Construction of a Multiattribute Risk Function and its Properties for Assessing Hazardous Events

1989 ◽  
pp. 400-409
Author(s):  
Fumiko Seo
Keyword(s):  
Risk Function

scholarly journals DNA methylation and gene expression integration in cardiovascular disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Guillermo Palou-Márquez ◽  
Isaac Subirana ◽  
Lara Nonell ◽  
Alba Fernández-Sanlés ◽  
Roberto Elosua
Keyword(s):  
Gene Expression ◽  
Cardiovascular Disease ◽  
Dna Methylation ◽  
Cardiovascular Diseases ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Function ◽  
Predictive Biomarkers ◽  
Independent Study ◽  
Cell Type

Abstract Background The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. Results Four independent latent factors (9, 19, 21—only in women—and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. Conclusions Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.

Advanced cardiometabolic & inflammatory markers for prediction of cardiovascular disease and cancer

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Radenkovic ◽  
S.C Chawla ◽  
G Botta ◽  
A Boli ◽  
M.B Banach ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Risk Score ◽  
Cancer Incidence ◽  
Risk Function ◽  
Patient Characteristics ◽  
Risk Scores ◽  
Uk Biobank ◽  
Training Set ◽  
All Cause Mortality ◽  
The Uk

Abstract   The two leading causes of mortality worldwide are cardiovascular disease (CVD) and cancer. The annual total cost of CVD and cancer is an estimated $844.4 billion in the US and is projected to double by 2030. Thus, there has been an increased shift to preventive medicine to improve health outcomes and development of risk scores, which allow early identification of individuals at risk to target personalised interventions and prevent disease. Our aim was to define a Risk Score R(x) which, given the baseline characteristics of a given individual, outputs the relative risk for composite CVD, cancer incidence and all-cause mortality. A non-linear model was used to calculate risk scores based on the participants of the UK Biobank (= 502548). The model used parameters including patient characteristics (age, sex, ethnicity), baseline conditions, lifestyle factors of diet and physical activity, blood pressure, metabolic markers and advanced lipid variables, including ApoA and ApoB and lipoprotein(a), as input. The risk score was defined by normalising the risk function by a fixed value, the average risk of the training set. To fit the non-linear model >400,000 participants were used as training set and >45,000 participants were used as test set for validation. The exponent of risk function was represented as a multilayer neural network. This allowed capturing interdependent behaviour of covariates, training a single model for all outcomes, and preserving heterogeneity of the groups, which is in contrast to CoxPH models which are traditionally used in risk scores and require homogeneous groups. The model was trained over 60 epochs and predictive performance was determined by the C-index with standard errors and confidence intervals estimated with bootstrap sampling. By inputing the variables described, one can obtain personalised hazard ratios for 3 major outcomes of CVD, cancer and all-cause mortality. Therefore, an individual with a risk Score of e.g. 1.5, at any time he/she has 50% more chances than average of experiencing the corresponding event. The proposed model showed the following discrimination, for risk of CVD (C-index = 0.8006), cancer incidence (C-index = 0.6907), and all-cause mortality (C-index = 0.7770) on the validation set. The CVD model is particularly strong (C-index >0.8) and is an improvement on a previous CVD risk prediction model also based on classical risk factors with total cholesterol and HDL-c on the UK Biobank data (C-index = 0.7444) published last year (Welsh et al. 2019). Unlike classically-used CoxPH models, our model considers correlation of variables as shown by the table of the values of correlation in Figure 1. This is an accurate model that is based on the most comprehensive set of patient characteristics and biomarkers, allowing clinicians to identify multiple targets for improvement and practice active preventive cardiology in the era of precision medicine. Figure 1. Correlation of variables in the R(x) Funding Acknowledgement Type of funding source: None

scholarly journals Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6953
Author(s):  
Yixing Du ◽  
Zhijian Hu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Short Term Memory ◽  
Risk Function ◽  
Stability Margin ◽  
Risk Level ◽  
Stability Assessment ◽  
Lstm Network ◽  
Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

scholarly journals Trends and factors associated with modification or discontinuation of the initial antiretroviral regimen during the first year of treatment in the Turkish HIV-TR Cohort, 2011–2017

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Volkan Korten ◽  
◽  
Deniz Gökengin ◽  
Gülhan Eren ◽  
Taner Yıldırmak ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Virologic Failure ◽  
Risk Function ◽  
Transcriptase Inhibitor ◽  
Cox Proportional Hazards ◽  
Antiretroviral Drugs ◽  
Competing Risk ◽  
First Year ◽  
Strand Transfer ◽  
Factors Associated

Abstract Background There is limited evidence on the modification or stopping of antiretroviral therapy (ART) regimens, including novel antiretroviral drugs. The aim of this study was to evaluate the discontinuation of first ART before and after the availability of better tolerated and less complex regimens by comparing the frequency, reasons and associations with patient characteristics. Methods A total of 3019 ART-naive patients registered in the HIV-TR cohort who started ART between Jan 2011 and Feb 2017 were studied. Only the first modification within the first year of treatment for each patient was included in the analyses. Reasons were classified as listed in the coded form in the web-based database. Cumulative incidences were analysed using competing risk function and factors associated with discontinuation of the ART regimen were examined using Cox proportional hazards models and Fine-Gray competing risk regression models. Results The initial ART regimen was discontinued in 351 out of 3019 eligible patients (11.6%) within the first year. The main reason for discontinuation was intolerance/toxicity (45.0%), followed by treatment simplification (9.7%), patient willingness (7.4%), poor compliance (7.1%), prevention of future toxicities (6.0%), virologic failure (5.4%), and provider preference (5.4%). Non-nucleoside reverse transcriptase inhibitor (NNRTI)-based (aHR = 4.4, [95% CI 3.0–6.4]; p < 0.0001) or protease inhibitor (PI)-based regimens (aHR = 4.3, [95% CI 3.1–6.0]; p < 0.0001) relative to integrase strand transfer inhibitor (InSTI)-based regimens were significantly associated with ART discontinuation. ART initiated at a later period (2015-Feb 2017) (aHR = 0.6, [95% CI 0.4–0.9]; p < 0.0001) was less likely to be discontinued. A lower rate of treatment discontinuation for intolerance/toxicity was observed with InSTI-based regimens (2.0%) than with NNRTI- (6.6%) and PI-based regimens (7.5%) (p < 0.001). The percentage of patients who achieved HIV RNA < 200 copies/mL within 12 months of ART initiation was 91% in the ART discontinued group vs. 94% in the continued group (p > 0.05). Conclusion ART discontinuation due to intolerance/toxicity and virologic failure decreased over time. InSTI-based regimens were less likely to be discontinued than PI- and NNRTI-based ART.

scholarly journals sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ian D. Buller ◽  
Derek W. Brown ◽  
Timothy A. Myers ◽  
Rena R. Jones ◽  
Mitchell J. Machiela
Keyword(s):  
Relative Risk ◽  
Statistical Power ◽  
Risk Function ◽  
R Package ◽  
Cancer Epidemiology ◽  
Spatial Cluster ◽  
Cluster Detection ◽  
Detection Technique ◽  
Power Calculation ◽  
Spatial Clusters

Abstract Background Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. Results We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. Conclusions sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.

Passenger Flow Forecast of Urban Rail Transit Based on Support Vector Regression

2013 ◽  
Vol 433-435 ◽  
pp. 612-616 ◽  
Author(s):  
Bin Xia ◽  
Fan Yu Kong ◽  
Song Yuan Xie
Keyword(s):  
Support Vector Regression ◽  
Risk Function ◽  
Urban Rail Transit ◽  
Support Vector ◽  
Rail Transit ◽  
Risk Minimization ◽  
Promising Alternative ◽  
Minimization Principle ◽  
Passenger Flow ◽  
Urban Rail

This study analyses and compares several forecast methods of urban rail transit passenger flow, and indicates the necessity of forecasting short-term passenger flow. Support vector regression is a promising method for the forecast of passenger flow because it uses a risk function consisting of the empirical error and a regularized term which is based on the structural risk minimization principle. In this paper, the prediction model of urban rail transit passenger flow is constructed. Through the comparison with BP neural networks forecast methods, the experimental results show that applying this method in URT passenger flow forecasting is feasible and it provides a promising alternative to passenger flow prediction.

Feature selection based on empirical-risk function to detect lesions in vascular computed tomography

IRBM ◽  
2014 ◽  
Vol 35 (5) ◽  
pp. 244-254 ◽  
Author(s):  
M.A. Zuluaga ◽  
M. Hernández Hoyos ◽  
M. Orkisz
Keyword(s):  
Computed Tomography ◽  
Feature Selection ◽  
Risk Function ◽  
Empirical Risk

scholarly journals Creating an Age-Friendly Community in Affordable Housing Using a Resident Health Risk Assessment Focused on the 4Ms

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 49-49
Author(s):  
Jennifer DeGennaro ◽  
Sherry Pomerantz ◽  
Margaret Avallone ◽  
Melonie Handberry ◽  
Elyse Perweiler
Keyword(s):  
Risk Assessment ◽  
Health Risk ◽  
Health Risk Assessment ◽  
Affordable Housing ◽  
Risk Function ◽  
Well Being ◽  
Screening Tools ◽  
Care Plan ◽  
Care Plans

Abstract The NJGWEP team in partnership with Fair Share Housing/Northgate II (NGII), an affordable housing complex in Camden, NJ, employed an iterative quality improvement process to collaboratively develop a Resident Health Risk Assessment (RHRA) to meet the needs of the housing facility and incorporate the essential elements of the 4Ms framework (Mentation, Medication, Mobility, and What Matters). Using the RHRA, NG II social services staff and Rutgers School of Nursing (RSoN) students were trained to collect health information and administer several evidence-based screening tools (i.e., MiniCog, TUG, PHQ-2). A final element of the RHRA still in development is the documentation process of referral and follow-up based on personalized care plans. Since July 2019, 43 RHRAs have been completed (60% female, mean age 66, age range=43 to 88). Almost all residents (94%) have at least 1 chronic condition (HTN, DM, COPD, CHF), although only 26% have an advance care plan. Most (81%) were screened for future fall risk; function (ADLs/IADLs) was assessed for all (100%). Every resident who was able or did not refuse (88%) was screened for cognitive impairment. Just 7% were taking a high-risk medication (i.e., an opioid or benzodiazepine). The NJGWEP team has initiated an age-friendly community at NGII by providing education on geriatric-focused topics and implementing the 4Ms-focused RHRA to detect issues impacting the resident’s well-being. Establishing a follow-up process to track referrals to available resources will enable NGII to allow residents to age in place with appropriate supports.

Abstract MP019: Should Global Cardiovascular Risk Guide Treatment of Stage One Hypertension? A Cost-effectiveness Analysis

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Andrew Moran ◽  
Petra Rasmussen ◽  
Rachel Zhao ◽  
Pamela G Coxson ◽  
David Guzman ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Cost Effectiveness ◽  
Cost Saving ◽  
Risk Function ◽  
Annual Number ◽  
Base Case ◽  
Cvd Risk ◽  
Policy Model ◽  
Life Years ◽  
Stage 1

Introduction: Current U.S. hypertension guidelines base treatment on clinic blood pressure (BP) alone. International guidelines recommend adding global cardiovascular disease (CVD) risk to guide treatment. We projected incremental effectiveness and costs of treating stage 1 hypertension based on CVD risk assessment. Methods: We used the Coronary Heart Disease (CHD) Policy Model, a validated state-transition simulation of the CVD epidemic in the US, to model CHD and stroke events, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness (ICE) of increasingly aggressive treatment of hypertensive patients. Census and national survey data were used to estimate joint distributions of risk factors by age and sex; the CVD risk function was based on Framingham. We modeled treatment of BP to an approximate target <140/90 mmHg using standard dose medications, including averaged annual drug costs (e.g., $253 for a systolic BP reduction of 11.5 mmHg; $1,036 for reduction of 36.7 mmHg) and monitoring costs (2 or 4 visits/year for stage 1 or 2 plus 1 lab test/year for all). We compared a strategy in which only stage 2 hypertensives (≥160/≥100 mmHg) were treated to increasingly aggressive strategies in which stage 1 hypertensives (140-159/90-99 mmHg) with successively lower global CVD risk (15%, 10%, 5% risk, then all of stage 1) were also treated. Results: Reaching hypertension treatment targets with any policy simulated would prevent between 389,000 and 478,000 CVD events annually ( Table ). Treating all stage 2 and ≥15% CVD risk stage 1 hypertensives would be cost-saving and treating stage 1 with ≥10% or ≥5% CVD risk would incur modest costs. Treating all stage 1 would cost $161,000/QALY more than treating only ≥5% CVD risk. Conclusions: Treatment of low risk stage 1 hypertensives appears to come at high cost and limited added benefit unless treatment costs can be minimized. Using global CVD risk assessment might allow re-allocation of resources toward controlling hypertension in the highest risk patients. Table Simulated CVD outcomes, costs, and cost-effectiveness, 2010-2011, the CHD Policy Model Scenario Annual number hypertensives treated Annual CVD events Annual QALYs (millions) Annual costs (millions, $US) ICER * Base case, no intervention - 2,387,000 127.67 $827,313 reference Treat only stage 2 23,364,180 1,997,000 128.78 $825,264 cost saving Treat stage 2 + stage 1 >=15% CVD risk 30,654,361 1,943,000 128.93 $824,541 cost saving Treat stage 2 + stage 1 >=10% CVD risk 34,947,200 1,928,000 128.97 $824,898 $9,381 Treat stage 2 + stage 1 >= 5% CVD risk 44,321,985 1,913,000 129.02 $826,433 $28,931 Treat stage 2 + all stage 1 50,863,390 1,909,000 129.04 $828,290 $160,630 *ICER = difference in cost/difference in QALY in comparison with the next less effective strategy

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