The ARIC study is a prospective cohort investigation aimed to identify risk factors for atherosclerosis and cardiovascular disease. ARIC recruited probability samples of adults aged 45–64 years from 4 U.S. communities: Forsyth County, North Carolina; Jackson, Mississippi; Minneapolis suburbs, Minnesota; and Washington County, Maryland.5
Blacks were over-sampled from Forsyth County and exclusively sampled from Jackson. A total of 15,792 participants (8710 women, 4266 blacks) were enrolled from 1987 to 1989, and completed a home interview and clinic visit. Three triennial follow-up clinic visits were conducted (1990–92, 1993–95, 1996–98). In addition, participants are followed-up by annual telephone interviews (with a >93% response rate) and active surveillance of the ARIC community hospitals. The ARIC study was approved by institutional review boards at each participating center, and informed consent was obtained from participants at every clinic visit.
Electrocardiograms (ECGs) during the baseline visit were used to identify individuals with prevalent AF or atrial flutter for exclusion. Incident AF diagnoses within 10 years of the baseline exam were identified from 3 sources: ECGs performed during study follow-up visits through 1998, and hospital discharge records and death certificates through 10 years of follow-up.
All ARIC examination ECGs were recorded using MAC PC Personal Cardiographs (Marquette Electronics, Inc., Milwaukee, WI). A standard supine 12-lead resting ECG was recorded ≥ 1 hour after smoking tobacco or ingestion of caffeine at each clinic visit. ECGs were then transmitted by modem to the ARIC ECG Reading Center for computer coding. ECG recordings during follow-up that were computer coded as AF were visually re-checked by a cardiologist to confirm the diagnosis.6
Annual follow-up telephone calls were made to cohort participants in order to identify hospitalizations and deaths. In addition, ARIC community hospitals were surveyed for potential cardiovascular events. Hospital discharge ICD codes were recorded from all hospitalizations, and AF was identified by an ICD-9 discharge code of 427.31 or 427.32 among any of the discharge diagnoses. AF was also identified when any listed cause of death on a death certificate was coded as AF (ICD-9 code 427.3 or ICD-10 code I48). AF occurring simultaneously with heart revascularization surgery (ICD-9 code 36.X) or other cardiac surgery involving heart valves or septa (ICD-9 code 35.X) was not considered an incident event and follow-up was continued beyond that episode for incident AF not associated with cardiac surgery. Prior analysis within the ARIC cohort to determine the validity of hospital discharge diagnoses for AF reported 84% sensitivity and 98% specificity for the ascertainment of AF.3
Study participants were asked to fast for 12 hours before the clinic visit, during which a blood sample was obtained and a physical exam was performed. Blood collection and processing techniques for the ARIC study have been previously described.7
Enzymatic methods were used to measure total cholesterol (TC) and triglycerides (TG).8
High-density lipoprotein (HDL) cholesterol was measured enzymatically after dextran sulfate-Mg2+
precipitation of other lipoproteins.9
Low-density lipoprotein (LDL) cholesterol levels were estimated with the Friedewald formula for individuals with TG levels <400 mg/dL.10
In a scrub suit and without shoes, standing height and waist circumference (at the level of the umbilicus) were measured to the nearest centimeter. Body mass index (BMI) was calculated as weight (in kilograms) divided by height (in meters) squared.
Race, smoking status, and drinking status were determined by self-report. The sports index for physical activity during leisure time ranged from 1 (low) to 5 (high), and was based on the questionnaire developed by Baecke et al.11
Blood pressures were measured 3 times in the sitting position after 5 minutes of rest using a random-zero sphygmomanometer, and the last 2 measurements were averaged. Participants were asked to bring all medications with them during clinic visits. A prescription bottle or self-report was used to determine cholesterol and blood pressure medication use.
The presence of a systolic or diastolic murmur was identified during the physical examination by a trained clinician using a stethoscope. A resting 12-lead ECG was used to define the P-R interval and presence of left ventricular hypertrophy (LVH) and left atrial enlargement (LAE). ECG-diagnosed LVH was considered present if the Cornell voltage was >28 mm in men or >22 mm in women.12
LAE by ECG was defined as a p-wave duration of ≥ 120 ms.13
A participant was categorized as diabetic if they had a fasting glucose ≥ 126 mg/dL (or non-fasting glucose of ≥200 mg/dL), reported a physician diagnosis of diabetes, or was currently taking medication for diabetes. Prevalent CHD at baseline included a history of myocardial infarction (MI), MI adjudicated from the baseline ECG, or history of coronary bypass or angioplasty. Prevalent HF was identified using the Gothenburg criteria14
or self-report of HF medication use in the past 2 weeks.
All analyses were conducted using SAS version 8.2 (SAS Institute, Cary, NC). For the development of this risk score, we considered the following variables at baseline: age (45 to <50 (reference), 50 to <55, 55 to <60, and 60 to 64 years), gender (male, female (reference)), race (black, white (reference)), BMI (<20, 20 to <25 (reference), 25 to <30, ≥30 kg/m2), height (<164 (reference), 164 to <173, ≥173 cm), waist circumference (<88/<102 (reference), ≥88/102 cm in men/women), sports score (<2.0, 2.0 to <3.0, 3.0 to <4.0, ≥4.0 (reference)), smoking status (current, former, never (reference)), drinking status (current, former, never (reference)), systolic blood pressure (<100, 100 to <120 (reference), 120 to <140, 140 to <160, ≥160 mmHg), diastolic blood pressure (<70, 70 to <80 (reference), 80 to <90, 90 to <100, ≥100 mmHg), hypertension medication usage (no (reference), yes), total cholesterol (<200 (reference), 200 to <240, ≥240 mg/dL), LDL cholesterol (<100 (reference), 100 to <130, 130 to <160, 160 to <190, ≥190 mg/dL), HDL cholesterol (<40, 40 to <60, ≥60 mg/dL (reference)), triglycerides (<150 (reference), 150 to <200, ≥200 mg/dL), cholesterol medication usage (no (reference), yes), precordial murmur (no (reference), yes), heart rate (<60, 60 to <90 (reference), ≥90 beats per minute (bpm)), P-R interval (<160 (reference), 160 to <200, ≥200 ms), LVH by ECG (no (reference), yes), LAE by ECG (no (reference), yes), diabetes (no (reference), yes), CHD (no (reference), yes), and HF (no (reference), yes). Continuous variables were categorized based on clinical criteria or on the distribution of the variable in the ARIC study.
Of the 15,792 ARIC participants, we excluded those who were not of black or white race (N=48), blacks from Minneapolis and Washington County (N=55), prevalent AF (N=37) or missing AF status (N=244) at baseline, those with unreadable ECGs (N=85), and those with missing values for any variable in our final risk score (N=777). Person-years of follow-up were computed from the baseline exam until a first AF diagnosis, death, loss to follow-up, or a follow-up of 10 years, whichever came first. Univariate associations of AF with potential risk factors were run first using Cox proportional hazards models. Significant (p<0.10) risk factors from the univariate models were then pooled into 1 multivariate Cox model and a backwards stepwise elimination was used to identify significant (p<0.10) predictors in our multivariate model. All possible interactions of risk factors with age and race were then tested. Interaction tests between risk factors identified in our multivariate model and log of follow-up time confirmed the proportional hazards assumption was met.
Once the final Cox model was determined, we followed the method used by the Framingham Heart Study15
to calculate points associated with each level of our risk factors and to determine the 10-year probability of developing AF by point total. We calculated a score for all participants in our dataset by calculating a point total based on the risk score. The discrimination of both the Cox regression model and the actual point-based risk score was estimated using the area under the receiver-operating characteristics curve (AUC).16
The calibration, a measure of goodness of model fit, was assessed by comparing the observed and predicted number of AF events in deciles of predicted risk, as calculated by the Grønnesby-Borgan chi-square statistic.17
We also calculated a point-based score for participants using the Framingham Heart Study’s AF,2
and hard CHD19
risk scores, as well as ARIC’s CHD risk score,20, 21
in order to estimate how well these risk scores predict AF in comparison to our newly developed AF risk score.
Finally, 1000 bootstrap samples were generated, sampling individuals with replacement, in order to compare our Cox regression model to the point-based score and to conduct an internal validation of our risk score. Bootstrapping methods provide more stable estimates with lower bias compared to other methods of internal validation.22
However, since we used the same cohort to generate the 1000 bootstrap samples for validation of our risk score as we used to develop the risk score, we adjusted our AUC obtained for the internal validation for optimism.23