Abstract 144: Residual Cardiovascular Risk After Cardiac Rehabilitation

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Divya Ratan Verma ◽  
Shirely Noon ◽  
Taylor Rose ◽  
Dawn Young ◽  
Lillian Khor
Keyword(s):  
Risk Assessment ◽  
Cardiac Rehabilitation ◽  
Patient Characteristics ◽  
Residual Risk ◽  
Risk Scores ◽  
Central Adiposity ◽  
Risk Calculator ◽  
Residual Cardiovascular Risk ◽  
Framingham Risk ◽  
Ascvd Risk

Background: CAD is the leading cause of death and disability in US. Cardiac rehabilitation (CR) program is an important secondary-prevention intervention to reduce mortality, cardiovascular (CV) events, & disability. At start of CR program, patients undergo extensive risk assessment to guide risk reduction goals. However, the residual risk at CR completion is not well studied. We sought to investigate the residual modifiable risk factors of patients completing CR Methods: We retrospectively reviewed our center’s data on consecutive patients between October 2012 and November 2013 who were entered into the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) registry and identified those who completed the CR program. We calculated their residual risk using the newly released ACC/AHA’s ‘Pooled Cohort Equations CV Risk Calculator’ (ACC/AHA risk) and Framingham risk (FR) calculator Results: Out of 128 consecutive CR participants, 44 (34%) completed the program. Patient characteristics and risk assessment are summarized in table 1. As per AACVPR risk stratification algorithm, 37 (84%) of patients were intermediate to high risk. Compared to the start, at completion of CR program, there was a significant improvement in 6-minute walk distance (365±107 vs 484± 137, p<0.001), favorable reduction in total cholesterol, LDL-C, non-HDL-C (p<0.001) and metabolic syndrome (p=0.02). At time of completion, calculated 10 year CV risk using ACC/AHA risk calculator was still elevated (14±10%), while 64% of patients had elevated risk≥7.5% (mean 19.3±9%). FR estimation was low (9±4%). The two risk scores showed moderate correlation (Pearson’s r=0.6, p<0.001), but the ACC/AHA risk was significantly higher than the FR estimation (p<0.001). In multivariate linear regression model, waist circumference (WC) at discharge was significant modifiable independent predictor of ASCVD risk, while systolic BP showed a trend towards significance Conclusion: Successful completion of CR program is associated with improvement in CV risk profile. However, the residual CV risk remains elevated at time of CR completion and is driven by WC & systolic BP. Elevated WC from central adiposity is the main residual atherogenic CV risk factor post CR completion. Further research on significant WC reduction during CR is needed

Correlation of perioperative risk scores with hospital costs in neurosurgical patients

2020 ◽  
Vol 132 (3) ◽  
pp. 818-824
Author(s):  
Sasha Vaziri ◽  
Joseph M. Abbatematteo ◽  
Max S. Fleisher ◽  
Alexander B. Dru ◽  
Dennis T. Lockney ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Hospital Costs ◽  
Patient Characteristics ◽  
Risk Scores ◽  
Site Infection ◽  
Risk Calculator ◽  
Surgical Risk ◽  
Acs Nsqip ◽  
Neurosurgical Patients ◽  
Predicted Risk

OBJECTIVEThe American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) online surgical risk calculator uses inherent patient characteristics to provide predictive risk scores for adverse postoperative events. The purpose of this study was to determine if predicted perioperative risk scores correlate with actual hospital costs.METHODSA single-center retrospective review of 1005 neurosurgical patients treated between September 1, 2011, and December 31, 2014, was performed. Individual patient characteristics were entered into the NSQIP calculator. Predicted risk scores were compared with actual in-hospital costs obtained from a billing database. Correlational statistics were used to determine if patients with higher risk scores were associated with increased in-hospital costs.RESULTSThe Pearson correlation coefficient (R) was used to assess the correlation between 11 types of predicted complication risk scores and 5 types of encounter costs from 1005 health encounters involving neurosurgical procedures. Risk scores in categories such as any complication, serious complication, pneumonia, cardiac complication, surgical site infection, urinary tract infection, venous thromboembolism, renal failure, return to operating room, death, and discharge to nursing home or rehabilitation facility were obtained. Patients with higher predicted risk scores in all measures except surgical site infection were found to have a statistically significant association with increased actual in-hospital costs (p < 0.0005).CONCLUSIONSPrevious work has demonstrated that the ACS NSQIP surgical risk calculator can accurately predict mortality after neurosurgery but is poorly predictive of other potential adverse events and clinical outcomes. However, this study demonstrates that predicted high-risk patients identified by the ACS NSQIP surgical risk calculator have a statistically significant moderate correlation to increased actual in-hospital costs. The NSQIP calculator may not accurately predict the occurrence of surgical complications (as demonstrated previously), but future iterations of the ACS universal risk calculator may be effective in predicting actual in-hospital costs, which could be advantageous in the current value-based healthcare environment.

scholarly journals Residual Cardiovascular Risk at Low LDL: Remnants, Lipoprotein(a), and Inflammation

2020 ◽  
Author(s):  
Ron C Hoogeveen ◽  
Christie M Ballantyne
Keyword(s):  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Residual Risk ◽  
Atherosclerotic Cardiovascular Disease ◽  
C Reactive Protein ◽  
Residual Cardiovascular Risk ◽  
Lipoprotein A ◽  
Reactive Protein ◽  
Increased Risk ◽  
Ascvd Risk

Abstract Background Current guidelines target low-density lipoprotein cholesterol (LDL-C) concentrations to reduce atherosclerotic cardiovascular disease (ASCVD) risk, and yet clinical trials demonstrate persistent residual ASCVD risk despite aggressive LDL-C lowering. Content Non–LDL-C lipid parameters, most notably triglycerides, triglyceride-rich lipoproteins (TGRLs), and lipoprotein(a), and C-reactive protein as a measure of inflammation are increasingly recognized as associated with residual risk after LDL-C lowering. Eicosapentaenoic acid in statin-treated patients with high triglycerides reduced both triglycerides and ASCVD events. Reducing TGRLs is believed to have beneficial effects on inflammation and atherosclerosis. High lipoprotein(a) concentrations increase ASCVD risk even in individuals with LDL-C &lt; 70 mg/dL. Although statins do not generally lower lipoprotein(a), proprotein convertase subtilisin/kexin type 9 inhibitors reduce lipoprotein(a) and cardiovascular outcomes, and newer approaches are in development. Persistent increases in C-reactive protein after intensive lipid therapy have been consistently associated with increased risk for ASCVD events. Summary We review the evidence that biochemical assays to measure TGRLs, lipoprotein(a), and C-reactive protein are associated with residual risk in patients treated to low concentrations of LDL-C. Growing evidence supports a causal role for TGRLs, lipoprotein(a), and inflammation in ASCVD; novel therapies that target TGRLs, lipoprotein(a), and inflammation are in development to reduce residual ASCVD risk.

scholarly journals Enhanced Risk Assessment in Asymptomatic Individuals With Exercise Testing and Framingham Risk Scores

Circulation ◽  
2005 ◽  
Vol 112 (11) ◽  
pp. 1566-1572 ◽  
Author(s):  
Samia Mora ◽  
Rita F. Redberg ◽  
A. Richey Sharrett ◽  
Roger S. Blumenthal
Keyword(s):  
Risk Assessment ◽  
Exercise Testing ◽  
Risk Scores ◽  
Framingham Risk ◽  
Asymptomatic Individuals

scholarly journals ASSESSMENT OF 10-YEAR RISK OF DEVELOPING A MAJOR CARDIOVASCULAR EVENT IN PATIENTS ATTENDING A HOSPITAL FOR THE TREATMENT OF OTHER DISORDERS

2020 ◽  
pp. 74-78
Author(s):  
M. MAHIMA SWAROOPA ◽  
REDDY PRAVEEN ◽  
S. K. LAL SAHEB ◽  
S. K. SAI RINNISHA ◽  
P. SARANYA ◽  
...  
Keyword(s):  
High Risk ◽  
Low Risk ◽  
Risk Scores ◽  
Intermediate Risk ◽  
Total Risk ◽  
Cvd Risk ◽  
Framingham Risk ◽  
Risk Scoring ◽  
Ascvd Risk ◽  
Risk Estimator

Objective: To assess the individual’s predicted risk of developing a CVD event in 10 y using risk scores among persons with other disorders/diseases. Methods: This is a cross-sectional observational study conducted for a period of 6 mo among 283 subjects. Total risk was estimated individually by using Framingham Risk Scoring Algorithm and ASCVD risk estimator. Results: According to Framingham Risk score the prevalence of low risk (<10%) identified as 67.84% (192), followed by intermediate risk (10%-19%), 19.08% (54), and high risk (≥20%) 13.07% (37). By using ASCVD Risk estimator, risk has reported in our study population was low risk (<5%) is 48.76% (138), borderline risk (5-7.4%) is 13.07% (37), intermediate risk (7.5-19.9%) is about 25.09% (71), high risk (>20%) is about 13.07% (37). Conclusion: In this study burden of CVD risk was relatively low, which was estimated by both the Framingham scale and ASCVD Risk estimator. Risk scoring of individuals helps us to identify the patients at high risk of CV diseases and also helps in providing management strategies.

scholarly journals Cardiovascular disease risk assessment among patients attending two cardiac clinics in the Ashanti Region of Ghana

2020 ◽  
Vol 54 (3) ◽  
pp. 140-145
Author(s):  
Waindim Nyiambam ◽  
Augustina Sylverken ◽  
Isaac Owusu ◽  
Kwame Buabeng ◽  
Fred Boateng ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Risk Assessment ◽  
High Risk ◽  
Risk Score ◽  
Framingham Risk Score ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Scores ◽  
Ashanti Region ◽  
Framingham Risk

Background: Cardiovascular disease (CVD) is a major cause of morbidity and hypertension is the single most important modifiable risk. Assessment of an individual’s “total” predicted risk of developing a CVD event in 5- or 10-years using risk scores has been identified as an accurate measure of CVD risk. Using the latest Framingham risk score we assessed the risk among patients attending two cardiac clinics in Kumasi.Methods: We conducted a hospital-based cross-sectional study among 441 patients attending two cardiac clinics in Kumasi, the Ashanti region of Ghana. Hospital records were reviewed and information on demography, social history and laboratory results for the lipid profile tests were extracted.Results: The prevalence of low, medium and high risk were 41.5%, 28.1% and 30.4% respectively. More men were at high risk compared to females (36.0% vs 23.9%, p=0.003). The risk score showed good discrimination for cardiovascular risk stratification with an overall area under the curve of 0.95; 0.97 and 0.94 for males and females respectively. The sensitivity and specificity of the Framingham risk score were 89.5% and 86.3%, respectively.Conclusion: Majority of our study participants were at moderate to high risk with men being the most affected. The Framingham risk score proved to be a useful tool in predicting the 10-year total cardiovascular disease risk.Keywords: cardiovascular diseases, hypertension, Kumasi, total risk, Framingham risk scoreFunding: Not indicated

Risk Assessment of Core Banking System Replacement based on ISACA Framework

2014 ◽  
Vol 1 (1) ◽  
pp. 35-45
Author(s):  
Fenty Simanjuntak ◽  
Bobby Suryajaya
Keyword(s):  
Risk Assessment ◽  
Banking System ◽  
Residual Risk ◽  
Project Teams ◽  
Third Party ◽  
Project Delivery ◽  
Business Growth ◽  
Regulatory Requirements ◽  
The Core ◽  
Medium Risk

Many banks are looking for a better core banking system to support their business growth with a more efficient and flexible core banking system to improve their sales and services in the competitive market and to fulfill regulatory requirements. The decision of replacing the legacy core banking system is difficult due to the high IT investment cost required for banks because they are also trying to cut costs. But maintaining the legacy system is costly in terms of upgrade. Changing the core banking system is also a difficult process and increases risks. To have a successful Core Banking System implementation, risk assessment is required to be performed prior to starting any activities. The assessment can help project teams to identify the risks and then to mitigate the risks as part of the plan. In this research the Core Banking System replacement risks were assessed based on ISACA Framework for IT Risk. Fourteen risk scenarios related to Core Banking System Replacement were identified. The high and medium rated inherent risks can become medium and low residual risk after assessment by putting the relevant control in place. The result proves that by adding mitigation plan it will help to mitigate the Residual Risk to become low risk. There are still three residual risk which categorized as medium risk and should be further mitigated they are Software Implementation, Project Delivery and Selection/Performance of Third Party Suppliers. It is also found that COBIT 5 has considered some specific process capabilities that can be used to improve the processes to mitigate the medium risks.

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 &gt;400,000 participants were used as training set and &gt;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 &gt;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

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