Abstract P070: Long-term Cumulative Blood Pressure Improves CVD Risk Prediction Algorithms

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Norrina B Allen ◽  
Hongyan Ning ◽  
Donald Lloyd-Jones
Keyword(s):  
Blood Pressure ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Estimation ◽  
Incidence Rates ◽  
Cvd Risk ◽  
Prediction Algorithms ◽  
C Statistic

Background: Published risk prediction algorithms only include current BP; however, long-term BP patterns are associated with atherosclerotic (AS)CVD incidence. We tested whether the long-term (5- and 10-year) cumulative blood pressure improves 10 year ASCVD prediction. Methods: This study used the Lifetime Risk Pooling Project (LRPP) including the Framingham, CARDIA and ARIC cohorts. Participants with 15- and 20-year follow-up (5- and 10- years prior to risk calculation and 10 year follow-up), no history of prior CVD, and between the ages of 45 and 65 at the time of risk estimation were included. We calculated 10 year ASCVD risk using the 2013 ACC/AHA 10-year ASCVD Pooled Cohort Equations. Study-specific coefficients were calculated. Differences in the C-statistic, the category-free net reclassification index (NRI) and improved discrimination index (IDI) were examined between the model with baseline as compared to the model with cumulative SBP. Analyses were stratified by gender. Results: Among 11,475 individuals (42.4% male and 12.7% African American), those in the highest tertile of cumulative SBP were older, more likely to be male, and had a higher burden of other CVD risk factors. Overall, 1,487 (13%) participants experienced a CVD event (mean follow-up time was 12 years). ASCVD incidence rates increased with higher tertiles of cumulative SBP from 4 events per 1,000 person-years in the lowest tertile to 8 and 18 in the second and third tertiles, respectively. No significant improvements were seen in the C-statistic when including 5- or 10-year cumulative SBP (see table). However, the replacement cumulative SBP resulted in significant improvements in model reclassification of NRI and IDI with greater improvements for the 10- than 5-year cumulative measure. Conclusions: Measures of cumulative BP can improve the ability of CVD risk prediction models to correctly classify individuals. Additional studies on the inclusion of these measures in future risk prediction algorithms are warranted.

scholarly journals Tumor-necrosis Factor Inhibitors Improve Aortic Stiffness in Patients With Longstanding Rheumatoid Arthritis

2020 ◽  
Author(s):  
Alessandro Giollo ◽  
Giovanni Cioffi ◽  
Federica Ognibeni ◽  
Giovanni Orsolini ◽  
Andrea Dalbeni ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Aortic Stiffness ◽  
Term Treatment ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Long Term Treatment ◽  
Necrosis Factor

Abstract Background. Major cardiovascular disease (CVD) benefits of disease-modifying anti-rheumatic drugs (DMARDs) therapy occur in early RA patients with treat-to-target strategy. However, it is unknown whether long-term DMARDs treatment in established RA could be useful to improve CVD risk profile.Methods. Ultrasound aortic stiffness index (AoSI) has to be considered a proxy outcome measure in established RA patients. We measured AoSI in a group of RA patients on long-term treatment with tumour necrosis factor inhibitors (TNFi) or conventional synthetic DMARDs (csDMARDs). Eligible participants were assessed at baseline and after 12 months; changes in serum lipids, glucose and arterial blood pressure were assessed. All patients were on stable medications during the entire follow-up. Results. We included 107 (64 TNFi and 43 csDMARDs) RA patients. Most patients (74%) were in remission or low disease activity and had some CVD risk factors (45.8% hypertension, 59.8% dyslipidemia, 45.3% smoking). The two groups did not differ significantly for baseline AoSI (5.95±3.73% vs 6.08±4.20%, p=0.867). Follow-up AoSI was significantly increased from baseline in the csDMARDs group (+1.00%; p<0.0001) but not in the TNFi group (+0.15%, p=0.477). Patients on TNFi had significantly lower follow-up AoSI from baseline than the csDMARD group (-1.02%, p<0.001; ANCOVA corrected for baseline AoSI, age and systolic blood pressure). Furthermore, follow-up AoSI was significantly lower in TNFi users with 1-2 or >2 CVD risk factors than in those without. Conclusion. Long-term treatment with TNFi was associated with reduced aortic stiffness in patients with established RA and several CVD risk factors.

scholarly journals Use of Long-term Cumulative Blood Pressure in Cardiovascular Risk Prediction Models

2018 ◽  
Vol 3 (11) ◽  
pp. 1096 ◽  
Author(s):  
Lindsay R. Pool ◽  
Hongyan Ning ◽  
John Wilkins ◽  
Donald M. Lloyd-Jones ◽  
Norrina B. Allen
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Cardiovascular Risk Prediction

scholarly journals P0343RISK PREDICTION MODELS FOR PREDICTING PROGRESSION OF IGA NEPHROLOGY:A VALIDATION STUDY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Jingyuan Xie
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Collaborative Study ◽  
Clinical Parameters ◽  
Full Model ◽  
Risk Prediction Models ◽  
Clinical Model ◽  
C Statistic ◽  
Model C

Abstract Background and Aims Risk prediction models for IgA nephropathy (IgAN) containing clinical variables (clinical model) or clinical plus pathological variables (full model) have been established based on a large international collaborative study recently, but external validation of these models are still required before clinical application. The aim of this study is to externally validate previously reported risk prediction models based on our multi-center IgAN cohort. Method Biopsy-proven IgAN patients with eGFR ≥15 ml/min/1.73 m2 at baseline and a minimum follow-up of 6 months were enrolled. Primary outcome was defined as end-stage kidney disease (ESRD). Cox proportional hazards models were built to validate risk models. R2, Akaike information criterion (AIC) and C statistic were calculated to evaluate model accuracy. Results A total of 2300 IgAN patients with a median follow-up of 30 months were enrolled, and 214(9.3%) ESRD occurred during the follow-up period. The median age was 35(interquartile range, 28-44) years, and 1106 cases (48.1%) were men. Our cohort successfully validated the clinical model and the full model based on C statistic (0.90 and 0.91) and R2 (0.32 and 0.32). Our results showed limited improvement in model performance after adding the Oxford classification parameters to clinical parameters. However, both two models performed better than the model consisting only pathological parameters(C statistic 0.83, R2 0.24). We also validated other risk prediction models, including CLIN model (C statistic 0.90, R2 0.32) and CLINPATH model (C statistic 0.91, R2 0.31) derived from Chinese IgAN patients or CKD model (C statistic 0.90, R2 0.32) derived from Canadian CKD patients. It was found that clinical models based on different combinations of clinical parameters performed similarly. Conclusion In summary, we successfully validated a recently reported IgAN risk model and we found that clinical parameters alone could accurately predict ESRD risk in IgAN patients.

Abstract 15670: Age-related Differences in the Contribution of Systolic Blood Pressure and Biomarkers to Cardiovascular Disease Risk Prediction: The Atherosclerosis Risk in Communities (ARIC) Study

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Mahmoud Al Rifai ◽  
Chiadi E Ndumele ◽  
James A De Lemos ◽  
Caroline Sun ◽  
Ron C Hoogeveen ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Systolic Blood Pressure ◽  
Risk Prediction ◽  
Middle Age ◽  
Disease Risk ◽  
Older Individuals ◽  
Prediction Equations ◽  
Cvd Risk ◽  
C Statistic

I ntroduction: Systolic blood pressure (SBP) is an important component of all cardiovascular disease (CVD) risk prediction equations but its biological variability and impact on estimated risk is a concern. Furthermore, predictive value of SBP may differ in older individuals where traditional risk factors (TRF) are less predictive. Hypothesis: Biomarkers reflecting hypertension-related end organ injury (hsTnT, NT-proBNP, eGFR), improve CVD risk prediction in older but not middle age adults as compared to SBP. Methods: Using data from visits 2 (1990-92) and 5 (2011-13) of ARIC, we developed 3 models- Model 1 included all TRF; Model 2- all TRF except SBP + individual biomarkers and Model 3 all TRF + individual biomarkers. C-statistics were used to assess risk discrimination for coronary heart disease, stroke, heart failure, and CVD. Results: After excluding those with prevalent CVD, there were 12,567 individuals at visit 2 (mean age 57, SD 6 years; 43% men) and 4,508 individuals at visit 5 (mean age 76, SD 5 years; 37% men). Over a median (IQR) follow-up time of 22 (12.4–26.7) years and 6.2 (5.4–6.8) years, the incidence rates of CVD events (per 1000 person-years) were 19.0 and 21.8 at visits 2 and 5, respectively. At visit 2, the model with SBP and biomarkers resulted in the largest improvement in C-statistic and SBP contributed to all models. However, at visit 5, removing SBP from the models with the biomarkers had no impact on C-statistic while the addition of the biomarkers (especially hsTnT and NT-proBNP) significantly improved C-statistics for most outcomes ( Table ). Among the biomarkers eGFR had the least additive value. Conclusions: HsTnT and NT-proBNP significantly improve risk discrimination of CHD, stroke, and HF among middle and older adults, while SBP has value in middle age but not in older age. Biomarkers should be considered in risk prediction equations in older individuals where the value of TRF such as SBP decrease.

Abstract P346: Cohort Identification to Harness the Electronic Health Record for Cardiovascular Disease Prediction

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Sheila M Manemann ◽  
Jennifer St Sauver ◽  
Janet E Olson ◽  
Nicholas B Larson ◽  
Paul Y Takahashi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Electronic Health Record ◽  
Risk Prediction ◽  
Health Record ◽  
Next Generation ◽  
Cvd Risk ◽  
Electronic Health

Background: Current cardiovascular disease (CVD) risk scores are derived from research cohorts and are particularly inaccurate in women, older adults, and those with missing data. To overcome these limitations, we aimed to develop a cohort to capitalize on the depth and breadth of clinical data within electronic health record (EHR) systems in order to develop next-generation sex-specific risk prediction scores for incident CVD. Methods: All individuals 30 years of age or older residing in Olmsted County, Minnesota on 1/1/2006 were identified. We developed and validated algorithms to define a variety of risk factors, thus building a comprehensive risk profile for each patient. Outcomes including myocardial infarction (MI), percutaneous intervention (PCI), coronary artery bypass graft (CABG), and CVD death were ascertained through 9/30/2017. Results: We identified 73,069 individuals without CVD (Table). We retrieved a total of 14,962,762 lab results; 14,534,466 diagnoses; 17,062,601 services/procedures; 1,236,998 outpatient prescriptions; 1,079,065 heart rate measurements; and 1,320,115 blood pressure measurements. The median number of blood pressure and heart rate measurements ascertained per individuals were 11 and 9, respectively. The five most prevalent conditions were: hypertension, hyperlipidemia, arthritis, depression, and cardiac arrhythmias. During follow-up 1,455 MIs, 1,581 PCI, 652 CABG, and 2,161 CVD-related deaths occurred. Conclusions: We developed a cohort with comprehensive risk profiles and follow-up for each patient. Using sophisticated machine learning approaches, this electronic cohort will be utilized to develop next-generation sex-specific CVD risk prediction scores. These approaches will allow us to address several challenges with use of EHR data including the ability to 1) deal with missing values, 2) assess and utilize a large number of variables without over-fitting, 3) allow non-linear relationships, and 4) use time-to-event data.

Preeclampsia and the Ten-Year Risk of Incident Chronic Kidney Disease

2020 ◽  
Vol 10 (3) ◽  
pp. 188-197
Author(s):  
Samira Behboudi-Gandevani ◽  
Mina Amiri ◽  
Maryam Rahmati ◽  
Saber Amanollahi Soudmand ◽  
Fereidoun Azizi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Kidney Disease ◽  
Population Based ◽  
Incidence Rates ◽  
Prior History ◽  
P Value

Background: Although preeclampsia (PE), as an endothelial disorder can lead to renal dysfunction during pregnancy, results of studies focusing on the potential long-term potential effects of PE on renal function are insufficient and those available are controversial. This study investigated the incidence rate and risk of chronic kidney disease (CKD) among women with prior history of PE compared with healthy controls in a long-term population-based study. Methods: This was a prospective population-based cohort study. Subjects were 1,851 eligible women, aged 20–50 years, with at least 1 pregnancy (177 women with prior-PE and 1,674 non-PE controls) selected from among the Tehran-Lipid and Glucose-Study-participants. A pooled-logistic-regression-model and Cox’s-proportional-hazards-models were utilized to estimate the risk of CKD in women of both PE and without PE groups, after further adjustment for confounders. Results: Median and interquartile ranges for follow-up durations of the PE and non-PE groups were 7.78 (5.19–10.40) and 7.32 (4.73–11.00) years, respectively. Total cumulative incidence rates of CKD at the median follow-up time of each group were 35/100,000 (95% CI 25/100,000–50/100,000) and 36/100,000 (95% CI 32/100,000–39/100,000) in PE and non-PE women, respectively (p value = 0.90). Based on pooled-logistic-regression-analysis, OR of CKD progression (adjusted for age, body mass index [BMI], systolic blood pressure [SBP], and diastolic blood pressure [DBP]) for the PE group did not differ, compared to their non-PE counterparts (OR 1.04; p value = 0.80; 95% CI 0.77–1.40). Compared to non-PE women, women with prior PE did not have higher hazard ratios (HRs) of developing CKD in the unadjusted model (unadjusted HR 1.1, 95% CI 0.83–1.69, p = 0.35), results which remained unchanged after adjustment for age, BMI, baseline SBP, and DBP. Conclusion: PE was not found to be a risk factor for CKD. More studies using a prospective cohort design with long-term follow-ups are needed to investigate the relationship between preeclamsia and CKD.

scholarly journals Long-term maintenance rituximab for ANCA-associated vasculitis: relapse and infection prediction models

Rheumatology ◽  
2020 ◽  
Author(s):  
Mark E McClure ◽  
Yajing Zhu ◽  
Rona M Smith ◽  
Seerapani Gopaluni ◽  
Joanna Tieu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Groups ◽  
Risk Of Infection ◽  
Risk Prediction Models ◽  
Anca Associated Vasculitis ◽  
Term Maintenance

Abstract Objectives Following a maintenance course of rituximab (RTX) for ANCA-associated vasculitis (AAV), relapses occur on cessation of therapy, and further dosing is considered. This study aimed to develop relapse and infection risk prediction models to help guide decision making regarding extended RTX maintenance therapy. Methods Patients with a diagnosis of AAV who received 4–8 grams of RTX as maintenance treatment between 2002 and 2018 were included. Both induction and maintenance doses were included; most patients received standard departmental protocol consisting of 2× 1000 mg 2 weeks apart, followed by 1000 mg every 6 months for 2 years. Patients who continued on repeat RTX dosing long-term were excluded. Separate risk prediction models were derived for the outcomes of relapse and infection. Results A total of 147 patients were included in this study with a median follow-up of 63 months [interquartile range (IQR): 34–93]. Relapse: At time of last RTX, the model comprised seven predictors, with a corresponding C-index of 0.54. Discrimination between individuals using this model was not possible; however, discrimination could be achieved by grouping patients into low- and high-risk groups. When the model was applied 12 months post last RTX, the ability to discriminate relapse risk between individuals improved (C-index 0.65), and once again, clear discrimination was observed between patients from low- and high-risk groups. Infection: At time of last RTX, five predictors were retained in the model. The C-index was 0.64 allowing discrimination between low and high risk of infection groups. At 12 months post RTX, the C-index for the model was 0.63. Again, clear separation of patients from two risk groups was observed. Conclusion While our models had insufficient power to discriminate risk between individual patients they were able to assign patients into risk groups for both relapse and infection. The ability to identify risk groups may help in decisions regarding the potential benefit of ongoing RTX treatment. However, we caution the use of these prediction models until prospective multi-centre validation studies have been performed.

scholarly journals Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment

2021 ◽  
Author(s):  
Zhe Xu ◽  
Matthew Arnold ◽  
David Stevens ◽  
Stephen Kaptoge ◽  
Lisa Pennells ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Predictive Performance ◽  
Statin Treatment ◽  
Clinical Practice Research Datalink ◽  
Cvd Risk ◽  
Risk Prediction Models

Abstract Cardiovascular disease (CVD) risk prediction models are used to identify high-risk individuals and guide statin-initiation. However, these models are usually derived from individuals who may initiate statins during follow-up. We present a simple approach to address statin-initiation to predict “statin-naïve” CVD risk. We analyzed primary care data (2004-2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (40-85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin-initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g., numbers-needed-to-screen to prevent one case) against models ignoring statin-initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for versus ignoring statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in numbers-needed-to-screen to prevent one case. In conclusion, incorporating statin effects from trial results into risk prediction models enables statin-naïve CVD risk estimation, provides moderate gains in predictive ability, but had a limited impact on treatment decision-making under current guidelines in this population.

Abstract 14: Examining the Predictive Power and the Impact of Incorporating Hemoglobin A1c into Existing Diabetes Risk Prediction Models among African American Adults in the Jackson Heart Study

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Mary E Lacy ◽  
Gregory Wellenius ◽  
Charles B Eaton ◽  
Eric B Loucks ◽  
Adolfo Correa ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Predictive Power ◽  
Prediction Models ◽  
Model Performance ◽  
Diabetes Risk ◽  
Risk Prediction Models ◽  
C Statistic ◽  
Heart Study ◽  
The Impact

Background: In 2010, the American Diabetes Association (ADA) updated diagnostic criteria for diabetes to include hemoglobin A1c (A1c). However, the appropriateness of these criteria in African Americans (AAs) is unclear as A1c may not reflect glycemic control as accurately in AAs as in whites. Moreover, existing diabetes risk prediction models have been developed in populations composed primarily of whites. Objectives were to (1) examine the predictive power of existing diabetes risk prediction models in the Jackson Heart Study (JHS), a prospective cohort of 5,301 AA adults and (2) explore the impact of incorporating A1c into these models. Methods: We selected 3 widely-used diabetes risk prediction models and examined their ability to predict 5-year diabetes risk among 3,185 JHS participants free of diabetes at baseline and who returned for the 5 year follow-up visit. Incident diabetes was identified at follow-up based on current antidiabetic medications, fasting glucose ≥126 mg/dl or A1c ≥6.5%. We evaluated model performance using model discrimination (C-statistic) and reclassification (net reclassification index (NRI) and integrated discrimination improvement (IDI)). For each of the 3 models, model performance in JHS was evaluated using (1) covariates identified in the original published model and (2) published covariates plus A1c. Results: Of 3,185 participants (mean age 53.7; 64.0% female), 9.8% (n=311) developed diabetes over 5 years of follow-up. Each diabetes prediction model suffered a drop in predictive power when applied to JHS using ADA 2010 criteria (Table 1). The performance of all 3 models improved significantly with the addition of A1c, as evidenced by the increase in C-statistic and improvement in reclassification. Conclusion: Despite evidence that A1c may not accurately reflect glycemic control in AAs as well as in whites, adding A1c to existing diabetes risk prediction models developed in primarily white populations significantly improved 5-year predictive power of all 3 models among AAs in the JHS.

scholarly journals Use of linked electronic health records to evaluate cardiovascular risk prediction models in Ontario, Canada

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Atul Sivaswamy ◽  
Anam Khan ◽  
Karen Tu ◽  
Paymon Azizi ◽  
Dennis Ko ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Administrative Data ◽  
Index Date ◽  
Prediction Models ◽  
Health Records ◽  
Individual Level ◽  
Cardiovascular Risk Prediction

IntroductionElectronic health records (EHR) contain individual-level clinical information not found in traditional administrative databases. As part of the CANHEART-Strategy for Patient Oriented Research (SPOR) initiative, we created a linked EHR-administrative data cohort that enables us to measure the Framingham and ACC/AHA Pooled Cohort cardiovascular risk prediction scores in Ontario, Canada. Objectives and ApproachAn EHR primary care cohort was created using the Electronic Medical Record Administrative data Linked Database (EMRALD) database, which contains the blood pressure and lipid values, weight and height measures, prescriptions and smoking status of up to 350,000 patients in Ontario, Canada. We enriched the lipid information through linkage to the Ontario Laboratory Information System, which is a repository of 90%+ of all lipid tests in Ontario. Individual-level information on co-morbidities, hospitalizations and mortality attributed to cardiovascular causes (e.g. myocardial infarction, stroke, cardiovascular mortality) were obtained through linkage to provincial health administrative and vital statistics databases using CANHEART methodology (www.canheart.ca). ResultsPatients were entered into the cohort between 2008 and 2014 if they had measurements for blood pressure and lipids (total cholesterol and high-density lipoprotein) taken within a year of each other during this accrual window. The earliest such group of values was chosen and determined the individual’s index date. Age, sex, smoking, diabetes and anti-hypertensive treatment status were extracted from EHR or administrative data to calculate the two scores. Patients were excluded if not aged 40-75 on the index date or if they had a history of cardiovascular disease. A cohort of 84,628 Ontario residents (mean age 55.0 years) had the elements required to calculate both scores. Follow-up for outcome events were done through record linkage to the end of 2014, with a mean follow-up of 3.62 years. Conclusion/ImplicationsThe creation of this cohort will allow for the validation of the Framingham and AHA/ACC Pooled Cohort equations in the diverse Ontario population. It would also enable the possible development of a new ‘made-in-Canada’ cardiovascular risk prediction model.

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