A new method of evaluating irregular heartbeats outperformed the approach that’s currently used widely in stroke units to detect instances of atrial fibrillation.
The technology, called electrocardiomatrix, goes further than standard cardiac telemetry by examining large amounts of telemetry data in a way that’s so detailed it’s impractical for individual clinicians to attempt.
Co-inventor Jimo Borjigin, Ph.D., recently published the latest results from her electrocardiomatrix technology in Stroke.
Among stroke patients with usable data (260 of 265), electrocardiomatrix was highly accurate in identifying those with Afib.
“We validated the use of our technology in a clinical setting, finding the electrocardiomatrix was an accurate method to determine whether a stroke survivor had an Afib,” says Borjigin, an associate professor of neurology and molecular and integrative physiology at Michigan Medicine.
A crucial metric
After a stroke, neurologists are tasked with identifying which risk factors may have contributed in order to do everything possible to prevent another event.
That makes detecting irregular heartbeat an urgent concern for these patients, explains first author Devin Brown, M.D., professor of neurology and a stroke neurologist at Michigan Medicine.
“Atrial fibrillation is a very important and modifiable risk factor for stroke,” Brown says.
Importantly, the electrocardiomatrix identification method was highly accurate for the 212 patients who did not have a history of Afib, Borjigin says.
She says this group is most clinically relevant, because of the importance of determining whether stroke patients have previously undetected Afib.
When a patient has Afib, their irregular heartbeat can lead to blood collecting in their heart, which can form a stroke-causing clot.
Many different blood thinners are on the market today, making it easier for clinicians to get their patients on an anticoagulant they’ll take as directed.
The most important part is determining Afib’s presence in the first place.
Much-needed improvement
Brown says challenges persist in detecting intermittent Afib during stroke hospitalization.
“More accurate identification of Afib should translate into more strokes prevented,” she says.
Once hospitalized in the stroke unit, patients are typically placed on continuous heart rhythm monitoring.
Stroke neurologists want to detect possible intermittent Afib that initial monitoring like an electrocardiogram, or ECG, would have missed.
Because a physician can’t reasonably review every single heartbeat, current monitoring technology flags heart rates that are too high, Brown says.
The neurologist then reviews these flagged events, which researchers say could lead to some missed Afib occurrences, or false positives in patients with different heart rhythm issues.
In contrast, Borjigin’s electrocardiomatrix converts two-dimensional signals from the ECG into a three-dimensional heatmap that allows for rapid inspection of all collected heartbeats.
Borjigin says this method permits fast, accurate and intuitive detection of cardiac arrhythmias. It also minimizes false positive as well as false negative detection of arrhythmias.
“We originally noted five false positives and five false negatives in the study,” Borjigin says, “but expert review actually found the electrocardiomatrix was correct instead of the clinical documentation we were comparing it to.”
More applications
The Borjigin lab also recently demonstrated the usefulness of the electrocardiomatrix to differentiate between Afib and atrial flutter.
In addition, the lab has shown the ability of electrocardiomatrix to capture reduced heart-rate variability in critical care patients.
Borjigin says she envisions electrocardiomatrix technology will one day be used to assist the detection of all cardiac arrhythmias online or offline and side-by-side with the use of ECG.
“I believe that sooner or later, electrocardiomatrix will be used in clinical practice to benefit patients,” she says.
Atrial fibrillation (AF) is associated with a 5-fold increased risk of stroke.1
Current practice guidelines recommend risk stratification with the CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, stroke, vascular disease, sex) score to identify appropriate candidates for systemic anticoagulation to prevent stroke.2,3
Unfortunately, emerging clinical evidence has highlighted limitations in the predictive value of the CHA2DS2-VASc score.4–9
Use of markers that more directly reflect the underlying mechanisms of AF-related thromboembolism may help improve the current risk stratification paradigm.
There has been increasing recognition of the contribution of prothrombotic remodeling of the atrial architecture to thromboembolism risk.6,10
Abnormal atrial conduction measured through analysis of P-wave morphology—P-wave indices (PWIs)—has been associated with atrial remodeling11–14 and stroke.15–21
In people with AF, periods of sinus rhythm present an opportunity to detect underlying atrial remodeling through measurement of PWIs.
We hypothesized that the addition of PWIs to the CHA2DS2-VASc score would augment stroke-risk prediction in individuals with AF.
We tested this hypothesis in the ARIC study (Atherosclerosis Risk in Communities) and validated our findings in the MESA (Multi-Ethnic Study of Atherosclerosis), 2 large prospective community-based cohort studies in the United States.
Methods
Data Availability
Some access restrictions apply to the data underlying the findings.
The consent signed by study participants does not allow the public release of their data. Data from the ARIC study can be accessed by contacting the ARIC coordinating center ([email protected]). Data from the MESA study can be accessed by contacting the MESA data coordinating center ([email protected]).
Study Population
The ARIC study was designed to evaluate risk factors, etiology, and clinical manifestations of atherosclerotic coronary heart disease in the general population.
Between 1987 and 1989, 15 792 men and women 45 to 64 years of age were recruited and enrolled from 4 US communities (Washington County, MD; Forsyth County, NC; Jackson, MS; and suburban Minneapolis, MN). After the baseline examination, participants completed 5 follow-up study visits, the most recent in 2016–2017.
In between study visits, participants (or proxy) have been contacted annually by telephone (semiannually since 2012) to ascertain information on hospitalizations and deaths.
Active community-wide surveillance of local hospitals has been performed to identify additional hospitalizations and cardiovascular events.
Further details regarding outcome ascertainment procedures, study design, and population statistics have been previously described.22
Approval for the study was obtained from the institutional review board on human research at each participating institution, and all participants provided informed consent.
The present analysis utilized data obtained from the baseline study visit in 1987–1989 through 2013.
We excluded participants with missing ECG data (n=242), missing P-wave indices at baseline (n=45), prevalent AF (n=37), and those who were not white or black from all study sites and nonwhite from Minneapolis and Washington County (because of the small sample size; n=103), resulting in a baseline cohort of 15 365 participants. We then identified 2625 cases of incident AF after the baseline study visit. Because of the potential bias introduced by anticoagulant use when studying stroke risk, participants with anticoagulant use within 1 year of AF diagnosis (n=172) were excluded. We also excluded those without follow-up beyond AF date (n=224), resulting in a final cohort of 2229 participants with incident AF.
Two-dimensional (2D) and 3-dimensional (3D) echocardiograms were performed during visit 5 (2011–2013).
2D echocardiograms were performed on 6538 participants during visit 5.
As previously done, exclusions were made for race (n=42), resulting in 6496 participants.
Of these participants, 6008 had interpretable ECGs with PWI values.
Of these, 5830 had interpretable 2D echocardiogram data.
3D echocardiograms were performed on 3035 participants during visit 5.
Exclusions were made for race (n=16), poor image quality (n=1779), severe valvular heart disease or history of valve surgery (n=64), and missing body mass index or body surface area data (n=34), resulting in a final analysis cohort of 1142 participants with 3D echocardiograms.
The MESA study was designed to investigate the prevalence, natural history, and correlates of subclinical cardiovascular disease.
Between the years of 2000 and 2002, 6814 men and women 45 to 85 years of age without prevalent cardiovascular disease were recruited and enrolled from 6 US communities (Baltimore City and Baltimore County, MD; Chicago, IL; Forsyth County, NC; Los Angeles County, CA; New York, NY; and St. Paul, MN).
After the initial baseline study visit, there have been 5 additional follow-up visits, the most recent in 2016–2018.
Participants are contacted on an annual basis to identify new hospitalizations and medical diagnoses.
Death certificates and medical records are then reviewed for the purposes of outcome ascertainment.
Further details on study protocol and procedures have been previously described.23
Approval for the study was obtained from the institutional review board on human research at each participating institution, and all participants provided informed consent.
The present analysis utilized data obtained during the baseline visit in 2000–2002 through 2014. We excluded participants with prevalent AF (n=66) or missing ECG or P-wave indices at baseline (n=49), and we identified 876 cases of incident AF. We then excluded those without follow-up beyond the date of AF diagnosis (n=117), oral anticoagulant use within 1 year of AF diagnosis (n=54), and those with invalid P-wave axis measurements (n=5), resulting in a final cohort of 700 participants with incident AF.
Measurement of P-Wave Indices
Measurement of PWIs in ARIC17 and MESA23 was done in a consistent manner and has been described. We evaluated P-wave axis, P-wave duration, advanced interatrial block (aIAB), and P-wave terminal force in lead V1 using a standard 12-lead ECG (Figure 1).
P-wave axis is a measure of the net direction of atrial depolarization. It is determined by measuring net-positive or net-negative P-wave deflections on all 6 limb leads and calculating the net direction of electric activity using the hexaxial reference system (Figure 1).
It is a standard, computer-generated ECG index that was reported on all ECGs in ARIC and MESA. Abnormal P-wave axis (aPWA) was defined as any value outside 0° to 75°.
P-wave duration is a reflection of the time required for right and left atrial depolarization. It was measured from the conclusion of the T-P segment (P-wave onset) to return to baseline (PR interval).
For biphasic P-waves, P-wave duration encompassed both positive and negative deflections from baseline. Prolonged P-wave duration (PPWD) was present if the maximum P-wave duration in any lead was >120 ms on a standard 12-lead ECG. aIAB is an indicator of interatrial conduction block in Bachman’s bundle such that the left atrium is activated superiorly.
It was defined as PPWD+biphasic P-wave morphology in leads III and aVF with biphasic morphology or notched morphology in lead II. P-wave terminal force in lead V1 is a measure of left atrial activation. It is determined by multiplying the duration (ms) and depth (μV) of the downward deflection (terminal portion) of the P-wave in lead V1. Abnormal P-wave terminal force in lead V1 (aPTFV1) was defined as ≤−4000 μV*ms. All P-wave indices were computed from the closest sinus rhythm ECG before AF diagnosis.
Stroke Classification
Details on stroke identification and specific classification criteria for ischemic stroke in the ARIC24 and MESA25 cohorts have been described.
In ARIC, potential cases of stroke were identified from review of hospital records and death certificates.
Classification of stroke was then adjudicated by physicians with the assistance of a computerized algorithm utilizing validated criteria from the National Survey of Stroke by the National Institute of Neurological Disorders.
Strokes were classified as definite or probable thrombotic stroke, definite or probable cardioembolic stroke, definite or probable subarachnoid hemorrhage, definite or probable brain hemorrhage, and possible stroke of undetermined type.
The primary end point in our study was ischemic stroke, which included all thrombotic and cardioembolic strokes (definite and probable).
In MESA, potential stroke cases were identified from review of medical records and death certificates. Stroke was defined as a focal neurological deficit lasting 24 hours or until death or if the deficit lasted <24 hours and there was a clinically relevant lesion on brain imaging. Cases with focal neurological deficits secondary to brain trauma, tumor, infections, or other nonvascular cause were excluded.
Cases were physician adjudicated by members of the MESA study events committee. Strokes were subclassified as subarachnoid hemorrhage, intraparenchymal hemorrhage, other hemorrhage, brain infarction, or other stroke.
We included all nonhemorrhagic strokes in our analysis. Because of the relatively low number of stroke events, we elected to use a composite primary end point of stroke and transient ischemic attack (TIA) for the MESA analysis. In MESA, TIA was defined as ≥1 documented episode of focal neurological deficit lasting 30 seconds to 24 hours without brain imaging documenting stroke.
Assessment of Covariates
The covariates included in our analysis—age, sex, heart failure,26,27 hypertension,28,29 diabetes mellitus,29,30 myocardial infarction,27,31 stroke, TIA,32 and peripheral arterial disease27,33—were derived from data obtained during participant interviews, clinical examinations, and review of medical records as previously described. Prevalent (at baseline visit) and incident (during follow-up) variables were included.
Precise definitions were comparable in ARIC and MESA. All covariates in ARIC and MESA were ascertained at the time of AF diagnosis or at the most recent study visit examination before AF diagnosis.
AF Ascertainment
AF cases were ascertained by review of ECGs during study visits and hospital discharge records. AF associated with cardiothoracic surgery was not considered. Further details on specific ascertainment procedures in ARIC34 and MESA35have been previously described.
Anticoagulation Status
Use of anticoagulants was captured through participant interviews during each study visit in MESA and ARIC and annually in ARIC after 2006.
During the time period of our analysis in both ARIC and MESA, Coumadin was the only anticoagulant captured during participant interviews.
Echocardiography
2D and 3D echocardiography analysis protocols have been previously described.36,37 Briefly, 2D echocardiograms were performed using dedicated Philips iE33 Ultrasound systems with Vision 2011.
3D echocardiograms were performed using a dedicated Philips X3-1 transducer. Echocardiograms were analyzed at the Echocardiography Reading Center (Brigham and Women’s Hospital) in accordance with American Society of Echocardiography recommendations.
N-Terminal Pro B-Type Natriuretic Peptide Levels
N-terminal pro B-type natriuretic peptide levels were measured from participant plasma samples obtained during ARIC visit 5 using an electrochemiluminescent immunoassay on an automated Cobas e411 analyzer (Roche Diagnostics).
Statistical Analysis
We used the ARIC sample to derive a novel risk score for AF-related ischemic stroke prediction by incorporating PWIs with the CHA2DS2-VASc score variables. We used the MESA sample to validate our risk score.
In the ARIC sample, we used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% CIs of abnormal PWIs for ischemic stroke.
Person-years at risk were calculated from the date of AF ascertainment until the date of ischemic stroke, death not because of stroke, loss to follow-up, or end of follow-up, whichever occurred first.
We constructed 2 models for the association of each PWI and CHA2DS2-VASc variable with incident ischemic stroke in ARIC. Model 1 was an unadjusted model.
Model 2 was adjusted for remaining CHA2DS2-VASc variables: age, sex, heart failure, hypertension, diabetes mellitus, previous stroke/TIA, previous myocardial infarction, and peripheral arterial disease.
To estimate the predictive value of PWIs for stroke, we constructed 6 models for 1-year stroke risk that were used in our cohort of participants with incident AF.
Model A was constructed with the CHA2DS2-VASc score variables. Models B to E were constructed by adding aPWA, PPWD, aPTFV1, and aIAB to model A, respectively. Model F was constructed by adding all 4 PWIs to model A.
We evaluated model performance by calculating the C-statistic, categorical net reclassification improvement (NRI), and relative integrated discrimination improvement (IDI) using model A as the benchmark for comparison.
Reclassification categories were defined as <1%, 1% to 2%, and >2% 1-year risk of stroke. We used the Hosmer-Lemeshow χ2 statistic to evaluate model calibration and also compared observed to predicted stroke rates for score categories.
To create our risk score for 1-year stroke prediction, we screened our results for PWIs that resulted in meaningful improvement in risk reclassification and model discrimination.
To assign a point value for the candidate PWI, we compared the coefficient of the PWI with the coefficient of CHA2DS2-VASc variables in the same Cox proportional hazards model for ischemic stroke. We validated the new score in MESA.
To estimate the odds of structural heart disease for abnormal PWIs, we conducted a cross-sectional analysis in participants from visit 5 who underwent 2D and 3D echocardiograms.
We utilized logistic regression models to calculate odds ratios.
Model A was unadjusted.
Model B was additionally adjusted for age, sex, and race.
Data are presented as odds ratios (95% CI) for categorical variables and difference (95% CI) for continuous variables.
Finally, we calculated the C-statistic of the CHA2DS2-VASc score for 5-year ischemic stroke in participants without AF with abnormal PWIs and participants with AF.
For participants without AF, we used our baseline cohort (15 365 participants at visit 1). Baseline ECGs were used to evaluate PWIs.
For participants with AF, we utilized our cohort of participants with incident AF (2229 participants with incident AF). Participants were followed until their first ischemic stroke, death, or censorship.
The proportional hazards assumption was assessed with scaled Schoenfeld residuals for both graphical and numeric tests, time interaction terms, and inspection of log-negative log survival curves.
Model assumptions were not violated in any model. Statistical analysis was performed using SAS version 9.3 (SAS Institute Inc) and STATA 13.0 (StataCorp LP). To evaluate whether differences between baseline characteristics were statistically significant, a Student t test was used for continuous variables and a χ2 test was used for categorical variables. All P values reported were 2-sided, and statistical significance threshold was chosen as 0.05.
Results
In the ARIC sample, we identified 163 ischemic strokes over a mean follow-up time of 5.4 years after AF diagnosis.
There were 47 ischemic strokes within the first year after AF diagnosis. In the MESA sample, we identified 31 cases of stroke/TIA over a mean follow-up time of 3.3 years after AF diagnosis. There were ≤10 cases of stroke/TIA within the first year after AF diagnosis. Because of the Centers for Medicare and Medicaid Services sparse cell suppression policy, which stipulates that no cell (eg, admittances, discharges, patients, services) ≤10 may be displayed, we are unable to report the exact number of events in the first year after AF diagnosis.
The mean (SD) time period from ECG detection of aPWA to AF diagnosis in ARIC and MESA was 8.0 (5.3) and 4.4 (3.0) years, respectively.
The baseline characteristics of participants at the time of AF diagnosis in each study sample are listed in Table 1.
In the ARIC sample, participants who developed stroke were more likely to be women and to have prevalent heart failure, hypertension, myocardial infarction, stroke/TIA, peripheral arterial disease, aPWA, PPWD, aPTFV1, and aIAB.
There were no statistically significant differences in the CHADS2 and CHA2DS2VASc scores between participants who did and did not develop stroke.
In the MESA sample, participants who developed stroke were on average older, more likely to be women, and more likely to have prevalent hypertension, heart failure, stroke/TIA, and aPWA than MESA participants who did not develop stroke. Participants who developed stroke or TIA in MESA had higher CHADS2 and CHA2DS2-VASc scores compared with those who did not.
| Characteristic | ARIC | MESA | ||||
|---|---|---|---|---|---|---|
| All (N=2229) | No Stroke (N=2066) | Stroke (N=163) | All (N=700) | No Stroke/TIA (N=669) | Stroke/TIA (N=31) | |
| Age, y | 73±8 | 73±8 | 69±8‡ | 76±8 | 76±8 | 79±6† |
| Female sex | 1039 (47) | 955 (46) | 84 (52) | 315 (45) | 299 (45) | 16 (52) |
| Diabetes mellitus | 679 (30) | 622 (30) | 57 (35) | 129 (18) | ‖ | ‖ |
| Hypertension | 1661 (75) | 1533 (74) | 128 (79) | 476 (68) | 452 (68) | 24 (77) |
| Previous MI | 524 (24) | 485 (23) | 39 (24) | 40 (6) | 40 (6) | ‖ |
| Heart failure | 840 (38) | 774 (37) | 66 (41) | 55 (8) | ‖ | ‖ |
| PAD | 205 (9.0) | 181 (9) | 24 (15)† | 15 (2) | 15 (2) | ‖ |
| Past stroke/TIA | 326 (15) | 297 (14) | 29 (18) | 45 (6) | ‖ | ‖ |
| CHADS2 | 2.1±1.3 | 2.0±1.3 | 2.1±1.4 | 1.6±1.0 | 1.6±1.0 | 2.1±1.2‡ |
| CHA2DS2VASc | 3.6±1.7 | 3.6±1.7 | 3.7±1.9 | 3.0±1.3 | 3.0±1.3 | 3.6±1.4† |
| Race | ||||||
| Black | 414 (19) | 375 (18) | 39 (24) | 142 (20) | ‖ | ‖ |
| White | 1815 (81) | 1691 (82) | 124 (76) | 342 (49) | 323 (48) | 19 (61) |
| Chinese | § | § | § | 94 (13) | 92 (14) | ‖ |
| Hispanic | § | § | § | 122 (17) | 115 (17) | ‖ |
| aPWA | 529 (24) | 472 (23) | 57 (35)‡ | 82 (12) | 74 (11) | ‖ |
| PWA, degrees | 51±26 | 51±26 | 49±30 | 50±29 | 50±28 | 40±47 |
| PPWD | 921 (41) | 851 (41) | 70 (43) | 157 (22) | ‖ | ‖ |
| PWD, ms | 113±18 | 112±18 | 115±21 | 110±22 | 110±20 | 99±41 |
| aPTFV1 | 670 (30) | 617 (30) | 53 (33) | 160 (23) | ‖ | ‖ |
| PTFV1, μV*ms | 2492±2240 | 2471±2218 | 2762±2491 | 2675±2205 | 2704±2222 | 2059±1707 |
| aIAB | 99 (4.4) | 81 (3.9) | 18 (11)‡ | § | § | § |
| Current smoker | 465 (21) | 434 (21) | 31(19) | 53 (7) | ‖ | ‖ |
| COPD | 248 (11) | 224 (11) | 24 (15) | 34 (5) | ‖ | ‖ |
aIAB indicates advanced interatrial block; aPTFV1, abnormal P-wave terminal force in V1; aPWA, abnormal P-wave axis; MI, myocardial infarction; PAD, peripheral arterial disease; PPWD, prolonged P-wave duration; and TIA, transient ischemic attack.
*Data are presented as n (%) or mean±SD.
†P value <0.05 from χ2 tests or t tests for comparisons of those who did or did not experience an incident or recurrent stroke or TIA (MESA) during follow-up.
‡P value <0.01 from χ2 tests or t tests for comparisons of those who did or did not experience an incident or recurrent stroke or TIA (MESA) during follow-up.
§Variable unavailable in ARIC/MESA database.
‖Suppressed because of small cell size in accordance with the Centers for Medicare and Medicaid sparse cell suppression policy.
Table 2 lists the results of our Cox proportional hazards models that were used to estimate the association between abnormal PWIs and ischemic stroke in the ARIC cohort. aPWA and aIAB were associated with increased risk of ischemic stroke in the unadjusted model (model 1). These associations remained significant after adjustment for the individual CHA2DS2-VASc variables (model 2). PPWD and aPTFV1 were not associated with increased risk of ischemic stroke in either model.
| Variable | Model 1* | Model 2* | ||
|---|---|---|---|---|
| HR (95% CI) | P | HR (95% CI) | P | |
| Abnormal P-wave axis | 1.92 (1.40–2.66) | <0.0001 | 1.88 (1.36–2.61) | 0.0001 |
| Prolonged P-wave duration | 1.04 (0.76–1.42) | 0.82 | 0.97 (0.71–1.33) | 0.83 |
| Abnormal P-wave terminal force in V1 | 1.27 (0.91–1.76) | 0.15 | 1.08 (0.77–1.51) | 0.67 |
| Advanced interatrial block | 3.22 (1.98–5.27) | <0.0001 | 2.93 (1.78–4.81) | <0.0001 |
| Age | 1.01 (0.98–1.03) | 0.67 | 0.99 (0.97–1.02) | 0.59 |
| Male sex | 0.79 (0.58–1.07) | 0.13 | 0.90 (0.65–1.23) | 0.49 |
| Stroke/transient ischemic attack | 1.57 (1.05–2.35) | 0.03 | 1.40 (0.93–2.10) | 0.10 |
| Heart failure | 1.66 (1.21–2.28) | 0.002 | 1.48 (1.07–2.05) | 0.02 |
| Hypertension | 2.00 (1.37–2.92) | 0.0003 | 1.80 (1.22–2.66) | 0.003 |
| Diabetes mellitus | 1.72 (1.24–2.38) | 0.001 | 1.49 (1.07–2.08) | 0.02 |
| Myocardial infarction | 1.20 (0.84–1.72) | 0.32 | 1.01 (0.69–1.46) | 0.97 |
| Peripheral arterial disease | 1.94 (1.26–2.99) | 0.003 | 1.89 (1.22–2.93) | 0.004 |
HR indicates hazard ratio.
*Model 1: Unadjusted Cox proportional hazards model. Model 2: Cox proportional hazards model adjusted for remaining CHA2DS2VASc variables of age, sex, stroke/transient ischemic attack, heart failure, hypertension, diabetes mellitus, myocardial infarction, and peripheral artery disease.
Table 3 lists our analysis of stroke-risk prediction model discrimination, risk reclassification, and calibration. Model A, which was constructed with the CHA2DS2-VASc variables, served as our benchmark for stroke prediction. aPWA was the only PWI that resulted in meaningful improvement in discrimination and risk reclassification. For 1-year stroke risk, the addition of aPWA (model B) to model A improved the C-statistic (95% CI) from 0.659 (0.574–0.743) to 0.738 (0.663–0.814) corresponding to a categorical NRI (95% CI) of 0.266 (0.084–0.442) and a relative IDI (95%) of 0.773 (0.474–1.131). The addition of all PWIs to model A (model F) improved the C-statistic (95% CI) from 0.659 (0.574–0.743) to 0.749 (0.678–0.820) corresponding to a categorical NRI (95% CI) of 0.374 (0.215–0.521) and a relative IDI (95% CI) of 1.04 (0.652–1.52). All models were well calibrated for 1-year stroke risk.
| Model‡ | C-Statistic (95% CI) | χ2 (P Value)* | NRI (95% CI)† | Relative IDI (95% CI) |
|---|---|---|---|---|
| A | 0.659 (0.574–0.743) | 4.7 (0.86) | ||
| B | 0.738 (0.663–0.814) | 10.4 (0.32) | 0.266 (0.084–0.442) | 0.773 (0.474–1.131) |
| C | 0.659 (0.575–0.743) | 4.7 (0.86) | 0.001 (−0.001 to 0.002) | 0.001 (−0.001 to 0.002) |
| D | 0.668 (0.586–0.750) | 3.1 (0.96) | −0.052 (−0.151 to 0.026) | 0.067 (−0.010 to 0.138) |
| E | 0.666 (0.581–0.751) | 8.5 (0.48) | 0.072 (−0.040 to 0.184) | 0.239 (0.050–0.498) |
| F | 0.749 (0.678–0.820) | 5.1 (0.82) | 0.374 (0.215–0.521) | 1.04 (0.652–1.52) |
IDI indicates integrative discrimination improvement; and NRI, net reclassification index.
*Hosmer-Lemeshow χ2 statistic.
†For categorical NRI, we used the following categories for 1-year stroke risk: <1%, 1% to <2%, and
≥2% (based on 47 cases).
Model A: age, sex, stroke/transient ischemic attack, heart failure, hypertension, diabetes mellitus, myocardial infarction, and peripheral artery disease.
Model B: model A+abnormal P-wave axis.
Model C: model A+prolonged P-wave duration.
Model D: model A+abnormal P-wave terminal force in V1.
Model E: model A+advanced interatrial block. Model F: model A+abnormal P-wave axis, prolonged P-wave duration, abnormal P-wave terminal force in V1, and advanced interatrial block.
Based on our findings that aPWA was the only PWI that significantly improved the prediction of ischemic stroke, we constructed a new risk score by incorporating aPWA into the CHA2DS2-VASc score. In model B, the β estimates of aPWA and CHA2DS2-VASc score, modeled as a continuous variable, for 1-year stroke risk were 0.582 and 0.262, respectively.
Thus, aPWA was assigned a value of 2 points to create the P2-CHA2DS2-VASc score—aPWA (2 points), age (1 point for 65–74, 2 points for ≥75), sex (1 point for female), heart failure (1 point), hypertension (1 point), diabetes mellitus (1 point), previous myocardial infarction/peripheral artery disease (1 point), and prevalent stroke/TIA (2 points).
In the ARIC sample, the CHA2DS2-VASc score had modest discrimination for 1-year stroke risk when assessed by C-statistic (0.60; 95% CI, 0.51–0.69). The P2– CHA2DS2-VASc demonstrated superior model discrimination assessed by C-statistic (0.67; 95% CI, 0.60–0.75) and IDI (1.19; 95% CI, 0.96–1.44).
This corresponded to a substantial categorical NRI (0.25; 95% CI, 0.13–0.39; Table 4).
Compared with its performance in ARIC, in the MESA sample, the CHA2DS2-VASc had better discrimination assessed by C-statistic (0.68; 95% CI, 0.52–0.84). The P2-CHA2DS2-VASc again demonstrated superior discrimination properties assessed by C-statistic (0.75; 95% CI, 0.60–0.91) and relative IDI (0.82; 95% CI, 0.36–1.39). This corresponded to a substantial categorical NRI (0.51; 95% CI, 0.18–0.86; Table 4).
| Study | Score | C-Statistic (95% CI) | NRI (95% CI)* | Relative IDI (95% CI) |
|---|---|---|---|---|
| ARIC | CHA2DS2VASc† | 0.60 (0.51–0.69) | ||
| P2-CHA2DS2VASc‡ | 0.67 (0.60–0.75) | 0.25 (0.13–0.39) | 1.19 (0.96–1.44) | |
| MESA | CHA2DS2VASc† | 0.68 (0.52–0.84) | ||
| P2-CHA2DS2VASc‡ | 0.75 (0.60–0.91) | 0.51 (0.18–0.86) | 0.82 (0.36–1.39) |
IDI indicates integrated discrimination improvement; and NRI, net reclassification improvement.
*For categorical NRI, we used the following categories for stroke risk: <1%, 1% to <2%, and ≥2%.
†Age (1 point for >65, 2 points for >75 years), sex (1 point for female), heart failure (1 point), hypertension (1 point), diabetes mellitus (1 point), previous myocardial infarction/peripheral artery disease (1 point), and prevalent stroke/transient ischemic attack (2 points).
‡CHA2DS2VASc+abnormal P-wave axis (2 points).
Table 5 lists our analysis of 1-year stroke-risk reclassification using the P2-CHA2DS2-VASc score compared with the CHA2DS2-VASc score. In ARIC participants who developed stroke within 1 year of AF diagnosis, 14% were correctly reclassified to higher risk categories, whereas 6% were incorrectly classified to lower risk categories. In those who did not develop stroke, 20.9% were correctly reclassified to lower risk categories, whereas 5.3% were incorrectly reclassified to higher risk categories. In MESA participants who developed stroke within 1 year of AF diagnosis, 33.3% were correctly reclassified to higher risk categories, and no participants were incorrectly reclassified to lower risk categories. In those who did not develop stroke, 8.5% were incorrectly reclassified to higher risk categories, whereas 26.3% were correctly reclassified to lower risk categories.
| CHA2DS2VASc‡ | P2-CHA2DS2VASc§ | CHA2DS2VASc‡ | P2-CHA2DS2VASc§ | ||||||
|---|---|---|---|---|---|---|---|---|---|
| <1% | 1% to 2% | >2% | Total | <1% | 1% to 2% | >2% | Total | ||
| <1% | 19 | 3* | 0* | 22 | <1% | 290 | 22† | 0† | 312 |
| 1% to 2% | 3† | 9 | 4* | 16 | 1% to 2% | 167* | 686 | 94† | 947 |
| >2% | 0† | 0† | 9 | 9 | >2% | 0* | 290* | 633 | 923 |
| Total | 22 | 12 | 13 | 47 | Total | 457 | 998 | 727 | 2182 |
| Participants with stroke in 1 y | Participants without stroke in 1 y |
IDI indicates integrative discrimination improvement; NRI, net reclassification index; and TIA, transient ischemic attack.
*Favorable reclassification.
†Unfavorable reclassification.
‡Age (1 point for >65, 2 points for >75), sex (1 point for female), heart failure (1 point), hypertension (1 point), diabetes mellitus (1 point), previous myocardial infarction/peripheral artery disease (1 point), and prevalent stroke/transient ischemic attack (2 points).
§CHA2DS2VASc+abnormal P-wave axis (2 points).
Figure 2 depicts the observed and predicted 1-year stroke risk for P2-CHA2DS2-VASc score categories in ARIC and MESA. The score was well calibrated in ARIC. Given the low number of events in MESA, some variation between observed and predicted risk is to be expected. A 2% annual stroke risk, which is the threshold for anticoagulation according to the 2014 American Heart Association/American College of Cardiology/Heart Rhythm Society2 practice guidelines, corresponded to a score of 4 to 5.
The association of PWIs with structural heart disease is displayed in Table 6. After adjustment for age, sex, and race, all abnormal PWIs were independently associated with left atrial enlargement, greater left ventricular mass, and greater left ventricular end diastolic dimension. PPWD, aPWA, and aIAB were associated with higher N-terminal pro b-type natriuretic peptide levels. PPWD, aPTFV1, and aPWA were associated with lower left ventricular ejection fraction, aPWA and PPWD were associated with lower left atrial emptying fraction, and aPWA was associated with lower left atrial global longitudinal strain.
| Structural Heart Disease Variable* | Model† | aPWA | PPWD | aPTFV1 | aIAB |
|---|---|---|---|---|---|
| Left atrial volume index >34 mL/m2 | A | 1.74 (1.50–2.01)§ | 2.16 (1.86–2.50)§ | 1.57 (1.36–1.82)§ | 2.18 (1.61–2.97)§ |
| B | 1.69 (1.46–1.96)§ | 1.93 (1.66–2.24)§ | 1.49 (1.29–1.74)§ | 1.79 (1.31–2.44)§ | |
| Left ventricular mass index >115 g/m2 for men, >95 g/m2 for women | A | 1.20 (1.01–1.41)‡ | 1.94 (1.65–2.29)§ | 1.69 (1.43–1.99)§ | 2.35 (1.70–3.26)§ |
| B | 1.19 (1.01–1.41)‡ | 2.06 (1.74–2.43)§ | 1.67 (1.42–1.98)§ | 2.39 (1.71–3.33)§ | |
| Left ventricular end diastolic diameter >5.8 cm for men, >5.2 cm for women | A | 1.43 (1.03–1.98)‡ | 2.45 (1.75–3.42)§ | 1.72 (1.24–2.38)§ | 2.26 (1.23–4.14)§ |
| B | 1.43 (1.03–1.99)‡ | 2.75 (1.96–3.87)§ | 1.75 (1.26–2.43)§ | 2.71 (1.46–5.01)§ | |
| Left ventricular ejection fraction <52% for men, <54% for female | A | 1.28 (1.05–1.57)‡ | 1.34 (1.10–1.64)§ | 1.46 (1.19–1.79)§ | 1.21 (0.75–1.96) |
| B | 1.20 (0.98–1.48) | 1.20 (0.98–1.48) | 1.33 (1.08–1.63)§ | 1.08 (0.66–1.76) | |
| NT-pro BNP pg/mL, difference (95% CI) | A | 203.6 (156.1–251.2)§ | 92.2 (45.6–138.9)§ | 52.0 (3.51–100.5)‡ | 279 (160–399)§ |
| B | 197.1 (149.7–244.5)§ | 67.8 (20.6–115.0)§ | 38.9 (–9.68 to 87.5) | 230 (111–350)§ | |
| LAGLS %, difference (95% CI) | A | −1.65 (−2.43, −0.87)§ | −1.10 (−1.86, −0.33)§ | 0.15 (−0.63, 0.94) | −1.28 (−3.41, 0.84) |
| B | −1.40 (−2.16, −0.63)§ | −0.53 (−1.30, 0.25) | 0.46 (−0.31, 1.23) | −0.38 (−2.49, 1.73) | |
| LAEF %, difference (95% CI) | A | −3.23 (−4.69, −1.78)§ | −2.29 (−3.72, −0.85)§ | −0.49 (−1.95, 0.97) | −3.18 (−7.15, 0.80) |
| B | −2.85 (−4.29, −1.40)§ | −1.57 (−3.03, −0.10)‡ | −0.03 (−1.48, 1.42) | −1.97 (−5.95, 2.00) |
aIAB indicates advanced interatrial block; aPTFV1, abnormal P-wave terminal force in V1; aPWA, abnormal P-wave axis; LAEF, left atrial emptying fraction; LAGLS, left atrial global longitudinal strain; NT-pro BNP, N-terminal pro b-type natriuretic peptide; and PPWD, prolonged P-wave duration.
*Data displayed as odds ratio (95% CI) unless otherwise stated. Difference is the difference in number of units (pg/mL for NT-pro BNP and % for LAGLS and LAEF) between participants with an abnormal PWI and participants with a normal PWI (eg, compared with participants with normal PWA, those with abnormal PWA have higher NT-pro BNP and lower LAGLS and LVEF).
†Logistic regression models. Model A is unadjusted. Model B is adjusted for age, sex, and race.
‡P<0.05.
§P<0.01.
The predictive value of the CHA2DS2-VASc score for 5-year ischemic stroke risk is listed in Table I in the online-only Data Supplement. The CHA2DS2-VASc score had a C-statistic of 0.682 (0.617–0.748) and 0.636 (0.577–0.695) in participants without AF with any abnormal PWI and participants with AF, respectively.
More information: Devin L. Brown et al, Electrocardiomatrix Facilitates Accurate Detection of Atrial Fibrillation in Stroke Patients, Stroke (2019).DOI: 10.1161/STROKEAHA.119.025361
Journal information: Stroke
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