Improvement in Cardiovascular Risk Prediction with Electronic Health Records

Pike, Mindy; Decker, Paul A.; Larson, Nicholas B.; Sauver, Jennifer L. St.; Takahashi, Paul Y.; Roger, Véronique L.; Rocca, Walter A.; Miller, Virginia M.; Olson, Janet E.; Pathak, Jyotishman; Bielinski, Suzette J.

doi:10.1007/s12265-016-9687-z

Cited by 42 publications

(29 citation statements)

References 27 publications

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“…It is the second version of the QRISK score, an algorithm for predicting an individual's cardiovascular risk. It was established for the contemporary Northern European population and has been proven to have a high accuracy in predicting major cardiovascular events …”

Section: Methodsmentioning

confidence: 99%

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Association of epistaxis with atherosclerotic cardiovascular disease

2018

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Objectives/Hypothesis To investigate the association between epistaxis and atherosclerotic cardiovascular disease. Study Design Case‐control cohort study. Methods This study included patients from the tertiary‐care ear, nose, and throat department at the University Hospital of Zurich between December 1, 2016 and June 1, 2017. We assessed the cardiovascular risk profiles in a group of 41 patients presenting with epistaxis, and a group of 41 matched controls, focusing on a surrogate parameter for atherosclerosis: the carotid intima‐media thickness (CIMT). Results With a mean of 1.06 mm (standard deviation [SD] = 0.17), CIMT values were on average 26% higher in epistaxis patients than in their controls, with a mean of 0.84 mm (SD = 0.14; P < .001). Occurrence of severe epistaxis was also associated with lower ankle–brachial index values at 0.96 (SD = 0.12) versus 1.05 (SD = 0.17) (P < .001) and significantly higher QRISK2 relative risks (an algorithm for predicting cardiovascular risk) than found in the control group (1.81, SD = 0.97 vs. 1.35, SD = 0.28; P = .028). A binary logistic regression model, adjusted for possible confounders, showed an odds ratio of 2.5 for the occurrence of epistaxis per increase in CIMT of 0.1 mm in the study population (95% confidence interval: 1.56‐4.11; P < .001). Conclusions The occurrence of severe epistaxis was shown to be closely associated with the prevalence of atherosclerotic cardiovascular disease. Accordingly, patients affected by epistaxis should be regarded as at an elevated cardiovascular risk, which indicates the need for appropriate further medical assessment and preventive measures. Level of Evidence 3b Trial registration Clinical trials NCT03092973 Laryngoscope, 129:783–787, 2019

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Section: Methodsmentioning

confidence: 99%

“…It was established for the contemporary Northern European population and has been proven to have a high accuracy in predicting major cardiovascular events. [32][33][34]…”

Section: Data Collectionmentioning

confidence: 99%

Association of epistaxis with atherosclerotic cardiovascular disease

2018

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“…The QRISK algorithms have been validated by ourselves and others in independent groups of patients using UK primary care databases such as QResearch, 4 Clinical Practice Research Datalink (CPRD), 4 The Health Improvement Network (THIN), [5][6][7][8][9] and clinical cohorts [10][11][12] as well as in international populations. 13 14 Their use has been evaluated in observational studies, 15 cost effectiveness evaluations, 16 and clinical trials. 17 18 QRISK2 is now used across England's health service (NHS England) and recommended in the NHS Quality and Outcomes Framework, 19 guidance from the National Institute of Health and Care Excellence, 20 and NHS Health Check.…”

Section: What This Study Addsmentioning

confidence: 99%

Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study

Hippisley‐Cox

Coupland

Brindle

2017

BMJ

1,139

1,079

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Objectives To develop and validate updated QRISK3 prediction algorithms to estimate the 10 year risk of cardiovascular disease in women and men accounting for potential new risk factors. Design Prospective open cohort study. Setting General practices in England providing data for the QResearch database. Participants 1309 QResearch general practices in England: 981 practices were used to develop the scores and a separate set of 328 practices were used to validate the scores. 7.89 million patients aged 25-84 years were in the derivation cohort and 2.67 million patients in the validation cohort. Patients were free of cardiovascular disease and not prescribed statins at baseline. Methods Cox proportional hazards models in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QRISK2 (age, ethnicity, deprivation, systolic blood pressure, body mass index, total cholesterol: high density lipoprotein cholesterol ratio, smoking, family history of coronary heart disease in a first degree relative aged less than 60 years, type 1 diabetes, type 2 diabetes, treated hypertension, rheumatoid arthritis, atrial fibrillation, chronic kidney disease (stage 4 or 5)) and new risk factors (chronic kidney disease (stage 3, 4, or 5), a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, systemic lupus erythematosus (SLE), atypical antipsychotics, severe mental illness, and HIV/AIDs). We also considered erectile dysfunction diagnosis or treatment in men. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status. Main outcome measures Incident cardiovascular disease recorded on any of the following three linked data sources: general practice, mortality, or hospital admission records. Results 363 565 incident cases of cardiovascular disease were identified in the derivation cohort during follow-up arising from 50.8 million person years of observation. All new risk factors considered met the model inclusion criteria except for HIV/AIDS, which was not statistically significant. The models had good calibration and high levels of explained variation and discrimination. In women, the algorithm explained 59.6% of the variation in time to diagnosis of cardiovascular disease (R2, with higher values indicating more variation), and the D statistic was 2.48 and Harrell’s C statistic was 0.88 (both measures of discrimination, with higher values indicating better discrimination). The corresponding values for men were 54.8%, 2.26, and 0.86. Overall performance of the updated QRISK3 algorithms was similar to the QRISK2 algorithms. Conclusion Updated QRISK3 risk prediction models were developed and validated. The inclusion of additional clinical variables in QRISK3 (chronic kidney disease, a measure of systolic blood pressure variability (standard deviation of r...

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“…The promise of precision medicine rests in part on the ability to leverage Big Data resources such as EHRs that track vast clinical, demographic and genetic data into accurate prediction tools for individual patient risk . Already, clinical information recorded in EHRs has been used to develop risk models for a wide range of conditions; examples include cardiovascular disease , hospital readmission , acute kidney injury , and postoperative complications . The clinical research potential of EHRs is immense; however, EHRs are imperfect and highly susceptible to missing and inaccurate diagnoses and behavioral information .…”

Section: Introductionmentioning

confidence: 99%

Corrected ROC analysis for misclassified binary outcomes

et al. 2017

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Creating accurate risk prediction models from Big Data resources such as Electronic Health Records (EHRs) is a critical step toward achieving precision medicine. A major challenge in developing these tools is accounting for imperfect aspects of EHR data, particularly the potential for misclassified outcomes. Misclassification, the swapping of case and control outcome labels, is well known to bias effect size estimates for regression prediction models. In this paper, we study the effect of misclassification on accuracy assessment for risk prediction models and find that it leads to bias in the area under the curve (AUC) metric from standard ROC analysis. The extent of the bias is determined by the false positive and false negative misclassification rates as well as disease prevalence. Notably, we show that simply correcting for misclassification while building the prediction model is not sufficient to remove the bias in AUC. We therefore introduce an intuitive misclassification-adjusted ROC procedure that accounts for uncertainty in observed outcomes and produces bias-corrected estimates of the true AUC. The method requires that misclassification rates are either known or can be estimated, quantities typically required for the modeling step. The computational simplicity of our method is a key advantage, making it ideal for efficiently comparing multiple prediction models on very large datasets. Finally, we apply the correction method to a hospitalization prediction model from a cohort of over 1 million patients from the Veterans Health Administrations EHR. Implementations of the ROC correction are provided for Stata and R. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

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Improvement in Cardiovascular Risk Prediction with Electronic Health Records

Cited by 42 publications

References 27 publications

Association of epistaxis with atherosclerotic cardiovascular disease

Association of epistaxis with atherosclerotic cardiovascular disease

Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study

Corrected ROC analysis for misclassified binary outcomes

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