Abstract 17956: External Validation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Matthew J Kolek ◽  
Amy J Graves ◽  
Aihua Bian ◽  
Pedro L Teixeira ◽  
Moore B Shoemaker ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Prediction Model ◽  
Electronic Medical Records ◽  
Risk Prediction ◽  
Medical Records ◽  
Risk Model ◽  
External Validation ◽  
Left Ventricular ◽  
Cox Proportional Hazards Model ◽  
Ambulatory Setting

Background: Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and healthcare costs. Accurate prediction of incident AF might enhance patient care and improve outcomes. We aimed to externally validate the AF risk model developed by the CHARGE-AF investigators utilizing a large repository of electronic medical records (EMR). Methods: Using a database of de-identified EMRs, we conducted a retrospective cohort study of subjects serially followed in internal medicine clinics at our institution (minimum 3 visits in a 24 month window). Subjects were followed for incident AF from 2005 until 2010. We applied the published CHARGE-AF Cox proportional hazards model beta coefficients to our cohort. Predictors included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval. Calibration and discrimination were assessed by generating calibration plots and calculating C-statistics. Results: The study included 33,494 subjects with median age 57 years (25th to 75th percentile: 49 - 67), 57% women, 86% whites, and 14% African Americans. During the mean follow-up period of 4.8 ± 0.85 years, 2455 (7.3%) subjects developed AF. After correcting for baseline hazard, the CHARGE-AF model over-predicted AF at the highest risk deciles but was otherwise well-calibrated (Figure) and showed good discrimination, with a C-statistic of 0.746 (95% confidence interval: 0.738 to 0.754). Conclusion: From clinical factors readily accessible in a large de-identified EMR repository, we externally validated the CHARGE-AF risk prediction model to identify individuals at risk for developing AF in an ambulatory setting. These data not only provide strong validation for the CHARGE-AF risk prediction tool, but also indicate that the tool, and thus primary prevention strategies, can be implemented in an EMR context.

scholarly journals Re‐CHARGE‐AF: Recalibration of the CHARGE‐AF Model for Atrial Fibrillation Risk Prediction in Patients With Acute Stroke

2021 ◽  
Author(s):  
Jeffrey M. Ashburner ◽  
Xin Wang ◽  
Xinye Li ◽  
Shaan Khurshid ◽  
Darae Ko ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Acute Stroke ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Model ◽  
Cox Proportional Hazards Model ◽  
Baseline Risk ◽  
Accurate Estimation ◽  
Chi Square ◽  
C Statistic

Background Performance of existing atrial fibrillation (AF) risk prediction models in poststroke populations is unclear. We evaluated predictive utility of an AF risk model in patients with acute stroke and assessed performance of a fully refitted model. Methods and Results Within an academic hospital, we included patients aged 46 to 94 years discharged for acute ischemic stroke between 2003 and 2018. We estimated 5‐year predicted probabilities of AF using the Cohorts for Heart and Aging Research in Genomic Epidemiology for Atrial Fibrillation (CHARGE‐AF) model, by recalibrating CHARGE‐AF to the baseline risk of the sample, and by fully refitting a Cox proportional hazards model to the stroke sample (Re‐CHARGE‐AF) model. We compared discrimination and calibration between models and used 200 bootstrap samples for optimism‐adjusted measures. Among 551 patients with acute stroke, there were 70 incident AF events over 5 years (cumulative incidence, 15.2%; 95% CI, 10.6%–19.5%). Median predicted 5‐year risk from CHARGE‐AF was 4.8% (quartile 1–quartile 3, 2.0–12.6) and from Re‐CHARGE‐AF was 16.1% (quartile 1–quartile 3, 8.0–26.2). For CHARGE‐AF, discrimination was moderate (C statistic, 0.64; 95% CI, 0.57–0.70) and calibration was poor, underestimating AF risk (Greenwood‐Nam D’Agostino chi‐square, P <0.001). Calibration with recalibrated baseline risk was also poor (Greenwood‐Nam D’Agostino chi‐square, P <0.001). Re‐CHARGE‐AF improved discrimination ( P =0.001) compared with CHARGE‐AF (C statistic, 0.74 [95% CI, 0.68–0.79]; optimism‐adjusted, 0.70 [95% CI, 0.65–0.75]) and was well calibrated (Greenwood‐Nam D’Agostino chi‐square, P =0.97). Conclusions Covariates from an established AF risk model enable accurate estimation of AF risk in a poststroke population after recalibration. A fully refitted model was required to account for varying baseline AF hazard and strength of associations between covariates and incident AF.

scholarly journals Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records

2016 ◽  
Vol 1 (9) ◽  
pp. 1007 ◽  
Author(s):  
Matthew J. Kolek ◽  
Amy J. Graves ◽  
Meng Xu ◽  
Aihua Bian ◽  
Pedro Luis Teixeira ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Prediction Model ◽  
Electronic Medical Records ◽  
Medical Records

Fall Ascertainment and Development of a Risk Prediction Model Using Electronic Medical Records

2019 ◽  
Vol 67 (7) ◽  
pp. 1417-1422 ◽  
Author(s):  
Caryn E. S. Oshiro ◽  
Timothy B. Frankland ◽  
A. Gabriela Rosales ◽  
Nancy A. Perrin ◽  
Christina L. Bell ◽  
...  
Keyword(s):  
Prediction Model ◽  
Electronic Medical Records ◽  
Risk Prediction ◽  
Medical Records ◽  
Risk Prediction Model

scholarly journals Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258377
Author(s):  
Beom Joon Kim ◽  
Keon-Joo Lee ◽  
Eun Lyeong Park ◽  
Kanta Tanaka ◽  
Masatoshi Koga ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Prediction Model ◽  
Risk Prediction ◽  
Recurrent Events ◽  
Recurrent Stroke ◽  
External Validation ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
Stroke Recurrence

Background There is currently no validated risk prediction model for recurrent events among patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). Considering that the application of conventional risk scores has contextual limitations, new strategies are needed to develop such a model. Here, we set out to develop and validate a comprehensive risk prediction model for stroke recurrence in AIS patients with AF. Methods AIS patients with AF were collected from multicenter registries in South Korea and Japan. A developmental dataset was constructed with 5648 registered cases from both countries for the period 2011‒2014. An external validation dataset was also created, consisting of Korean AIS subjects with AF registered between 2015 and 2018. Event outcomes were collected during 1 year after the index stroke. A multivariable prediction model was developed using the Fine–Gray subdistribution hazard model with non-stroke mortality as a competing risk. The model incorporated 21 clinical variables and was further validated, calibrated, and revised using the external validation dataset. Results The developmental dataset consisted of 4483 Korean and 1165 Japanese patients (mean age, 74.3 ± 10.2 years; male 53%); 338 patients (6%) had recurrent stroke and 903 (16%) died. The clinical profiles of the external validation set (n = 3668) were comparable to those of the developmental dataset. The c-statistics of the final model was 0.68 (95% confidence interval, 0.66 ‒0.71). The developed prediction model did not show better discriminative ability for predicting stroke recurrence than the conventional risk prediction tools (CHADS2, CHA2DS2-VASc, and ATRIA). Conclusions Neither conventional risk stratification tools nor our newly developed comprehensive prediction model using available clinical factors seemed to be suitable for identifying patients at high risk of recurrent ischemic stroke among AIS patients with AF in this modern direct oral anticoagulant era. Detailed individual information, including imaging, may be warranted to build a more robust and precise risk prediction model for stroke survivors with AF.

Atrial Fibrillation and Hypertension: “Quo Vadis”

2022 ◽  
Vol 18 ◽  
Author(s):  
McCall Walker ◽  
Paras Patel ◽  
Osung Kwon ◽  
Ryan J Koene ◽  
Daniel A. Duprez ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Blood Pressure ◽  
Blood Pressure Measurement ◽  
Renin Angiotensin System ◽  
Left Ventricular ◽  
Ambulatory Setting ◽  
Blood Pressure Goal ◽  
Contributing Factors ◽  
Acceptable Method ◽  
Quo Vadis

Abstract: Hypertension is one of the most well-established risk factors for atrial fibrillation. Long-standing untreated hypertension leads to structural remodeling and electrophysiologic alterations causing an atrial myopathy that forms a vulnerable substrate for the development and maintenance of atrial fibrillation. Hypertension-induced hemodynamic, inflammatory, hormonal, and autonomic changes all appear to be important contributing factors. Furthermore, hypertension is also associated with several atrial fibrillation-related comorbidities. As such, hypertension may represent an important target for therapy in atrial fibrillation. Clinicians should be aware of pitfalls of the blood pressure measurement in atrial fibrillation. While the auscultatory method is preferred, the use of automated devices appears to be an acceptable method in the ambulatory setting. There are pathophysiologic bases and emerging clinical evidence suggesting the benefit of renin-angiotensin system inhibition in risk reduction of atrial fibrillation development particularly in patients with left ventricular hypertrophy or left ventricular dysfunction. A better understanding of hypertension’s pathophysiologic link to atrial fibrillation may lead to the development of novel therapies for the primary prevention of atrial fibrillation. Finally, future studies are needed to address optimal blood pressure goal to minimize the risk of atrial fibrillation-related complications.

scholarly journals GARFIELD-AF model for prediction of stroke and major bleeding in atrial fibrillation: a Danish nationwide validation study

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e033283 ◽  
Author(s):  
Frederik Dalgaard ◽  
Karen Pieper ◽  
Freek Verheugt ◽  
A John Camm ◽  
Keith AA Fox ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischaemic Stroke ◽  
Major Bleeding ◽  
Risk Model ◽  
Oral Anticoagulants ◽  
External Validation ◽  
Bleeding Risk ◽  
Risk Scores ◽  
Secondary Outcome

ObjectivesTo externally validate the accuracy of the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) model against existing risk scores for stroke and major bleeding risk in patients with non-valvular AF in a population-based cohort.DesignRetrospective cohort study.SettingDanish nationwide registries.Participants90 693 patients with newly diagnosed non-valvular AF were included between 2010 and 2016, with follow-up censored at 1 year.Primary and secondary outcome measuresExternal validation was performed using discrimination and calibration plots. C-statistics were compared with CHA2DS2VASc score for ischaemic stroke/systemic embolism (SE) and HAS-BLED score for major bleeding/haemorrhagic stroke outcomes.ResultsOf the 90 693 included, 51 180 patients received oral anticoagulants (OAC). Overall median age (Q1, Q3) were 75 (66–83) years and 48 486 (53.5%) were male. At 1-year follow-up, a total of 2094 (2.3%) strokes/SE, 2642 (2.9%) major bleedings and 10 915 (12.0%) deaths occurred. The GARFIELD-AF model was well calibrated with the predicted risk for stroke/SE and major bleeding. The discriminatory value of GARFIELD-AF risk model was superior to CHA2DS2VASc for predicting stroke in the overall cohort (C-index: 0.71, 95% CI: 0.70 to 0.72 vs C-index: 0.67, 95% CI: 0.66 to 0.68, p<0.001) as well as in low-risk patients (C-index: 0.64, 95% CI: 0.59 to 0.69 vs C-index: 0.57, 95% CI: 0.53 to 0.61, p=0.007). The GARFIELD-AF model was comparable to HAS-BLED in predicting the risk of major bleeding in patients on OAC therapy (C-index: 0.64, 95% CI: 0.63 to 0.66 vs C-index: 0.64, 95% CI: 0.63 to 0.65, p=0.60).ConclusionIn a nationwide Danish cohort with non-valvular AF, the GARFIELD-AF model adequately predicted the risk of ischaemic stroke/SE and major bleeding. Our external validation confirms that the GARFIELD-AF model was superior to CHA2DS2VASc in predicting stroke/SE and comparable with HAS-BLED for predicting major bleeding.

scholarly journals Simple risk models to predict cardiovascular death in patients with stable coronary artery disease

2020 ◽  
Author(s):  
Ian Ford ◽  
Michele Robertson ◽  
Nicola Greenlaw ◽  
Christophe Bauters ◽  
Gilles Lemesle ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Model ◽  
Stable Coronary Artery Disease ◽  
Cardiovascular Death ◽  
Left Ventricular ◽  
Cox Proportional Hazards Model ◽  
Clinical Variables ◽  
Model Based ◽  
Artery Disease

Abstract Aims Risk estimation is important to motivate patients to adhere to treatment and to identify those in whom additional treatments may be warranted and expensive treatments might be most cost effective. Our aim was to develop a simple risk model based on readily available risk factors for patients with stable coronary artery disease (CAD). Methods and results Models were developed in the CLARIFY registry of patients with stable CAD, first incorporating only simple clinical variables and then with the inclusion of assessments of left ventricular function, estimated glomerular filtration rate, and haemoglobin levels. The outcome of cardiovascular death over ∼5 years was analysed using a Cox proportional hazards model. Calibration of the models was assessed in an external study, the CORONOR registry of patients with stable coronary disease. We provide formulae for calculation of the risk score and simple integer points-based versions of the scores with associated look-up risk tables. Only the models based on simple clinical variables provided both good c-statistics (0.74 in CLARIFY and 0.80 or over in CORONOR), with no lack of calibration in the external dataset. Conclusion Our preferred model based on 10 readily available variables [age, diabetes, smoking, heart failure (HF) symptom status and histories of atrial fibrillation or flutter, myocardial infarction, peripheral arterial disease, stroke, percutaneous coronary intervention, and hospitalization for HF] had good discriminatory power and fitted well in an external dataset. Study registration The CLARIFY registry is registered in the ISRCTN registry of clinical trials (ISRCTN43070564).

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