Risk Estimates Research Articles

Radiation Dose-Volume-Response Relationships for Adverse Events in Childhood Cancer Survivors: Introduction to the Scientific Issues in PENTEC

At its very core, radiation oncology involves a trade-off between the benefits and risks of exposing tumors and normal tissue to relatively high doses of ionizing radiation. This trade-off is particularly critical in childhood cancer survivors (CCS), in whom both benefits and risks can be hugely consequential due to the long life expectancy if the primary cancer is controlled. Estimating the normal tissue-related risks of a specific radiation therapy plan in an individual patient relies on predictive mathematical modeling of empirical data on adverse events. The Pediatric Normal-Tissue Effects in the Clinic (PENTEC) collaborative network was formed to summarize and, when possible, to synthesize dose-volume-response relationships for a range of adverse events incident in CCS based on the literature. Normal-tissue clinical radiation biology in children is particularly challenging for many reasons: (1) Childhood malignancies are relatively uncommon-constituting approximately 1% of new incident cancers in the United States-and biologically heterogeneous, leading to many small series in the literature and large variability within and between series. This creates challenges in synthesizing data across series. (2) CCS are at an elevated risk for a range of adverse health events that are not specific to radiation therapy. Thus, excess relative or absolute risk compared with a reference population becomes the appropriate metric. (3) Various study designs and quantities to express risk are found in the literature, and these are summarized. (4) Adverse effects in CCS often occur 30, 50, or more years after therapy. This limits the information content of series with even very extended follow-up, and lifetime risk estimates are typically extrapolations that become dependent on the mathematical model used. (5) The long latent period means that retrospective dosimetry is required, as individual computed tomography-based radiation therapy plans gradually became available after 1980. (6) Many individual patient-level factors affect outcomes, including age at exposure, attained age, lifestyle exposures, health behaviors, other treatment modalities, dose, fractionation, and dose distribution. (7) Prospective databases with individual patient-level data and radiation dosimetry are being built and will facilitate advances in dose-volume-response modeling. We discuss these challenges and attempts to overcome them in the setting of PENTEC.

First forensic quantification, source-identification and health risk estimation of volatile organic carbons in the anthropogenically impacted Omi-Asoro Stream in Ilesa, Nigeria

First forensic quantification, source-identification and health risk estimation of volatile organic carbons in the anthropogenically impacted Omi-Asoro Stream in Ilesa, Nigeria

Serial neutrophils count as a stratifying tool for early follicular tonsilitis remission

Background/Aim: This study examines the correlation between sequential serum neutrophil levels and the overall improvement status or reduction rate of intravenous antibiotic administration. This study seeks to identify a cost-effective and easily accessible biomarker that can accurately predict the need for hospitalisation in patients with acute tonsillitis who are receiving intravenous broad-spectrum antibiotics. The goal is to determine the patients' suitability for early discontinuation of intravenous antibiotics and discharge. Methods: This study was conducted at the King Hussein Medical Centre in Jordan, focusing on patients with severe acute tonsillitis who did not respond to oral antibiotics. Data was collected from the electronic medical record system (Hakeem) from January 2018 to May 2021. The study classified serial serum neutrophil measurements during the first week of hospital admission into four categories. The levels were compared between the non-penicillin Cohort (Group I) and penicillin Cohort (Group II) using an Independent-T Test. The outcomes of interest included a drop in neutrophil counts exceeding 50%, a decrease in the use of intravenous antibiotics in favor of oral antibiotics, and early eligibility for discharge. The study also examined serial neutrophil counts over the tested days and compared to the outcomes of positive and negative states using receiver operating curve analyses. The optimal cutoff operating point for each ROC curve was determined by selecting the highest Youden's index of each prognosticator day level. The analysis of all results was conducted using SPSS version 20 with a significance level set at p-value <0.05. Results: This study analyzed 1638 adult and elderly patients admitted to KHMC, RMS, Amman, Jordan between January 2018 and May 2021. The average age was 51.88±16.22 years, with patients dependent on intravenous penicillin being slightly older. The distribution of males and females was significant, with males making up 2.55 times the number of females. The odds ratio for positive outcomes was 4.12, with a high rate of poor outcomes in 1318 out of all eligible patients. A total of 994 (60.7%) medical patients were treated with intravenous administration, while 644 (39.3%) surgical patients were scheduled for a tonsillectomy procedure. The risk estimate for patients admitted from medical wards compared to surgical wards was an odds ratio of 0.562. The study also provided sensitivity analysis for the four tested serial neutrophil prognosticators at different day levels. Conclusion: The study supports the use of Somatic Nucleated Cell (SNC) as a reliable measure for evaluating EFR in cases of catarrhal acute tonsillitis. The maximum SNC count in Group one was 11,269 cells per cubic millimeter. Acute streptococcal infection often exhibits an ESR higher than 40, but this may be concealed by other complications. The SNC test has advantages like simplicity, quickness, and cost-effectiveness, but lacks specificity. Monitoring SNC and EFR fluctuations can initiate treatment cessation, preventing relapse and severe complications during recovery.

Specification curve analysis to identify heterogeneity in risk factors for dementia: findings from the UK Biobank

BackgroundIn 2020, the Lancet Commission identified 12 risk factors as priorities for prevention of dementia, and other studies identified APOE e4/e4 genotype and family history of Alzheimer’s disease strongly associated with dementia outcomes; however, it is unclear how robust these relationships are across dementia subtypes and analytic scenarios. Specification curve analysis (SCA) is a new tool to probe how plausible analytical scenarios influence outcomes.MethodsWe evaluated the heterogeneity of odds ratios for 12 risk factors reported from the Lancet 2020 report and two additional strong associated non-modifiable factors (APOE e4/e4 genotype and family history of Alzheimer’s disease) with dementia outcomes across 450,707 UK Biobank participants using SCA with 5357 specifications across dementia subtypes (outcomes) and analytic models (e.g., standard demographic covariates such as age or sex and/or 14 correlated risk factors).ResultsSCA revealed variable dementia risks by subtype and age, with associations for TBI and APOE e4/e4 robust to model specification; in contrast, diabetes showed fluctuating links with dementia subtypes. We found that unattributed dementia participants had similar risk factor profiles to participants with defined subtypes.ConclusionsWe observed heterogeneity in the risk of dementia, and estimates of risk were influenced by the inclusion of a combination of other modifiable risk factors; non-modifiable demographic factors had a minimal role in analytic heterogeneity. Future studies should report multiple plausible analytic scenarios to test the robustness of their association. Considering these combinations of risk factors could be advantageous for the clinical development and evaluation of novel screening models for different types of dementia.

A risk estimation method for depression based on the dysbiosis of intestinal microbiota in Japanese patients

IntroductionEarly detection of depression is important for preventing depression-related suicides and reducing the risk of recurrence. This study explored the association between depression and intestinal microbiota and developed a depression risk-estimation method based on this.MethodsThe intestinal microbiota of Japanese patients with depression (33 males and 35 females) and disease-free controls (246 males and 384 females) in their 20’s to 60’s were compared by sex using 16S rRNA gene amplicon sequencing. A depression-risk estimation method was developed using structural equation modeling.ResultsIntestinal bacteria taxa that differed between depression and control groups were identified based on effect size (absolute value greater than 0.2). Neglecta was more abundant, while Coprobacter, Butyricimonas, Clostridium_XlVb, and Romboutsia were less abundant in the male depression group compared to the male control group. In the female depression group, Massilimicrobiota, Merdimonas, and Sellimonas were more abundant, whereas Dorea and Agathobacter were less abundant compared to the female control group. Several of the intestinal bacterial taxa that were less abundant in depression were associated with butyrate or hydrogen production. Using these depression-associated intestinal bacteria as indicators, risk-estimation models using structural equation modeling for depression were developed. In the risk-estimation models for males and females, the areas under the receiver operating characteristic curve were 0.72 and 0.70, respectively, indicating that these models can distinguish between individuals with and without depression.ConclusionsThis study provides insights into depression etiology and aids in its early detection and treatment.

Seventy Years of Dose-response Models: From the Target Theory to the Use of Big Databases Involving Cell Survival and DNA Repair.

Radiobiological data, whether obtained at the clinical, biological or molecular level has significantly contributed to a better description and prediction of the individual dose-response to ionizing radiation and a better estimation of the radiation-induced risks. Particularly, over the last seventy years, the amount of radiobiological data has considerably increased, and permitted the mathematical formulas describing dose-response to become less empirical. A better understanding of the basic radiobiological mechanisms has also contributed to establish quantitative inter-correlations between clinical, biological and molecular biomarkers, refining again the mathematical models of description. Today, big data approaches and, more recently, artificial intelligence may finally complete and secure this long process of thinking from the multi-scale description of radiation-induced events to their prediction. Here, we reviewed the major dose-response models applied in radiobiology for quantifying molecular and cellular radiosensitivity and aimed to explain their evolution: Specifically, we highlighted the advances concerning the target theory with the cell survival models and the progressive introduction of the DNA repair process in the mathematical models. Furthermore, we described how the technological advances have changed the description of DNA double-strand break (DSB) repair kinetics by introducing the important notion of DSB recognition, independent of that of DSB repair. Initially developed separately, target theory on one hand and, DSB recognition and repair, on the other hand may be now fused into a unified model involving the cascade of phosphorylations mediated by the ATM kinase in response to any genotoxic stress.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations.

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736–0.903), along with an F1 score of 0.798 (0.594–0.905), precision of 0.806 (0.596–0.896), recall of 0.830 (0.650–0.946), and a Cohen’s Kappa (κ) of 0.708 (0.562–0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model’s competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.

Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population.

Risk stratification based on prediction models has become increasingly important in preventing and managing chronic diseases. However, due to cost- and time-limitations, not every population can have resources for collecting enough detailed individual-level information on a large number of people to develop risk prediction models. A more practical approach is to use prediction models developed from existing studies and calibrate them with relevant summary-level information of the target population. Many existing studies were conducted under the population-based case-control design. Gail et al. (J Natl Cancer Inst 81:1879-1886, 1989) proposed to combine the odds ratio estimates obtained from case-control data and the disease incidence rates from the target population to obtain the baseline hazard function, and thereby the pure risk for developing diseases. However, the approach requires the risk factor distribution of cases from the case-control studies be same as the target population, which, if violated, may yield biased risk estimation. In this article, we propose two novel weighted estimating equation approaches to calibrate the baseline risk by leveraging the summary information of (some) risk factors in addition to disease-free probabilities from the targeted population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation studies and an application to colorectal cancer studies demonstrate the proposed estimators perform well for bias reduction in finite samples.

Comparison of Triglyceride–Glucose (TyG) Index and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) Index in Prediction of Male Hypogonadism

Several studies have shown the association between decreased insulin sensitivity and the risk of male hypogonadism. Homeostatic model assessment of insulin resistance (HOMA-IR) is a well-established marker of decreased insulin sensitivity. The triglyceride–glucose index (TyG), calculated as ln fasting triglyceride mg/dL× fasting glucose mg/dL/2, was recently suggested to be a cheaper and a reliable surrogate marker to detect insulin resistance (IR). Our aim was to compare the performance of those two indexes in the prediction of male hypogonadism. The data on 192 men from infertile couples (18–50 years; sperm concentration <20 x 106/mL) and 199 population-based matched controls collected during the years 2009–2012 (baseline) were evaluated retrospectively. Half of these subjects (72 subfertile men and 122 controls) were reinvestigated 5–10 years later (median year (range): 7 (5–10)). The patients receiving any hormonal therapy were excluded. Hypogonadism was defined as fasting, morning serum testosterone below 12 nmol/L. In receiver operating characteristic curve analysis, the optimal diagnostic cutoff values for baseline HOMA-IR and TyG to predict MetS at re-examination were 2.68 (Area Under Curve (AUC) = 0.886, p < 0.001) and 8.60 (AUC = 0.816, p <0.001), respectively. Moreover, in binary logistic regression analysis performed on the whole cohort using these thresholds for high values of HOMA-IR and high TyG, the odds-ratios (ORs) for hypogonadism were 6.48 (95% Confidence Interval (CI): 3.77–11.2; p <0.001) and 3.58 (95% CI: 2.17–5.94; p <0.001), respectively. Even though high HOMA-IR levels provided better risk estimates, high TyG was also highly related to the risk of hypogonadism. These markers can be utilized to identify men being at high risk of hypogonadism.

VALUE AT RISK ESTIMATION USING EXTREME VALUE THEORY APPROACH IN INDONESIA STOCK EXCHANGE

Extreme Value Theory (EVT) is a method used to identify extreme cases in heavy tail data such as financial time series data. This research aimed to obtain an estimate of stock risk through the EVT approach and compare the accuracy of the two EVT approaches, Block Maxima (BM) and Peaks Over Threshold (POT). The method used to estimate stock risk is VaR with the BM and POT approaches, and the Z statistic is used to compare the accuracy. The data used, and the limitation in this research is daily closing price data for non-cyclical consumer stocks included in LQ45 for the period February 01, 2017, to January 31, 2023. Other research limitations are using weekly blocks or 5 working days in dividing BM blocks, using the percentage method in determining threshold values in the POT approach, and using Maximum Likelihood Estimation (MLE) to estimate EVT parameter estimates. The results of the VaR analysis show that the risk level generated by the POT method is greater than the risk level from BM. The results of backtesting between the two EVT approaches in estimating VaR values show that the POT approach is more accurate than the BM approach.

Evolving Concepts of the SCORE System: Subtracting Cholesterol from Risk Estimation: A Way for a Healthy Longevity?

The role of cholesterol, mainly low-density lipoproteins (LDL-C), as a causal risk factor for atherosclerotic cardiovascular disease (ASCVD) is now established and accepted by the international scientific community. Based on this evidence, the European and American guidelines recommend early risk stratification and “rapid” achievement of the suggested target according to the risk estimation to reduce the number of major cardiovascular events. Prolonged exposure over the years to high levels of LDL-C is one of the determining factors in the development and progression of atherosclerotic plaque, on which the action of conventional risk factors (cigarette smoking, excess weight, sedentary lifestyle, arterial hypertension, diabetes mellitus) as well as non-conventional risk factors (gut microbiota, hyperuricemia, inflammation), alone or in combination, favors the destabilization of the atherosclerotic lesion with rupture/fissuration/ulceration and consequent formation of intravascular thrombosis, which leads to the acute clinical manifestations of acute coronary syndromes. In the current clinical practice, there is a growing number of cases that, although extremely common, are emblematic of the concept of long-term exposure to the risk factor (LDL hypercholesterolemia), which, not adequately controlled and in combination with other risk factors, has favored the onset of major cardiovascular events. The triple concept of “go lower, start earlier and keep longer!” should be applied in current clinical practice at any level of prevention. In the present manuscript, we will review the current evidence and documents supporting the causal role of LDL-C in determining ASCVD and whether it is time to remove it from any score.

Critical Appraisal of the 2017 Bethesda System for Reporting Thyroid Cytopathology with Cytohistological Concordance and Estimation of Risk of Malignancy - An Institutional Study

Critical Appraisal of the 2017 Bethesda System for Reporting Thyroid Cytopathology with Cytohistological Concordance and Estimation of Risk of Malignancy - An Institutional Study

Building resilience to multiple climate change-related risks in QwaQwa using the community capitals approach

ABSTRACT QwaQwa, in South Africa, experiences high exposure to multiple climate change impacts. The area is particularly vulnerable to the effects of climate change. As such, this paper aims to assess community resilience, estimate the risk of multiple climate change impacts in QwaQwa, and propose strategies to reduce risk and build resilience. Data were collected using a household survey and key informant interviews with a sample of 349 participants. An indicator method, based on the Community Capitals Framework, was used to measure resilience and estimate risk. The composite resilience indicators were weighted using Principal Component Analysis. The Cronbach's alpha (α) test indicated a very good internal consistency in the scaled items. The results indicated that medium resilience to climate change impacts in QwaQwa was mostly driven by access to all six capitals measured in this study: natural, human, social, financial, political, and infrastructural capitals. The multi-hazard risk estimate resulted in a very high index of 4.1. These results were validated at a stakeholder workshop. Strategies that use nature-based solutions, training and education for people of all ages, sectors and professions, and increased community and local government cooperation were deemed essential for boosting resilience and reducing disaster risks in the area.

A Deep Learning Method to Mitigate the Impact of Subjective Factors in Risk Estimation for Machinery Safety

A Deep Learning Method to Mitigate the Impact of Subjective Factors in Risk Estimation for Machinery Safety

Artificial intelligence enabled electrocardiogram for mortality and ventricular arrhythmia prediction

Abstract Background Artificial intelligence-enhanced electrocardiograms (AI-ECG) can be used to predict risk of events but existing models suffer from lack of actionability, explainability and biological plausibility, and therefore are not used clinically. Purpose We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE). Methods AIRE was developed in a dataset of 1,163,401 ECGs from 189,539 patients from a secondary care setting in the USA. Uniquely, AIRE uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to create a subject-specific survival curve using a single ECG. AIRE was trained to predict time-to-mortality and fine-tuned for the additional endpoints described below. Results AIRE outputs a subject-specific survival curve that predicts not only mortality but time-to-mortality, using only a single ECG (Figure 1A). AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776), Figure 2) and can differentiate prognosis in high and low risk subjects even when only cardiologist reported normal ECGs are considered (Figure 2). AIRE also accurately predicted cardiovascular (CV) death 0.832 (0.831-0.834), future ventricular arrhythmia (0.760 (0.756-0.763)), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.889))). In particular AIRE was superior to left ventricular ejection fraction (LVEF) in predicting risk of future ventricular arrhythmias in subjects with LVEF <50% (C-index 0.649 (0.638 – 0.660) vs 0.615 (0.603 – 0.626) p < 0.0001 ) and in dilated cardiomyopathy (C-index 0.690 (0.669-0.711) vs 0.608 (0.584 – 0.631), p < 0.0001). We used a variational autoencoder (VAE) to visualise the features most correlated with the mortality predictions. Features of QRS morphology, particularly broader and more left bundle branch block morphologies, inverted and biphasic T waves as well as ST segment changes were associated with high predicted mortality (Figure 1B). We externally validated our findings in four diverse, transnational cohorts from Brazil and the UK including volunteers and primary care subjects (Figure 2), and found AIRE accurately predicted all-cause mortality in all four external cohorts. Through phenome- and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function (LV mass, LVEF, LV trabeculation), and genes associated with cardiac structure, QT interval, biological aging and metabolic syndrome (VGLL2, KCNQ1, CCDC91, TBX3). Conclusion AIRE is an explainable, biologically plausible and actionable AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short- and long-term risk estimation.Figure 1Figure 2

#783 Individualized net benefit of intensive blood pressure lowering among older adults in SPRINT

Abstract Background and Aims The optimal blood pressure (BP) target for older adults remains controversial. This study aimed to determine whether results from the Systolic Blood Pressure Intervention Trial (SPRINT) could be tailored to individual trial participants using risk estimates and simulated preferences about the relative importance of multiple outcomes related to BP lowering. Method This analysis included 4,850 SPRINT participants age ≥65 years. Parsimonious Cox models were internally validated to predict the absolute difference in risk between intensive versus standard BP lowering in each SPRINT participant for all-cause death, cardiovascular outcomes, cognitive outcomes, and serious adverse events. Treatment effects were combined using simulated preference weights into an individualized net benefit, which represents the weighted sum of risk differences across outcomes. Individualized net benefits among older SPRINT participants were compared across categories of age (65-74 years vs. ≥ 75 years), frailty (fit, less fit, and frail), and polypharmacy status. Results When simulating preferences for older adults who view death, cardiovascular events, and cognitive impairment as much more important than the harms of BP lowering, the median (IQR) individualized net benefit was 5% (3%, 7%), and 100% had a net benefit that favoured intensive BP lowering. When simulating benefits and harms to have similar importance, the median (IQR) individualized net benefit was 2% (1%, 3%), and 95.4% had a positive net benefit. In subgroup analyses, the predicted net benefit of intensive BP lowering was significantly greater among those with advanced age, frailty, and polypharmacy compared with lower risk individuals (P<0.001 for all comparisons). Conclusion Almost all older participants in SPRINT had a predicted net benefit that favoured intensive BP control, but the degree of net benefit varied considerably. Personalizing BP targets for older adults according to each person's estimated risks and preferences may provide more refined BP target recommendations.

#2468 2-Step-Scores with optional histopathology for the prediction of delayed graft function and 1-year transplant loss in Eurotransplant

Abstract Background and Aims The decision for acceptance or discard of the increasingly rare and marginal deceased donor kidneys in Eurotransplant (ET) countries has to be made without solid evidence. Thus, we developed and validated flexible clinicopathological scores we call 2-Step Scores for the prognosis of delayed graft function (DGF) as 2-Step-DGF and one-year death-censored transplant loss (1y-tl) as 2-Step-1y, reflecting current practice of six ET countries including Croatia and Belgium. Method The training set was n = 620 for DGF and n = 711 for 1y-tl, with validation sets n = 158 and n = 162. In step 1, stepwise logistic regression models including only clinical predictors were used to estimate the risks. In step 2, risk estimates were updated for statistically relevant intermediate risk percentiles with nephropathology. Results Step 1 revealed an increased risk of DGF with increased cold ischaemia time, donor and recipient BMI, dialysis vintage, number of HLA-DR mismatches or recipient CMV IgG positivity. On the training and validation set, c-statistics were 0.672 and 0.704, respectively. At a range between 18% and 36%, accuracy of DGF-prognostication improved with nephropathology including number of glomeruli and Banff cv (updated overall c statistics of 0.696 and 0.701, respectively). Risk of 1y-tl increased in recipients with cold ischaemia time, sum of HLA-A. -B, -DR mismatches and donor age. On training and validation sets, c-statistics were 0.700 and 0.769, respectively. Accuracy of 1y-tl prediction improved (c-statistics = 0.706 and 0.765) with Banff ct. Overall, calibration was good on the training, but moderate on the validation set; discrimination was at least as good as established scores when applied to the validation set. Receiver operating characteristics of both 2-Step-DGF and 2-Step-1y compared to established scores are shown in Figs 1 and 2. Conclusion Our flexible 2-Step Scores with optional inclusion of time-consuming and often unavailable nephropathology should yield good results for clinical practice in ET, and may be superior to established scores. Our scores are adaptable to donation after cardiac death and perfusion pumps use.

#1012 Lifetime risk of autosomal recessive kidney diseases calculated from genetic databases

Abstract Background and Aims Genetic kidney diseases represent an often rare and clinically diverse group. Their distinct clinical and genetic variations and biases in patient referrals and identification pose challenges in phenotype-based estimated prevalences. This study aimed to determine the calculated lifetime risk of autosomal recessive kidney diseases. Method We conducted a thorough literature review to compile a list of 149 genes linked to these diseases. Disease-causing variants were collected from ClinVar, HGMD, LOVD, and our database and reevaluated according to current guidelines. Minor allele frequencies were obtained from gnomAD and our database. We estimated the lifetime risk of autosomal recessive kidney diseases in a dataset of 12,800 disease-causing variants across 149 disease-associated genes (31 glomerulopathy, 16 tubulopathy, 87 ciliopathy, and 15 CAKUT genes). Results The combined estimated lifetime risk is 10.68 per 100,000 (95% confidence interval 6.29-18.40) based on our data and 22.77 per 100,000 (15.86-33.77) using the European gnomAD dataset. Notably, the three disorders with the highest lifetime risk (≥1.2 per 100,000), collectively accounting for 28% of the overall lifetime risk, were caused by PKHD1 (autosomal recessive polycystic kidney disease), SLC12A3 (Gitelman syndrome), and COL4A3 (Alport syndrome) variants (Fig. 1). Extrapolation to all modes of inheritance, the overall lifetime risk for monogenic kidney disease in adults ranged from 1 in 602 to 1 in 1,283 (Fig. 2). Conclusion This study provides a population-genetic estimate of lifetime risk for autosomal recessive kidney diseases across different populations, addressing underestimated prevalences and diagnostic probabilities. The data, therefore, informs resource allocation for therapy development, public health management, and biomedical research.

A robust assessment of inland waterway collision risk based on AIS and visual data fusion

By integrating AIS data with visual data, this study aims to propose a data fusion-based methodology for the robust assessment of vessel collision risk in inland waterways. With the data from the Huangpu River, one case study is created and comparison results show that visual data could greatly overestimate the vessel collision risk while the vessel conflict count could be underestimated from AIS data. The proposed methodology could contribute to a 35.70% reduction in the false detection rate compared to the pure visual data but an increase of 16.84% in the conflict detection accuracy compared to the AIS data. Moreover, results show that the proposed methodology is able to perform much better than the pure AIS data technique during the daytime period while it outperforms the pure visual data technique during the nighttime period. This suggests that the proposed data-fusion methodology could provide a more reliable and accurate estimate for the vessel collision risk in inland waterways. Results also show that higher collision risk is associated with the high tide period and turning area in the Huangpu River. Therefore, special attention should be placed to enhance the navigation safety during this time period for the inland waterway turning area.