Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records - PubMed (original) (raw)

. 2016 Dec 1;1(9):1007-1013.

doi: 10.1001/jamacardio.2016.3366.

Amy J Graves 2, Meng Xu 2, Aihua Bian 2, Pedro Luis Teixeira 3, M Benjamin Shoemaker 1, Babar Parvez 1, Hua Xu 4, Susan R Heckbert 5, Patrick T Ellinor 6, Emelia J Benjamin 7, Alvaro Alonso 8, Joshua C Denny 3, Karel G M Moons 9, Ayumi K Shintani 10, Frank E Harrell Jr 2, Dan M Roden 1, Dawood Darbar 11

Affiliations

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

Matthew J Kolek et al. JAMA Cardiol. 2016.

Abstract

Importance: Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and health care expenditures. Accurate prediction of incident AF would enhance AF management and potentially improve patient outcomes.

Objective: To validate the AF risk prediction model originally developed by the Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation (CHARGE-AF) investigators using a large repository of electronic medical records (EMRs).

Design, setting, and participants: In this prediction model study, deidentified EMRs of 33 494 individuals 40 years or older who were white or African American and had no history of AF were reviewed and analyzed. The participants were followed up in the internal medicine outpatient clinics at Vanderbilt University Medical Center for incident AF from December 31, 2005, until December 31, 2010. Adjusting for differences in baseline hazard, the CHARGE-AF Cox proportional hazards model regression coefficients were applied to the EMR cohort. A simple version of the model with no echocardiographic variables was also evaluated. Data were analyzed from October 31, 2013, to January 31, 2014.

Main outcomes and measures: Incident AF. Predictors in the model included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, type 2 diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval.

Results: Among the 33 494 participants, the median age was 57 (interquartile range, 49-67) years; 57% of patients were women, 43% were men, 85.7% were white, and 14.3% were African American. During the mean (SD) follow-up of 4.8 (0.9) years, 2455 individuals (7.3%) developed AF. Both models had poor calibration in the EMR cohort, with underprediction of AF among low-risk individuals and overprediction of AF among high-risk individuals (10th and 90th percentiles for predicted probability of incident AF, 0.005 and 0.179, respectively). The full CHARGE-AF model had a C index of 0.708 (95% CI, 0.699-0.718) in our cohort. The simple model had similar discrimination (C index, 0.709; 95% CI, 0.699-0.718; P = .70 for difference between models).

Conclusions and relevance: Despite reasonable discrimination, the CHARGE-AF models showed poor calibration in this EMR cohort. This study highlights the difficulties of applying a risk model derived from prospective cohort studies to an EMR cohort and suggests that these AF risk prediction models be used with caution in the EMR setting. Future risk models may need to be developed and validated within EMR cohorts.

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Conflict of interest statement

Karel G.M. Moons gratefully acknowledges financial contribution by the Netherlands Organisation for Scientific Research (project 9120.8004 and 918.10.615). All other authors: no disclosures

Figures

Figure 1

Figure 1

Kaplan-Meier curve showing the cumulative incidence of atrial fibrillation in the Vanderbilt electronic medical record cohort. The shaded area represents 95% confidence interval. AF: atrial fibrillation.

Figure 2

Figure 2

Calibration curve depicting predicted and observed cumulative incidence of atrial fibrillation using the CHARGE-AF model in the Vanderbilt electronic medical record cohort. The figure is restricted to 95% of subjects with predicted cumulative incidence of atrial fibrillation from 0 to 0.3. The diagonal line represents a hypothetical ideal curve where predicted and actual atrial fibrillation probability match perfectly for all levels of predicted risk, representing perfect calibration. The curve shows under-prediction for subjects with a predicted probability of atrial fibrillation 0 to 0.15 and over-prediction for subjects with a predicted probability ≥0.15. A histogram showing the distribution of predicted risk is shown. AF: atrial fibrillation.

Figure 3

Figure 3

Scatterplot of cumulative predicted probability of atrial fibrillation at 5 years and age. The C-index for a Cox proportional hazards model using only age as a dependent variable was 0.684 (95% confidence interval: 0.674 to 0.694). While age is the most powerful predictor of incident atrial fibrillation, this figure demonstrates that for any given age there is a wide variability in the predicted probability of developing atrial fibrillation. AF: atrial fibrillation.

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