P-wave duration is a predictor for long-term mortality in post-CABG patients
P-wave duration is a predictor for long-term mortality in post-CABG patients
Sheila Tatsumi Kimura-Medorima 0 1 2
Ana Paula Beppler Lazaro Lino 0 1 2
Marcel P. C. Almeida 0 1 2
Marcio J. O. Figueiredo 0 1 2
Lindemberg da Mota Silveira-Filho 0 2
Pedro Paulo Martins de Oliveira 0 2
Otavio Rizzi Coelho 0 1 2
JoseÂ Roberto Matos Souza 0 1 2
Wilson Nadruz 0 2
Orlando Petrucci 0 2
Andrei C. Sposito 0 1 2
0 Abbreviations: AF , Atrial Fibrillation; CABG, Coronary Artery Bypass Grafting; CAD, Coronary Artery Disease; CCD , Congestive Cardiac Disease
1 Cardiology Division, State University of Campinas (Unicamp) , Campinas, São Paulo , Brazil , 2 Surgery Department, State University of Campinas (Unicamp) , Campinas, São Paulo , Brazil
2 Editor: Elena Cavarretta, Universita degli Studi di Roma La Sapienza , ITALY
Risk stratification in secondary prevention has emerged as an unmet clinical need in order to mitigate the Number-Needed-to-Treat and make expensive therapies both clinically relevant and cost-effective. P wave indices reflect atrial conduction, which is a sensitive marker for inflammatory, metabolic, and pressure overload myocardial cell remodeling; the three stimuli are traditional mechanisms for adverse clinical evolution. Accordingly, we sought to investigate the predictive role of P-wave indices to estimate residual risk in patients with chronic coronary artery disease (CAD). The cohort included 520 post-Coronary Artery Bypass Grafting patients with a median age of 60 years who were followed for a median period of 1025 days. The primary endpoint was long-term all-cause death. Cubic spline model demonstrated a linear association between P-wave duration and incidence rate of long-term all-cause death (p = 0.023). P-wave >110ms was a marker for an average of 425 days shorter survival as compared with P-wave under 80ms (Logrank p = 0.020). The Cox stepwise regression models retained P-wave duration as independent marker (HR:1.37; 95%CI:1.05±1.79,p = 0.023). In conclusion, the present study suggests that P-wave measurement may constitute a simple, inexpensive and accessible prognostic tool to be added in the bedside risk estimation in CAD patients.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
In primary prevention setting, cardiovascular risk stratification is largely accepted as an
approach to select individuals in whom medical attention must be intensified[1±3]. Similar
to the primary spectrum, individuals at secondary prevention present a broad range of
cardiovascular risk; however, worldwide guidelines classify them as a single high-risk category[
Recent evidences suggest the use of risk stratification as a strategy to mitigate the
NumberNeeded-to-Treat and make expensive therapies both clinically relevant and cost-effective[
Hence, risk stratification in secondary prevention has emerged as a paramount and unmet
COPD, Chronic Obstructive Pulmonary Disease;
ECG, Electrocardiogram; EuroSCORE, European
system for cardiac operative risk evaluation; GFR,
Glomerular filtration rate; IDI, Integrated Diagnostic
Improvement; IQR, Interquartile range; MDRD,
Modification of Diet in Renal Disease Study
equation; MRI, Magnetic Resonance Imaging; NRI,
Net Reclassification Improvement; PASP,
Pulmonary Artery Systolic Pressure; PCI,
Percutaneous Coronary Intervention.
Residual risk stratification in individuals with coronary artery disease (CAD) has highlighted
undertreated conditions such as diabetes or dyslipidemia or the presence of target-organ
injuries. In individuals under optimal medical treatment, various biomarkers and the use of
cardiovascular imaging exams have predicted the residual risk[7±9]. Nevertheless, most of these
methods are not routinely performed in clinical practice even in developed countries.
Recently, both cardiovascular and all-cause mortality were shown to be predictable by
Pwave duration in a robust clinical cohort. The atrial delayed-conduction reflects
inflammatory and metabolic cell remodeling that antecedes noticeable atrial enlargement, whose main
stimulus is chronic pressure overload[11, 12]. In animal models and autopsies, P-wave
duration relates to early histological signs of fibrosis and inflammation[12, 13]. Further into the
atrial overload phase, atrial structural and functional changes identified by MRI still correlate
with the P-wave changes. Hence, we sought to investigate the predictive role of P-wave
indices in the estimation of residual risk of patients with stable CAD who underwent to
coronary bypass grafting (CABG). Our findings support the use of this simple affordable tool in
Between 2007 and 2013, we collected data from 520 consecutive patients who underwent
CABG during their hospitalization at the Clinics Hospital of the State University of Campinas
(HC-UNICAMP), Brazil. We included patients who underwent isolated completed CABG and
excluded those who required other concomitant surgical procedure such as valve replacement
and ventricular geometric reconstruction to reduce heterogeneity. These patients were selected
for having complete myocardial revascularization and therefore equally asymptomatic for
myocardial ischemic disease. In addition, these exclusion criteria were created with the
purpose of homogenizing the population studied and concentrating the outcomes of coronary
origin. The study flow chart is presented in Fig 1. The cohort recruitment for this observational
study was determined to allow a minimum of 645-days follow-up period, so the vital status
was accessed by telephone. All enrolled patients gave permission to participate signing the
informed consent and the Institutional Ethics Committee approved this study (CAAE
Nr.0828.0.146.000±10); identity and personal data are confidential.
All clinical data were measured, except for ethnic group that was self-reported. The twelve
leads ECG were performed within one week from the CABG and were manually analyzed
about rhythm and P-wave indices (duration, amplitude and dispersion). P-wave dispersion
was calculated by subtracting the maximum and minimum P-wave durations in any of the
twelve ECG leads, while exams measured P-wave duration and amplitude in lead II. We
decided to use the lead II due to the fact that this lead often presents the largest P-wave
duration. The paper speed used was 25 mm/sec. Two experienced cardiologists (STKM and
APBLL) who were blinded to the patients clinical status used manual caliper for measuring
Pwave duration and had an intra-observer correlation coefficient of 0.549, p = 0.002 and 0.759,
p<0.0001; and inter-observer correlation coefficient of 0.735, p<0.0001. The hand held caliper
measurement were confirmed in a subset of patients by the use of electronic digital paquimeter
and we found an agreement of 95.7% (standard deviation 0.133) and 98.5% (SD 0.097),
respectively. The above-mentioned P-wave measurements and their reference values are shown in
Laboratory analyses included automated blood cell count, urea (kinetic U.V. test),
creatinine (Jaffe method with compensation, kinetic colorimetric test) and electrolytes (ion-selective
electrode). Glomerular filtration rate (GFR) was estimated with MDRD formula.
Experienced physicians analyzed coronarography; a significant lesion was considered if more than
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Fig 1. Study flow chart.
70% stenosis. Echocardiography analysis followed guidelines. Chambers diameters
measured in parasternal long axis M-mode. Left ventricular mass was measured according to the
cube formula and left ventricular hypertrophy was considered if the mass was over 95g/m2 in
women or 115g/m2 in men. Ejection fraction reported as continuous variable was
estimated by Teicholz method or by Simpson method when left ventricular wall motion
abnormalities were observed.
The primary endpoint was long-term mortality; otherwise follow-up was censored at the
last outpatient visit registered by the hospital system. Descriptive statistics of continuous and
categorical data are expressed as the median and 25th and 75th percentile or frequencies and
percentages, respectively. Univariate comparisons before matching and correlations among
Pwave indices used Mann-Whitney test and linear regression, as appropriate. Furthermore, we
applied Cox regression analysis to investigate the predictive relevance of ECG parameters to
the time to all-cause mortality. Log rank tested unadjusted mortality differences according to
P-wave duration quartiles in Survival Kaplan-Meier plots. Covariates included in multivariable
models were pre-selected from a stepwise model, and included sex, age, and diabetes.
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Fig 2. P-wave measurements and reference values.
Spline-based model curve assessed the predictive capacity of P-wave duration for long-term
all-cause death, adjusting for age, sex, diabetes, chronic obstructive pulmonary disease, stroke,
two or more CABGs, creatinine, prior acute coronary syndrome episode, ejection fraction,
PASP, myocardial infarction in the previous 90 days. Finally, the sample size provided
posthoc power of 100% to the primary endpoint (reference population according to Bradshaw
et al.). Significance level was a two-sided p-value<0.05. Analyses were performed using
the Statistical Package for Social Sciences, version 21.0, software (IBM Corp, Armonk, NY)
and STATA version 14.0 (StataCorp, College Station, TX).
PLOS ONE | https://doi.org/10.1371/journal.pone.0199718
No / median % / IQR
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No / median % / IQR
multiple linear regression analyses using stepwise method and including variables associated
to each P-wave indices (model entry P = 0.05, removal P = 0.10). Multivariable linear
regression model for clinical correlates for each P-wave indices is detailed in Table 2.
Thereafter the Cox proportional hazards regression analyses were performed to identify
independent predictors of long-term mortality, including ECG parameters (Table 3). The
categorical variable ªP-wave 110msº was an independent predictor of long-term all-cause
mortality, adjusted for age, sex and diabetes (HR = 1.40, 95%CI = 1.02±1.91, p = 0.036). The
Cox proportional hazards regression multivariable analyses incorporating stepwise
regression models including all covariables described above and all three P-wave indices (model
entry p = 0.05, removal p = 0.10) retained the P-wave duration, two or more CABGs, prior
acute coronary syndrome episode, pulmonary arterial systolic pressure (PASP) >60mmHg
and diabetes in the final model (model p-value <0.0001, retained variables in Table 4). In a
Spline curve model we observed a linear association between P-wave duration and the
incidence rate of long-term all-cause death for 100-patients-years in a fully adjusted model with
trend p-value = 0.023 (Fig 4). We obtained similar results when adjusting only for age, sex
and diabetes (trend p-value = 0.029). Also, history of paroxysmal atrial fibrillation occurred
in 18 (3.5%) before surgery but it had no interaction in the association between P-wave
duration and survival.
The study was designed to investigate the clinical value of using regular ECG for estimating
the residual risk in individuals with stable CAD. Our main finding points an increase of 37%
in the long-term relative risk of all-cause death for each mm of increase in P-wave duration.
P-wave duration might reflect the electrical remodeling of the atria and is a predictor of
death, atrial fibrillation or heart failure hospitalization in a large spectrum of patients,
including those post-CABG . This simple parameter is a potential marker of atrial overload
preceding clinical event and is histologically correlated to the extent of fibrosis and fatty
infiltration in atrial tissues. Interestingly, the Bachmann's bundle and terminal crest were
the most affected areas, suggesting that these areas play a major role of inter- and intra atrial
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Fig 3. Kaplan-Meier curves for all-cause mortality, according to the P-wave indices (quartiles).
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conduction on prolongation of P-wave duration. In animal model, prolonged P-wave
duration was related to abnormal inter-atrial conduction, independent of the left atrium size,
mediated by dysregulation of connexin proteins expression (CX 40 and CX 43) and fibrosis
. In patients with acute coronary syndromes, it has been found an increased number of
inflammatory cells infiltrate the atria, coming from the adipose tissue, suggesting that the left
ventricular infarction induces atrial inflammation . By inference, it is possible that the
prolonged P-wave represents an early sign of chronic or acute inflammatory stimuli on atrial
P-wave duration of 154ms has been show to convey a three-fold all-cause adjusted mortality
risk in a long-term prospective cohort in the general population. In our study P-wave
duration over 110ms was associated to all-cause adjusted long-term mortality, suggesting that
this parameter should be considered in patients with CAD. In contrast, in the only prior study
with post-CABG patients, P-wave duration was not related to mortality, however PR interval
was a significant predictor of death after adjustment for confounders. Since the three
studies had prolonged clinical follow-up and adequate statistical power, the divergence of results
should be due to the differences in severity of the residual risk in patients enrolled, observed
for instance by the incidence of diabetes in our population (42%, compared to 27% in Lauer
et al. ). In fact, P-wave was a risk marker in both studies with individuals at less severe
cardiovascular risk. In the study with individuals at greater overall risk, P-wave was not a
predictor but rather electrocardiographic signals compatible with more advanced cardiac structural
alterations such as left ventricular overload.
Diastolic dysfunction is a marker of common pathophysiologic process related to
longterm pressure overload and cardiac remodeling, thus P-wave duration could reflect insults
from clinical or subclinical diseases and act as a noninvasive barometer of clinical status.
Diastolic dysfunction was significantly associated to P-wave amplitude and P-wave dispersion
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in our study, but did not reach significance for P-wave duration, possibly due to qualitative
assessment of diastolic function instead of quantitative measurements, such as E/e' index for
In spite of the fact that the P-wave indices are easily obtained the main limitation is related
to measurement techniques. It was already demonstrated that hand-held calipers
measurements have less accuracy compared with digital measurements[
]. However, the present
1 Stepwise regression models including all variables from EuroSCORE I and all three P-wave indices (model entry
p = 0.05, removal p = 0.10)
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Fig 4. Spline model predictiveness curve for P-wave duration and the risk prediction for all-cause death. The analysis was adjusted for age, sex, diabetes, chronic
obstructive pulmonary disease, stroke, two or more previous CABG, creatinine, previous acute coronary syndrome, ejection fraction, pulmonary artery systolic
study reflects the real world where most of the ECG is manually examined. Besides the possible
error of measurement our data reached statistical relevance, reflected previous findings and
clinical correlates, demonstrating its feasibility as a prognostic tool. We only assessed vital
status and we were not able to verify differences between cardiovascular and
non-cardiovascular death due to sample size and adjudication limitations. Since atrial fibrillation is often an
asymptomatic arrhythmia, we weren't able estimate the association between P-wave duration
and the incidence of this arrhythmia post-discharge. P-wave axis was recently demonstrated as
another potential marker for high-risk post-CABG patients[
]. This finding was not available
when the present study was designed and we are able to verify the additive value of these two
P-wave markers. Finally, we did not perform echocardiographic evaluation of patients
enrolled. Thus, future studies are required to better characterize the nature of this association.
In secondary prevention setting the balance between the residual risk and the use of
expensive new therapeutic options is a daily concern for clinicians worldwide. So far, very few
studies have dedicated to identify variables that may potentially be useful for such risk
discrimination, particularly those subclinical, non-invasive and non-expensive. In the present study,
we found that P-wave duration may represent one of these variables and having all the
above10 / 13
mentioned features, we believe it must be considered in prospective multivariate modeling for
generating risk algorithms in CAD patients. Meanwhile, in bedside clinical practice, finding a
P-wave 110ms must be taken as a warning sign.
S1 File. Figure A. Linear regression between P-wave duration and dispersion. Table A.
Clinical correlates of P-wave indices.
Conceptualization: Sheila Tatsumi Kimura-Medorima, Marcio J. O. Figueiredo, Andrei C.
Data curation: Sheila Tatsumi Kimura-Medorima, Ana Paula Beppler Lazaro Lino, Marcel P.
Formal analysis: Andrei C. Sposito.
Investigation: Marcel P. C. Almeida, Pedro Paulo Martins de Oliveira, JoseÂ Roberto Matos
Methodology: Ana Paula Beppler Lazaro Lino, Marcel P. C. Almeida, JoseÂ Roberto Matos
Project administration: Andrei C. Sposito.
Supervision: Marcio J. O. Figueiredo, Lindemberg da Mota Silveira-Filho, Pedro Paulo
Writing ± original draft: Sheila Tatsumi Kimura-Medorima.
Writing ± review & editing: Lindemberg da Mota Silveira-Filho, Otavio Rizzi Coelho, JoseÂ
Roberto Matos Souza, Wilson Nadruz, Jr, Orlando Petrucci, Jr, Andrei C. Sposito.
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