Risk-based Strategies Research Articles

The promising efficacy of a risk-based letermovir use strategy in CMV-positive allogeneic hematopoietic cell recipients.

Letermovir is the first approved drug for cytomegalovirus (CMV) infection prophylaxis in adult patients who are CMV positive undergoing allogeneic hematopoietic cell transplantation (allo-HCT). Because CMV infection risk varies from patient to patient, we evaluated whether a risk-based strategy could be effective. In this single-center study, all consecutive adult patients who were CMV positive and underwent allo-HCT between 2015 and 2021 were included. During period 1 (2015-2017), letermovir was not used, whereas during period 2 (2018-2021), letermovir was used in patients at high risk but not in patients at low risk, except in those receiving corticosteroids. In patients at high risk, the incidence of clinically significant CMV infection (csCMVi) in period 2 was lower than that in period 1 (P< .001) by week 14 (10.5% vs 51.6%) and week 24 (16.9% vs 52.7%). In patients at low risk, although only 28.6% of patients received letermovir in period 2, csCMVi incidence was also significantly lower (P= .003) by week 14 (7.9% vs 29.0%) and week 24 (11.2% vs 33.3%). Among patients at low risk who did not receive letermovir (n= 45), 23 patients (51.1%) experienced transient positive CMV DNA without csCMVi, whereas 17 patients (37.8%) experienced negative results. In both risk groups, the 2 periods were comparable for CMV disease, overall survival, progression-free survival, relapse, and nonrelapse mortality. We concluded that a risk-based strategy for letermovir use is an effective strategy which maintains the high efficacy of letermovir in patients at high risk but allows some patients at low risk to not use letermovir.

Resilience and risk-based restoration strategies for critical infrastructure under uncertain disaster scenarios

Improving the resilience of critical infrastructure (CI) systems to natural disasters is essential for society's long-term development. Considering the uncertainty in disaster scenarios, this study first proposes a two-stage risk-neutral stochastic model to develop restoration resource deployment and allocation strategies with the objective of maximizing the expected value of resilience (EVR) of a CI system. Secondly, a risk metric is applied to assess the risk on the value of resilience imposed by the uncertainty. And a multi-objective optimization model for risk management is proposed, in which minimizing the value of the risk metric is formulated as a secondary objective to the EVR. The trade-offs between the EVR and the risk objective are assessed via the ε-constraint method. Finally, the utility of proposed models is illustrated using the electric power system of the Greater Toronto Area (GTA). The results show that: (i) there is a significant advantage in using risk-neutral strategies to improve system resilience over strategies without considering the characteristics of disaster scenarios; (ii) the value of the risk metric can be significantly reduced at the expense of decreasing EVR; and (iii) if the use cost of the resources is considered, there are optimal amounts of available resources for risk-neutral and risk-averse CI system operators. The models can assist CI system operators in selecting effective emergency response strategies for disasters with uncertain scenarios.

Abstract PD14-01: PD14-01 A model to assess the utility of risk-based screening algorithms

Abstract PD14-01: PD14-01 A model to assess the utility of risk-based screening algorithms

Machine learning-based model for predicting the esophagogastric variceal bleeding risk in liver cirrhosis patients

BackgroundLiver cirrhosis patients are at risk for esophagogastric variceal bleeding (EGVB). Herein, we aimed to estimate the EGVB risk in patients with liver cirrhosis using an artificial neural network (ANN).MethodsWe included 999 liver cirrhosis patients hospitalized at the Beijing Ditan Hospital, Capital Medical University in the training cohort and 101 patients from Shuguang Hospital in the validation cohort. The factors independently affecting EGVB occurrence were determined via univariate analysis and used to develop an ANN model.ResultsThe 1-year cumulative EGVB incidence rates were 11.9 and 11.9% in the training and validation groups, respectively. A total of 12 independent risk factors, including gender, drinking and smoking history, decompensation, ascites, location and size of varices, alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), hematocrit (HCT) and neutrophil-lymphocyte ratio (NLR) levels as well as red blood cell (RBC) count were evaluated and used to establish the ANN model, which estimated the 1-year EGVB risk.The ANN model had an area under the curve (AUC) of 0.959, which was significantly higher than the AUC for the North Italian Endoscopic Club (NIEC) (0.669) and revised North Italian Endoscopic Club (Rev-NIEC) indices (0.725) (all P < 0.001). Decision curve analyses revealed improved net benefits of the ANN compared to the NIEC and Rev-NIEC indices.ConclusionsThe ANN model accurately predicted the 1-year risk for EGVB in liver cirrhosis patients and might be used as a basis for risk-based EGVB surveillance strategies.

Identification of disinfection by-products (DBP) in thermal water swimming pools applying non-target screening by LC-/GC-HRMS

The discovery of new disinfection by-products (DBPs) is still a rarely investigated research area in past studies. In particular, compared to freshwater pools, therapeutic pools with their unique chemical composition have rarely been investigated for novel DBPs. Here we have developed a semi-automated workflow that combines data from target and non-target screening, calculated and measured toxicities into a heat map using hierarchical clustering to assess the pool's overall potential chemical risk. In addition, we used complementary analytical techniques such as positive and negative chemical ionization to demonstrate how novel DBPs can be better identified in future studies. We identified two representatives of the haloketones (pentachloroacetone, and pentabromoacetone) and tribromo furoic acid detected for the first time in swimming pools. Non-target screening combined with target analysis and toxicity assessment may help to define risk-based monitoring strategies in the future, as required by regulatory frameworks for swimming pool operations worldwide.

Prostate cancer risk assessment by the primary care physician and urologist: transabdominal- versus transrectal ultrasound prostate volume-based use of the Rotterdam Prostate Cancer Risk Calculator.

Our objective was to assess the accuracy of transabdominal ultrasound (TAUS) measured prostate volume in the primary care setting with transrectal ultrasound (TRUS) measured prostate volume by the urologist as the reference test. Furthermore, our objective was to assess whether risk-stratification using TAUS prostate volume by the primary care physician could reduce unnecessary referrals to the urologist. Men in two Dutch primary care offices with a prostate cancer (PCa) screening request received a digital rectal examination (DRE), prostate specific-antigen (PSA), and TAUS prostate volume measurement by the general practitioner, followed by Rotterdam Prostate Cancer Risk Calculator (RPCRC) risk assessment. The examination was repeated by a urologist using TRUS. A prostate biopsy was performed in case of a RPCRC positive biopsy advice. A non-inferiority analysis was performed comparing TAUS and TRUS prostate volume differences. A risk-based referral strategy using TAUS and the RPCRC in the primary care setting was compared with the standard referral strategy based on PSA (≥3 ng/mL) and DRE. A total of 105 men were included with a median PSA of 1.9 ng/mL. The mean prostate volumes measured by TAUS and TRUS were 55 and 45 mL, respectively. The mean overestimation of the prostate volume by TAUS as compared to the reference test was 9.9 mL (95% CI: 5.9-13.8). According to Dutch standard practice, 41 out of 105 (39%) men would have been referred to the urologist. Stratification in primary care based on the RPCRC using TAUS prostate volume would have avoided 29 out of the 41 (71%) referrals, at the expense of non-referral of 5 out of 11 (45%) men with a biopsy indication, according to the urologist. RPCRC-based risk stratification in primary care using TAUS prostate volume measurement is feasible and may prevent unnecessary referrals to the urologist and reduce costs. The accuracy of the risk assessment with TAUS might be improved by sufficient training and centralization to achieve a higher volume of consultations in primary care facilities.

Pavement maintenance planning using a risk-based approach and fault tree analysis

ABSTRACT Optimal planning of Maintenance and Rehabilitation (M&R) treatments due to limited resources and funding is vital. A pavement performance function formula is needed to model the M&R optimisation problem. By considering important features, a pavement deterioration function was developed with an R2 of 0.897. The essence of pavement deterioration is uncertain. Hence, disregarding the uncertainty may result in sub-optimal solutions. Therefore, two approaches were employed to optimise M&R treatments in a large-scale pavement network, including deterministic and risk-based. Both approaches aimed to minimise the required budget in the planning period constrained to acceptable pavement condition. A novel and robust metaheuristic algorithm was adjusted to solve the M&R planning problem deterministically. In the risk-based method, appropriate probability distribution functions were fitted to historical data of uncertain parameters. Monte Carlo simulation was performed to generate probability distributions of the required budget and condition of pavement sections. By using a risk-based strategy instead of the deterministic one, the required budget was reduced by 30%. Fault tree analysis was performed on the solutions obtained from the risk-based method to increase the pavement network’s reliability. The results of the mentioned analysis represented that only by spending 2.5% more budget network’s reliability could be significantly increased.

A hybrid decision support system with golden cut and bipolar q-ROFSs for evaluating the risk-based strategic priorities of fintech lending for clean energy projects

In the last decade, the risk evaluation and the investment decision are among the most prominent issues of efficient project management. Especially, the innovative financial sources could have some specific risk appetite due to the increasing return of investment. Hence, it is important to uncover the risk factors of fintech investments and investigate the possible impacts with an integrated approach to the strategic priorities of fintech lending. Accordingly, this study aims to analyze a unique risk set and the strategic priorities of fintech lending for clean energy projects. The most important contributions to the literature can be listed as to construct an impact-direction map of risk-based strategic priorities for fintech lending in clean energy projects and to measure the possible influences by using a hybrid decision making system with golden cut and bipolar q-rung orthopair fuzzy sets. The extension of multi stepwise weight assessment ratio analysis (M-SWARA) is applied for weighting the risk factors of fintech lending. The extension of elimination and choice translating reality (ELECTRE) is employed for constructing and ranking the risk-based strategic priorities for clean energy projects. In this process, data is obtained with the evaluation of three different decision makers. The main superiority of the proposed model by comparing with the previous models in the literature is that significant improvements are made to the classical SWARA method so that a new technique is created with the name of M-SWARA. Hence, the causality analysis between the criteria can also be performed in this proposed model. The findings demonstrate that security is the most critical risk factor for fintech lending system. Moreover, volume is found as the most critical risk-based strategy for fintech lending. In this context, fintech companies need to take some precautions to effectively manage the security risk. For this purpose, the main risks to information technologies need to be clearly identified. Next, control steps should be put for these risks to be managed properly. Furthermore, it has been determined that the most appropriate strategy to increase the success of the fintech lending system is to increase the number of financiers integrated into the system. Within this framework, the platform should be secure and profitable to persuade financiers.

Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent system

Over the last decade, the international community has become more aware of the danger and harm caused by the practice of the crime of Money Laundering (ML). Organizations to combat this crime were created and the existing laws were expanded and tightened. Complex legislation, combined with the increase in the volume of financial transactions involved in money laundering schemes, motivated research targeting the improvement and automation of the critical processes of detecting, signaling and communicating suspicious clients. The Anti-Money Laundering (AML) activity is strongly impacted and dependent on this process, which has evolved slowly, in part due to its subjectivity and complexity. This article presents the characteristics of a multiagent system that incorporates machine learning and risk components to identify and flag suspicious bank clients. It also seeks to help human specialists in the analysis of suspicious behavior by these clients. The knowledge bases are obtained through data mining over a real database, obtaining for each client a transactional behavior pattern, enriched with specific rules based on legal aspects, and on the expertise of the AML analysts of the financial institution that funds this research. The adopted risk strategy seeks to identify and classify the ML risk level of each flagged client. To evaluate its results, the system analyzed six months of actual transactions. A set of candidate clients was produced and subsequently investigated by the financial institution’s AML analysts, who proved a high degree of correctness in the indicated suspicions, including cases that were not flagged by the antecedent systems in execution for decades at the institution.

Significant gaps in hepatitis B vaccination in adults in Viet Nam: Important targets toward hepatitis B elimination by 2030.

Gaps in adult hepatitis B vaccination were undefined in Vietnam, a lower-middle-income country. To address these gaps, this study defined hepatitis B vaccine coverage in adults and its associated factors in Ho Chi Minh City (HCMC), Viet Nam. We also proposed interventional strategies, prioritizing gap identification to facilitate hepatitis B elimination by 2030 and beyond. During 2019-2020, a multi-stage cluster serosurvey with probability proportional to size was conducted to representatively invite 20,000 adults (18years or older) throughout HCMC for hepatitis B screening (HBsAg, anti-HBs, and anti-HBc). Serologic results defined two dependent variables: vaccine-induced immunity (i.e., isolated anti-HBs) and susceptibility (i.e., HBV naive). Associations of dependent variables with surveyed demographics, socioeconomic statuses, behaviors, and medical history at risk for hepatitis B were evaluated using weighted Poisson regression. The prevalence was 18.5% (95%CI, 17.3-20.0%) for vaccine-induced immunity and 37.7% (35.6-39.8%) for susceptibility. Even though analyses in the general population revealed a falling trend in vaccine-induced immunity prevalence from younger to older age groups, sensitivity analyses in the non-infected population (i.e., those who were both negative for HBsAg and anti-HBc) showed that younger age groups, especially those aged 30 to 50years, had the lowest prevalence. Social inequalities existed in different ethnicities, residence areas, education levels, house ownership, and health insurance statuses. There was no significant association between vaccine-induced immunity or susceptibility and risky behaviors and medical histories. This study depicts a significant unmet need for hepatitis B vaccination in the general adult population in HCMC, Viet Nam. Indeed, the lack of vaccination was unevenly distributed regarding age groups, geographical areas, and socioeconomic statuses, which reveals profound social disparities. Therefore, to achieve hepatitis B elimination goals, besides the current recommendations for infants and risk-based strategies, hepatitis B vaccination should be recommended for the broader population.

Uporedna studija o proceni rizika elementarnih nečistoća u Montelukast tabletama za žvakanje i filmom obloženim tabletama

It is well documented that elemental impurities (EIs) are critical in the field of pharmaceutical development since they could affect the quality, efficacy and safety of the finished dosage form (FDF). The responsibility of pharmaceutical manufacturers is to demonstrate via assessment approach, risk-based control strategy and/or required data analysis that the FDFs are compliant with ICH Q3D (R2). The aim of this research is to conduct a comprehensive comparative EIs risk assessment study of three different Montelukast dosage forms produced as chewable tablets (4 mg and 5 mg) and film-coated tablets 10 mg. The inductively coupled plasma-mass spectrometry (ICP-MS) system was used for the determination of EIs in samples of Montelukast sodium as the active pharmaceutical ingredient (API), placebos for all FDFs, and FDFs. Moreover, the analyses were also conducted on three batches from all three studied FDFs. Based on ICH Q3D (R2) guidelines, the tested products for EIs Class 1 and Class 2A showed that EIs levels in the API and placebos are well below the ICH Option 1 oral and parenteral limits. For the examined batches of each FDF strength (total of 9), none of the EI exceeds their concentration limits.

Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations

Owing to potential regulation capacities from flexible resources in energy coupling, storage, and consumption links, central energy stations (CESs) can provide additional support to power distribution network (PDN) in case of power disruption. However, existing research has not explicitly revealed the emergency response of PDN with leveraging multiple CESs. This paper proposes a decentralized self-healing strategy of PDN to minimize the entire load loss, in which multi-area CESs' potentials including thermal storage and building thermal inertia, as well as the flexible topology of PDN, are reasonably exploited for service recovery. For sake of privacy preservation, the co-optimization of PDN and CESs is realized in a decentralized manner using adaptive alternating direction method of multipliers (ADMM). Furtherly, bilateral risk management with conditional value-at-risk (CVaR) for PDN and risk constraints for CESs is integrated to deal with uncertainties from outage duration. Case studies are conducted on a modified IEEE 33-bus PDN with multiple CESs. Numerical results illustrate that the proposed strategy can fully utilize the potentials of multi-area CESs for coordinated load restoration. The effectiveness of the performance and behaviors' adaptation against random risks is also validated.

Laser Measurement and Numerical Simulation of Elastomer Stopper Motion during High-Altitude Shipping of Pharmaceutical Syringes.

During high-altitude shipping of pre-filled syringes, pressure differentials can cause the elastomer stopper to move unintentionally. This motion represents a risk to container closure integrity and drug product sterility. To understand and quantitate this risk, we combined high-accuracy laser measurements and numerical simulations of stopper motion. We tested the effects of syringe barrel siliconization, stopper design, syringe orientation, and altitude rate on stopper displacement; only the siliconization factor had a significant effect. Our observations were compared with two mathematical models based on Boyle's Law and a force balance approach. For well-lubricated syringes, stopper motion was reasonably predicted by Boyle's Law (residual ≤ 10%). When the lubricant amount was reduced, Boyle's Law failed to accurately predict stopper motion (residual ≈ 40%). To simulate stopper motion more accurately, we developed a dynamic model in MATLAB-Simulink to incorporate the dry and viscous friction inherent to the lubricated interference fit. Using a Coulomb-viscous subroutine, deviations from Boyle's Law were successfully explained in terms of the displacement, but the system dynamics were not fully accurate. The combination of laser measurements and numerical simulation has yielded unique insight into stopper motion during high-altitude shipping. These tools can provide valuable input to a risk-based drug development strategy to enable global distribution of pre-filled syringes.

Life-Cycle Risk-Based Optimal Maintenance Strategy for Bridge Networks Subjected to Corrosion and Seismic Hazards

Life-Cycle Risk-Based Optimal Maintenance Strategy for Bridge Networks Subjected to Corrosion and Seismic Hazards

Predictors of Coronary Artery Disease (CAD) In Potential Liver Transplant (LT) Recipients and Performance ofThe CAD-LT Score In Identifying A Risk-Based Strategy For CAD

Predictors of Coronary Artery Disease (CAD) In Potential Liver Transplant (LT) Recipients and Performance ofThe CAD-LT Score In Identifying A Risk-Based Strategy For CAD

Does Bitcoin Contribute to Portfolio Performance?

This study analyzes the role of Bitcoin as an investment asset in a global multi-asset portfolio. For portfolio construction, we use a risk-based portfolio strategy that excludes estimates of future expected returns. We derive the optimal asset allocation ratio of Bitcoin included in the global multi-asset portfolio and compare the performance with the general portfolio. We find that the average investment weight of Bitcoin in the portfolio is 1.8%, and the portfolio containing Bitcoin shows superior investment performance compared to the portfolio without Bitcoin.

Risk-based contamination control strategy of manufacturing non-sterile pharmaceutical products

Risk-based contamination control strategy of manufacturing non-sterile pharmaceutical products

Modeling Optimal Energy Exchange Operation of Microgrids Considering Renewable Energy Resources, Risk-based Strategies, and Reliability Aspect Using Multi-objective Adolescent Identity Search Algorithm

The inherent volatility and unpredictable nature of renewable resources and load demand have posed significant challenges to the optimal energy exchange of microgrids (MGs). Therefore, this study presents a novel risk-based multi-objective energy exchange optimization for the networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. For this purpose, three different risk-based strategies are distinguished using the conditional value at risk approach. The proposed model is specified as an objective function in two distinct ways. The first function minimizes operation and maintenance costs, the cost of exchanging power between the upstream grid and MGs, and the cost of power loss, whereas the second function minimizes the amount of energy that is not supplied. Moreover, a stochastic scenario-based approach is incorporated into this approach to control uncertainties. Also, the scenario reduction method is implemented to reduce the computational burden. Finally, a developed multi-objective adolescent identity search algorithm (AISA) is proposed. Pareto theory and non-dominated sorting are employed to develop the proposed model. In the proposed data development model, it has been tried to improve the local and global search in a better way in order to solve local points and early convergence. The fuzzy model is used to select the optimal solution from the set of solutions. The modified IEEE 33-bus distribution system is used to implement the suggested model and assess its efficacy. The obtained results show that the proposed risk-based model can control the risk of the EMS problem due to the stochastic behavior of the input parameters. In this paper, for risk assessment high consequences and low probability events are considered extreme events. In the risk aversion strategy, the cost of the event is significantly reduced compared to the risk strategy, while the additional cost is much less than this is.

A risk-based post-resection follow-up strategy for hepatocellular carcinoma patients.

The optimal intervals for follow-up after hepatocellular carcinoma (HCC) patients undergo curative liver resection (LR) remain unclear. This study aimed to establish a risk-based post-resection follow-up strategy. Patients that were diagnosed with HCC and received LR from three hospitals in China were included. The risk-based strategy was established based on the random survival forest model and compared with a fixed strategy both internally and externally. In total, 3447 patients from three hospitals were included. The authors' strategy showed superiority in the early detection of tumor relapse compared with fixed surveillance. Under fewer total visits, risk-based strategy achieved analogous survival time compared to the total 20 times follow-ups based on fixed strategy. Twelve total visits (five, three, one, two, and one visits in years 1-5, respectively) for American Joint Committee on Cancer/International Union Against Cancer T1a stage patients, 13 total visits (five, four, one, two, and one visits in years 1-5, respectively) for T1b stage patients, 15 total visits (eight, three, three, zero, and one visits in years 1-5, respectively) for T2 stage patients, and 15 total visits (eight, four, one, one, and one visits in years 1-5, respectively) for T3 stage patients were advocated. The detailed follow-up arrangements were available to the public through an interactive website (https://sysuccfyz.shinyapps.io/RiskBasedFollowUp/). This risk-based surveillance strategy was demonstrated to detect relapse earlier and reduce the total number of follow-ups without compromising on survival. Based on the strategy and methodology of the authors, surgeons or patients could choose more intensive or flexible schedules depending on the requirements and economic conditions. A risk-based post-resection follow-up strategy was established by random survival forest model using a larger hepatocellular carcinoma population The strategy was demonstrated to detect tumor relapse earlier and reduce the total number of follow-ups without compromising on survival Our strategy and methodology could be widely applied by other surgeons and patients.

Evolution of breast cancer incidence in young women in a French registry from 1990 to 2018: Towards a change in screening strategy?

BackgroundThe worldwide incidence of invasive breast cancer in women is increasing according to several studies. This increase in incidence seems to be higher in young women (< 40 years). However, the reasons for this trend are poorly understood. This article aims to provide the most recent estimates of this trend and assess whether there is indeed an increase in the incidence of breast cancer among young women to strengthen prevention campaigns.MethodsWe collected data from the Isere cancer registry in France of all invasive breast cancers from January 1990 to December 2018. The standardized incidence rate was calculated for four age groups (< 40 years, 40–49 years, 50–74 years, ≥ 75 years) for this period. The 10-year relative survival was evaluated for each age group age for two periods (1990–1999 and 2000–2008). From 2011 to 2013, we analyzed the incidence and 5-year relative survival by tumor subtype (triple negative, luminal, HER2 amplified) for each age group.ResultsA total of 23,703 cases were selected, including 1343 young women (< 40 years). The incidence of invasive breast cancer increased annually by 0,8% (95% CI 0,7; 1) in all age groups combined from 1990 to 2018. The highest incidence increase is found among young women, by 2,1% annually (95% CI 1,3; 2,8). Regarding tumor subtypes from 2011 to 2018, the incidence of triple negatives increases higher in young women (+ 1,4% by year, 95% CI − 8,2; 11) and those over 75 years (+ 4% by year, 95% CI − 5,1; 13,2), but the results are not statistically significant. 10-year relative survival in young women increased from 74,6% (95% CI 69,6; 78,9) to 78,3%(95% CI 73,7; 82,1) between 1990–1999 and 2000–2008, respectively. Five-year relative survival is better in young women among triple negative and HER2 amplified.ConclusionOur study confirms the current trend of increasing the incidence of breast cancer in young women, associated with improved survival very likely attributable to earlier diagnosis due to increased awareness, and improvements in treatment. A better individualized risk-based screening strategy is needed for these patients. Additional studies will be needed to more accurately assess the risk of developing breast cancer and improve diagnostic performance.