A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

Large ships are typically with large inertia and longtime delay in motion, in prevailing collision avoidance methods, their maneuverability is generally neglected, there could be a dangerous situation if the system fails to control the ship course as ordered in a timely manner. This paper proposes a coordination system which consists of two algorithms for avoiding risk and then returning to scheduled waypoint. The avoiding risk algorithm are based on VO (velocity obstacle) method, the returning algorithm is derived from LOS (light of sight) guidance. For better performance, the ship model for simulation is a nonlinear Norrbin Model, with the controller improved by CGSA (closed loop gain shaping algorithm) method from traditional PID control, COLREGS (Convention on the International Regulations for Preventing Collisions at Sea) constrains are considered. To test the effectiveness of the proposed system, a series of complex scenarios including Imazu problem are applied.

Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study

Objectives To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination. Design Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries. Settings Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021. Main outcome measures Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices. Results Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down’s syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson’s disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%. Conclusion This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination.

Predictive Modeling for Adverse Events and Risk Stratification Programs for People Receiving Cancer Treatment

Emergency department visits and hospitalizations are common among people receiving cancer treatment, accounting for a large proportion of spending in oncology care and negatively affecting quality of life. As oncology care shifts toward value- and quality-based payment models, there is a need to develop interventions that can prevent these costly and low-value events among people receiving cancer treatment. Risk stratification programs have the potential to address this need and optimally would consist of three components: (1) a risk stratification algorithm that accurately identifies patients with modifiable risk(s), (2) intervention(s) that successfully reduce this risk, and (3) the ability to implement the risk algorithm and intervention(s) in an adaptable and sustainable way. Predictive modeling is a common method of risk stratification, and although a number of predictive models have been developed for use in oncology care, they have rarely been tested alongside corresponding interventions or developed with implementation in clinical practice as an explicit consideration. In this article, we review the available published predictive models for treatment-related toxicity or acute care events among people receiving cancer treatment and highlight challenges faced when attempting to use these models in practice. To move the field of risk-stratified oncology care forward, we argue that it is critical to evaluate predictive models alongside targeted interventions that address modifiable risks and to demonstrate that these two key components can be implemented within clinical practice to avoid unplanned acute care events among people receiving cancer treatment.

496Higher predicted 10-year risk for cardiovascular disease in primary care consulters for osteoarthritis

Background Osteoarthritis (OA) has a major impact on the global burden of disease and is associated with poorer cardiovascular disease (CVD) outcomes. However it’s unclear whether people with OA have higher long-term (10-year) CVD risk compared to people without OA. This study uses electronic health record (EHR) data from a national representative database to calculate the Framingham-score (a gender-specific risk algorithm) to test the hypothesis that OA consulters have higher long-term CVD risk than non-OA consulters. Methods 205,368 incident OA consulters with 1:1 age, gender, and practice-matched non-OA controls extracted from Clinical Practice Research Datalink (CPRD) were included. All predictors extracted from EHRs within three years prior to the index consultation were used to fit the Framingham-score. Absolute difference (AR) and relative rate ratio (RR) in the proportion of high-risk (predicted-risk ≥20%) individuals between OA and controls were estimated using Poisson regression. Results The proportion of high-risk individuals was higher in consulters for OA (5.69 (95% confidence interval (CI): 5.59-5.79)%) cf. 4.37 (4.28-4.46)%). The adjusted AR and RR was (1.94 (95%CI: 1.78-2.10) %) and (1.30 (1.27-1.34)), respectively. Conclusions This study confirms the expected higher predicted 10-year CVD risk in OA consulters compared to controls. This suggests that OA consulters could be a target group for more proactive interventions to prevent CVD events. Key messages OA consulters have a higher CVD risk predicted by Framingham-score calculated using EHRs than controls; OA consulters are a high risk group that can be considered as a target group for proactive prevention interventions for CVD.

Mapping Users’ Experience of a Family History and Genetic Risk Algorithm Tool in Primary Care

<b><i>Introduction:</i></b> The development of a family history (FH) questionnaire (FHQ) provides an insight into a patient’s familiarity of a trait and helps to identify individuals at increased risk of disease. A critical aspect of developing a new tool is exploring users’ experience. <b><i>Objective:</i></b> The objective of this study was to examine users’ experience, obstacles and challenges, and their views and concerns in the applicability of a new tool for determining genetic risk in Slovenia’s primary care. <b><i>Methods:</i></b> We used a qualitative approach. The participants completed a risk assessment software questionnaire that calculates users’ likelihood of developing familial diseases. Audio-taped semi-structured telephone interviews were conducted to evaluate their experience. There were 21 participants, and analyses using the constant comparative method were employed. <b><i>Results:</i></b> We identified 3 main themes: obstacles/key issues, suggestions for improvements, and coping. The participants were poorly satisfied with the clarity of instructions, technical usability problems, and issues with the entry of relatives’ data. They expressed satisfaction with some of the characteristics of the FHQ (e.g., straightforward and friendly format, easy entry, and comprehension). They suggested simpler language, that the disease risk should be targeted toward the disease, that the FHQ should include patient-specific recommendations, and that it should be part of the electronic medical records. When discussing what would they do with the results of the FHQ, the participants used different coping strategies: active (e.g., seeking information) or passive (e.g., avoidance). <b><i>Discussion/Conclusion:</i></b> User experience was shown to be a synthesis of obstacles, overcoming them with suggestions for improvements, and exploration of various coping mechanisms that may emerge from dealing with the stressor of “being at risk.”

Hair toxicology identifies greater substance use than self-report in high-risk 9-13 year-olds in the ABCD Study

Aim: A key aim of the Adolescent Brain Cognitive Development (ABCD) Study is to document substance use onset, patterns, and sequelae across adolescent development. However, substance use misreporting obscures accurate drug use characterization. Hair toxicology tests provide objective historical substance use data, but are rarely investigated in youth. Here, we compare objective hair toxicology results with self-reported substance use in youth. Methods: A literature-based substance use risk algorithm identified 696 ABCD Study participants for hair sample collections between baseline and 2-year follow-up (spanning ages 9-13) for laboratory analysis. Chi-square and t-tests assessed differences between participants demographics, positive and negative hair tests, risk algorithm scores, and self-reported substance use. Results: Hair testing confirmed that 17% of at-risk 9-13 year-olds had evidence of past 3-month use of one (n=99), two (n=17), three (n=3), or four (n=2) drug classes. After considering prescribed medication use, 11% had a positive test incongruent with self- or parent-report. No participant with a positive result self-reported substance use consistent with their toxicology results. Participants with positive tests under-reported use (p<.001), reported less sipping of alcohol (p<.001), and scored higher on the risk algorithm (p<.001) than those with negative hair toxicology. Conclusions: An alarming 11% of tested samples in at-risk 9-13 year-olds were positive for at least one unreported substance, suggesting underreporting in this population when participating in a national healthy development study. The degree of underreporting cannot yet be calculated, as at-risk samples were prioritized for assays. Expanded toxicology testing is key to characterize substance use in youth.

Potential of polygenic risk scores for improving population estimates of women’s breast cancer genetic risks

Purpose Breast cancer risk has conventionally been assessed using family history (FH) and rare high/moderate penetrance pathogenic variants (PVs), notably in BRCA1/2, and more recently PALB2, CHEK2, and ATM. In addition to these PVs, it is now possible to use increasingly predictive polygenic risk scores (PRS) as well. The comparative population-level predictive capability of these three different indicators of genetic risk for risk stratification is, however, unknown. Methods The Canadian heritable breast cancer risk distribution was estimated using a novel genetic mixing model (GMM). A realistically representative sample of women was synthesized based on empirically observed demographic patterns for appropriately correlated family history, inheritance of rare PVs, PRS, and residual risk from an unknown polygenotype. Risk assessment was simulated using the BOADICEA risk algorithm for 10-year absolute breast cancer incidence, and compared to heritable risks as if the overall polygene, including its measured PRS component, and PV risks were fully known. Results Generally, the PRS was most predictive for identifying women at high risk, while family history was the weakest. Only the PRS identified any women at low risk of breast cancer. Conclusion PRS information would be the most important advance in enabling effective risk stratification for population-wide breast cancer screening.

RISK FACTORS FOR FAILURE FOLLOWING ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION IN A PEDIATRIC POPULATION: A NOVEL PREDICTION ALGORITHM

Background: Pediatric anterior cruciate ligament (ACL) reconstruction is becoming increasingly common, however, there is limited literature on the risk factors for failure in this demographic. Hypothesis/Purpose: In the present study we sought to: 1. To determine the rate of pediatric ACL reconstruction (ACLR) failure requiring revision surgery in a nationally representative sample. 2. To determine the associated patient/injury-specific risk factors for ACLR failure. 3. To examine the differences in the rate and risks of failure between pediatric and adult patients. Methods: Patient records were drawn from Humana individual health plans and Medicare medical records. Adult and pediatric patients who underwent primary ACLR and subsequent reoperation for either ACL revision surgery or a revision meniscal procedure between 2011-2016 were identified. Multivariate regression was used to determine the significant risk factors for ACL revision and overall reoperation rates in pediatric and adult patients. A risk algorithm was developed to predict the risk of ACL revision following pediatric ACL reconstruction. Results: Pediatric patients were significantly more likely to require ACL revision within one year (OR=1.97, 95%CI 1.57-2.45, p<0.0001) and five years (OR=3.22, 95%CI 2.77-3.72, p<0.0001) following their index ACLR compared to adults. Survivorship of the index ACL procedure was significantly decreased in pediatric patients (log-rank test p<0.0001) (Figure 1). Pediatric patients were also at higher risk of sustaining a contralateral ACL tear compared to adults (5.9% vs. 1.4%, respectively, p<0.0001). Meniscus injury was a risk factor for overall re-operation (OR=2.18, 95%CI 1.67-2.89 p<0.0001) as well as ACL revision (OR=2.28, 95%CI=1.66-3.21, p<0.0001) in the pediatric cohort. This increased risk was sustained despite the type of meniscal tear intervention, with patients undergoing concurrent meniscal repair (OR=1.84, 95%CI 1.43-2.38, p<0.0001) or meniscectomy (OR=2.20, 95%CI 1.72-2.82, p<0.0001) having a higher likelihood of requiring a revision ACLR. Concomitant MCL injury but not LCL injury was a risk factor for ACL revision in this cohort (OR=1.70, 95%CI 1.31-2.19, p<0.0001). Male sex and (OR=0.78, 95%CI 0.63-0.96, p=0.0204) and being >14 years old (OR=0.62, 95%CI 0.45-0.86, p=0.0035) was associated with a decreased risk of overall reoperation. The risk algorithm demonstrated the highest probability for ACLR failure in females, less than 15 years of age, with concomitant meniscus and MCL injury, demonstrating a 36% risk of failure (Table 1). Conclusion: Compared to adults, pediatric patients have an increased likelihood of ACL revision surgery, contralateral ACL tears, and meniscal reoperation within 5 years of an index ACLR. [Table: see text][Figure: see text]

Development and validation of a predictive algorithm for risk of dementia in the community setting

BackgroundMost dementia algorithms are unsuitable for population-level assessment and planning as they are designed for use in the clinical setting. A predictive risk algorithm to estimate 5-year dementia risk in the community setting was developed.MethodsThe Dementia Population Risk Tool (DemPoRT) was derived using Ontario respondents to the Canadian Community Health Survey (survey years 2001 to 2012). Five-year incidence of physician-diagnosed dementia was ascertained by individual linkage to administrative healthcare databases and using a validated case ascertainment definition with follow-up to March 2017. Sex-specific proportional hazards regression models considering competing risk of death were developed using self-reported risk factors including information on socio-demographic characteristics, general and chronic health conditions, health behaviours and physical function.ResultsAmong 75 460 respondents included in the combined derivation and validation cohorts, there were 8448 cases of incident dementia in 348 677 person-years of follow-up (5-year cumulative incidence, men: 0.044, 95% CI: 0.042 to 0.047; women: 0.057, 95% CI: 0.055 to 0.060). The final full models each include 90 df (65 main effects and 25 interactions) and 28 predictors (8 continuous). The DemPoRT algorithm is discriminating (C-statistic in validation data: men 0.83 (95% CI: 0.81 to 0.85); women 0.83 (95% CI: 0.81 to 0.85)) and well-calibrated in a wide range of subgroups including behavioural risk exposure categories, socio-demographic groups and by diabetes and hypertension status.ConclusionsThis algorithm will support the development and evaluation of population-level dementia prevention strategies, support decision-making for population health and can be used by individuals or their clinicians for individual risk assessment.

POS1019 INCONSISTENCY OF THE DEGREE OF CARDIOVASCULAR RISK WHEN ASSESSED USING DIFFERENT INDICES IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS

Background:Evaluation of indicators of cardiovascular risk is one of the main tasks facing a rheumatologist in the tactics of choosing a therapy for patients (pts) with axial spondyloarthritis (axSpA). It is known that pts suffering from axSpA are characterized by a significant increase in cardiovascular risk (CVR). However, there are still no recommendations regulating risk assessment scales in pts with axSpA.Objectives:were to assess the CVR in pts with axSpA and to compare different cardiovascular risk scales in these pts.Methods:The study included 55 pts at the age of 45-65 years with diagnosis of axSpA fulfilling ASAS criteria (2009) from St. Petersburg’ axSpA register. Three indices of cardiovascular risk evaluation (Systematic COronary Risk Evaluation (SCORE) with increasing coefficient 1.5 for inflammatory diseases, Reynolds Risk Score (RRS), and the third modification of QRESEARCH Cardiovascular Risk Algorithm (QRISK3) were calculated. Risk gradation: low risk (<1%), medium (1.0-4.9%), high (5.0-9.9%), very high (> 10%).Results:Mean age of the pts was 45.8±10.3 years; males - 37 (67.3%) pts, HLA-B27 positive – 34 (61.8%); mean disease duration 12.5±8.7 years. Mean value of SCORE was 2.83±1.89%, of RRS – 5.04±3.98%, of QRISK3 – 7.91±4.91%.The gradation of the degree of risk depending on the applied assessment index is presented in Table 1.IndexResultsRisk degreeSCORERRSQRISK3Low21 (38,2%)8 (14,5%)0 (0,0%)Medium26 (47,3%)23 (41,8%)17 (30,9%)High8 (14,5%)17 (30,9%)22 (40,0%)Very high0 (0,0%)7 (12,7%)16 (29,1%)Particular attention is drawn to the 100% discrepancy of low risk values when comparing SCORE and QRISK3. A similar trend persisted when comparing medium, high and very high risk. Thus, the assessment of the risks of 10-year significant cardiovascular events in pts with axSpA using the SCORE index does not coincide with the QRISK3 index data in 87.27% of cases, and with the RRS data - in 58.18% of cases. In 84.3% of cases, the mismatch between the SCORE and RRS indexes was due to the presence of an increased CRP level.Conclusion:When assessing cardiovascular risk in pts with axial spondyloarthritis, a discrepancy was found between the degrees of risk when assessed using different scales. SCORE scores were significantly different from Reynolds’ and QRISK3 scores. These features can be interrelated with a small number of factors assessed when calculating the SCORE, even though there is a correction factor for rheumatic diseases. For pts with axial spondyloarthritis, it is necessary to use additional indicators that influence cardiovascular risk, such as CRP.Disclosure of Interests:None declared.