour in plant disease models, but also the significance of how that behaviour is included.

Concentrations, generation and risk characterization of phthalimide in tea-derived from folpet or not?

The fungicide folpet is rapidly degraded into phthalimide (PI) during both thermal processing and analytical procedures in sample preparation; thus, its residue definition has been modified into the sum of itself and PI. Tea is one of the world's most popular nonalcoholic beverages, where folpet is not listed as an applicable pesticide. To demonstrate how serious false-positives and overestimation in dietary risk are caused by the application of a new residue definition, the residue pattern of PI in made tea and processed tea leaves, along with its transfer rate during tea brewing and corresponding dietary risk, were investigated in the present study. The results revealed that PI residue in tea ranged from <10 μg/kg to 180 μg/kg with a median value of 10 μg/kg, 7.3 % of which was over the maximum residue limit established by EU (100 μg/kg, expressed as folpet). The PI residue in green tea was obviously higher than that in black, dark and oolong tea. Simulated heating experiments revealed that PI can arise from improper heating of folpet-free fresh tea leaves, and thus green tea bears a higher risk for its manufacturing employing a comparatively higher temperature. The transfer rate of PI during tea brewing was 104 ± 14 %. Nevertheless, the risk of PI through drinking tea was negligible to humans depending on the risk quotient (RQ) value, which was significantly lower than 1.

Degradation and horizontal gene transfer analysis of plasmid-encoded antibiotic resistance genes during UV254, hydroxyl radical and sulphate radical treatments

The environment has been implicated with antibiotic resistance (AR). Effective degradation and deactivation of antibiotic resistance genes (ARGs) in wastewater treatment can serve as barriers to AR dissemination. This study investigated the degradation kinetics of intracellular (i-) and extracellular (e-) plasmid-encoded tetA, ampR and sul1 ARGs using UV254, hydroxyl radical (HO.) and sulphate radical (SO4.-) UV-based advanced oxidation processes (UV254/H2O2 and UV254/S2O82- respectively). The degradation of tetA, ampR and sul1 was quantified using quantitative polymerase chain reaction (qPCR). The damages to each ARG were observed using two qPCR amplicons ranging between 162 and 1054 bp. Culture-based horizontal gene transformation experiments were used to estimate the deactivation kinetics of pCR™2.1-TOPO AR plasmid. ARG degradation and deactivation kinetics were then compared to understand the roles of each treatment strategy in AR mitigation. Results indicate that extracellular ARGs degradation kinetics of the treatments followed an order UV254/S2O82- > UV254/H2O2 > UV254. The base pair specific kinetic constants with respect to HO. and SO4.- at pH 7 were between 1.86 × 109-1.65 × 1011 M−1s−1 and 2.87 × 109-5.84 × 1011 M−1s−1 respectively. e-ARGs degradation was at least 2-fold higher than i-ARGs degradation for all treatments. UV254/S2O82- was most effective for ARG degradation under acidic pH (5–6) while UV254/H2O2 was most effective between pH 7 and 8. Higher degradation rates were recorded for AT-rich ampR and longer qPCR amplicons. Deactivation rates by UV254/H2O2 and UV254/S2O82- were 2.6-times higher than that of UV254. Generally, deactivation kinetics were ∼ 8–13 times faster than degradation kinetics observed for short ampR amplicon. These findings show an overestimation of the potential risks of ARG presence using short qPCR target amplicons and the impact of nucleotide composition on ARG damage.

Should We Rely on AI to Help Avoid Bias in Patient Selection for Major Surgery?

Many regard iatrogenic injuries as consequences of diagnosis or intervention actions. But inaction-not offering indicated major surgery-can also result in iatrogenic injury. This article explores some surgeons' overestimations of operative risk based on patients' race and socioeconomic status as unduly influential in their decisions about whether to perform major cancer or cardiac surgery on some patients with appropriate clinical indications. This article also considers artificial intelligence and machine learning-based clinical decision support systems that might offer more accurate, individualized risk assessment that could make patient selection processes more equitable, thereby mitigating racial and ethnic inequity in cancer and cardiac disease.

Relations between recurrence risk perceptions and fear of cancer recurrence in breast cancer survivors

PurposeThis study aimed to: (1) determine the accuracy of Dutch breast cancer survivors’ estimations of the locoregional recurrence risk (LRR); (2) examine which variables influence (the accuracy of) risk estimations, and risk appraisals; and (3) investigate the influence of the objective LRR risk (estimated using the INFLUENCE-nomogram), risk estimations and risk appraisals on fear of cancer recurrence (FCR). Findings of this study will inform clinicians on risk communication and can improve communication about FCR.MethodsIn a cross-sectional survey among 258 breast cancer survivors, women’s recurrence risk estimations (in odds) and risk appraisals (in high/low), FCR, demographics and illness perceptions, about one year after surgery were measured and compared to the objective risk for LRRs estimated using the INFLUENCE-nomogram.ResultsHalf of the women (54%) accurately estimated their LRR risk, 34% underestimated and 13% overestimated their risk. Risk estimations and risk appraisals were only moderately positively correlated (r = 0.58). Higher risk appraisals were associated with radiotherapy (r = 0.18) and having weaker cure beliefs (r = − 0.19). Younger age was associated with overestimation of risk (r = − 0.23). Recurrence risk estimations and risk appraisals were associated with more FCR (r = 0.29, r = 0.39). In regression, only risk appraisal contributed significantly to FCR.ConclusionAlthough women were fairly accurate in recurrence risk estimations, it remains difficult to predict over- or underestimation. Recurrence risk estimations and risk appraisal are two different concepts which are both associated with FCR and should therefore be addressed in patient-provider communication.

Using the Revised Cardiac Risk Index to Predict Major Postoperative Events for People With Kidney Failure: An External Validation and Update

BackgroundPeople with kidney failure have high risk of postoperative morbidity and mortality. Although the revised cardiac risk index (RCRI) is used to estimate the risk of major postoperative events, it has not been validated in this population. We aimed to externally validate the RCRI and determine whether updating the model improved predictions for people with kidney failure. MethodsWe derived a retrospective, population-based cohort of adults with kidney failure (maintenance dialysis or sustained estimated glomerular filtration rate < 15 mL/min per 1.73 m2) who had surgery in Alberta, Canada between 2005 and 2019. We categorized participants based on RCRI variables and assigned risk estimates of death or major cardiac events, and then estimated predictive performance. We re-estimated the coefficients for each RCRI variable and internally validated the updated model. Net benefit was estimated with decision curve analysis. ResultsAfter 38,541 surgeries, 1204 events (3.1%) occurred. The estimated C-statistic for the original RCRI was 0.64 (95% confidence interval: 0.62, 0.65). Examination of calibration revealed significant risk overestimation. In the re-estimated RCRI model, discrimination was marginally different (C-statistic 0.67 [95% confidence interval: 0.66, 0.69]), though calibration was improved. No net benefit was observed when the data were examined with decision curve analysis, whereas the original RCRI was associated with harm. ConclusionsThe RCRI performed poorly in a Canadian kidney failure cohort and significantly overestimated risk, suggesting that RCRI use in similar kidney failure populations should be limited. A re-estimated kidney failure-specific RCRI may be promising but needs external validation. Novel perioperative models for this population are urgently needed.