EDITORIAL COMMENTARY
Risk assessment for atrial fibrillation: Enter the P-wave Jared W. Magnani, MD, MSc, FAHA From the National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, Boston University School of Medicine, Boston, Massachusetts. The increased attention to risk prediction of atrial fibrillation (AF) is well deserved. AF is associated with multiple adverse outcomes and markedly increased health care utilization. A growing list of diverse exposures has been assessed for their relation to AF, but incorporating the findings into clinical practice is a challenge. Four distinct questions may assist in approaching the clinical and epidemiological literature and identifying the relevance and significance of such myriad investigations into AF risk assessment. First, and most evident, what is the proximity of the exposure to AF pathogenesis? Biological and pathophysiological mechanisms are complex, and AF is a heterogeneous syndrome. Associations that reach statistical significance may reflect unmeasured or unidentified pathophysiology or residual confounding. Epidemiological studies inform us of associations and have limited ability to dissect mechanisms; terms such as predict or mediate inform us of statistical relations, rather than biology or pathophysiology—hence the relevance of evaluating the proximity of the risk factor to mechanisms that may convey risk for AF arrhythmogenesis. Second, how accessible is a putative risk factor for measurement in clinical practice? There is a large literature relating diverse biomarkers to AF. However, biomarkers may be esoteric and not widely available for measurement. The American Heart Association’s statement on the evaluation of markers suggests assessing cost and accessibility as practical aspects in evaluating risk factors.1 Risk assessment may inform us of potential pathways and suggest mechanisms, but have limited “real-world” application. Third, can the putative AF risk factor be addressed in clinical practice? The Heart Rhythm Society consensus document on AF treatment and prevention emphasized “early identification and treatment of modifiable risk factors.”2 Whether a newly identified risk marker can be used to modify AF risk is fundamental metric for gauging its practical significance, no matter how small the P value. A fourth and final question is: What is the additional contribution of a potential risk factor to AF risk Dr Magnani is supported by the National Institutes of Health (grant no. R03AG045075) and a Boston University Evans Department of Medicine Career Investment Award. Address reprint requests and correspondence: Dr Jared W. Magnani, National Heart, Lung, and Blood Institute’s Framingham Heart Study, Section of Cardiovascular Medicine, Boston University School of Medicine, Boston Medical Center, 88 E Newton St, Boston, MA 02118. E-mail address: [email protected].
1547-5271/$-see front matter B 2015 Heart Rhythm Society. All rights reserved.
prediction? Statistical approaches such as discrimination and calibration3 provide a stringent evaluation of the incremental contributions of a risk factor toward AF, as demonstrated by community-based studies in AF risk score development. The work by Nielsen et al4 may be evaluated in this broader context of AF risk assessment. The investigators performed a prospective observational analysis of an ambulatory patient cohort residing in Copenhagen, Denmark, and related median P-wave duration to AF risk and secondary end points of cardiovascular mortality and ischemic stroke. Importantly, P-wave duration was quantified at a core facility using digitized tracings analyzed by contemporary software and was informatively evaluated as both a continuous and a categorical variable. AF was ascertained from International Classification of Diseases coding from national registries. The study’s central finding is that the P wave, though small in amplitude, had a very strong signal in relation to AF. The large size of this clinically based cohort (n ¼ 285,933) facilitated assessing 7 categories of P-wave duration. The analysis identified a convincing U-shaped relation between P-wave duration and AF: the lower and upper 5th percentiles of P-wave duration had a 1.6- to 2-fold increased risk of AF relative to the reference range (40th–60th percentile). The findings were consistent, even though not as strong, for both cardiovascular death and risk of ischemic stroke. Splines demonstrated similarly compelling associations between P-wave duration and AF. The work by Nielsen et al has noteworthy strengths. First, the size of the participant sample, derived from an urban center, increases the statistical power and validity of the findings. At the extremes of P-wave duration, the distributions are comparable to a large community-based cohort examination relating P-waves to AF.5 Second, using a core facility and computerized measurement boosts the reliability of the study. There has been ample literature using manually derived P-waves6; in contrast, automated measurement makes quantitative electrocardiography possible in a sample of this magnitude, conveys strong reliability, and provides the median value that could not otherwise be obtained. Third, evaluating the P-wave as a categorical and continuous measure enhances our understanding of the relation of the P-wave with AF. The U-shaped association, particularly the association of the shorter end of the P-wave with AF, will need further investigation. Fourth, the investigators also http://dx.doi.org/10.1016/j.hrthm.2015.05.007
2 adjusted in their analyses for the segment of the PR interval from the offset of the P-wave to the start of the QRS interval. Hence, the analysis accounted for the segment of the PR interval independent of the P-wave. The robust findings described here merit further consideration. First is the mechanistic proximity of the P-wave to AF. A fundamental premise is the electroanatomic and electrophysiological significance of the P-wave. Alterations in P-wave duration, atrial conduction time, and atrial refractory periods characterize the profuse remodeling of conditions that predispose to AF: sinus node dysfunction, heart failure, hypertension, sleep-disordered breathing.7–10 The work of Nielsen et al capitalizes on the evidence that the P-wave provides a strong noninvasive surrogate of atrial electrophysiology. Second, the electrocardiogram is ubiquitous in clinical practice. Automated P-wave measures are accessible from software algorithms; the work here suggests that using them may have practical value, particularly as a noninvasive, inexpensive barometer for evaluating AF risk. Third, enthusiasm for the P-wave is buttressed, however, by the fact that AF prevention is not directed toward modifying the P-wave. Rather, as emphasized by the Heart Rhythm Society statement, prevention should focus on what is modifiable to decrease AF risk (eg, diet and lifestyle, hypertension, and obesity).2 The P-wave may serve as an intermediate endophenotype between such risk factors and AF, but is a poor target for directing therapeutic interventions. Fourth and finally is the question of how P-waves add to incremental risk metrics. My collaborators and I reported that adding the P-wave to AF risk models did not improve risk prediction.11 However, larger AF risk scores developed in communitybased studies have not included the P-wave in risk prediction models.12,13 Nielsen et al should be encouraged to develop an AF risk prediction tool to assess the contributions of P-wave duration to AF. Investigation into the P-wave, strengthened by digitized electrocardiograms and contemporary software, has increased enormously. The P-wave has a place in the continued, developing epidemiological and observational literature describing AF risk. Bringing the P-wave into such research will enhance models of arrhythmogenesis by using an
Heart Rhythm, Vol 0, No 0, Month 2015 accessible surrogate of atrial electrical function. The work of Nielsen et al should encourage us to integrate the P-wave into AF risk prediction and prevention.
References 1. Hlatky MA, Greenland P, Arnett DK, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 2009;119:2408–2416. 2. Van Wagoner DR, Piccini JP, Albert CM, et al. Progress toward the prevention and treatment of atrial fibrillation: a summary of the Heart Rhythm Society Research Forum on the Treatment and Prevention of Atrial Fibrillation, Washington, DC, December 9-10, 2013. Heart Rhythm 2015;12:e5–e29. 3. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157–172. discussion 207–212. 4. Nielsen JB, Kuhl JT, Pietersen A, et al. P-wave duration and the risk of atrial fibrillation: results from the Copenhagen ECG Study. Heart Rhythm 2015;XX. XXX–XXX. 5. Soliman EZ, Prineas RJ, Case LD, Zhang ZM, Goff DC Jr. Ethnic distribution of ECG predictors of atrial fibrillation and its impact on understanding the ethnic distribution of ischemic stroke in the Atherosclerosis Risk in Communities (ARIC) study. Stroke 2009;40:1204–1211. 6. Magnani JW, Williamson MA, Ellinor PT, Monahan KM, Benjamin EJ. P wave indices: current status and future directions in epidemiology, clinical, and research applications. Circ Arrhythm Electrophysiol 2009;2:72–79. 7. Sanders P, Morton JB, Kistler PM, Spence SJ, Davidson NC, Hussin A, Vohra JK, Sparks PB, Kalman JM. Electrophysiological and electroanatomic characterization of the atria in sinus node disease: evidence of diffuse atrial remodeling. Circulation 2004;109:1514–1522. 8. Medi C, Kalman JM, Spence SJ, Teh AW, Lee G, Bader I, Kaye DM, Kistler PM. Atrial electrical and structural changes associated with longstanding hypertension in humans: implications for the substrate for atrial fibrillation. J Cardiovasc Electrophysiol 2011;22:1317–1324. 9. Stevenson IH, Roberts-Thomson KC, Kistler PM, Edwards GA, Spence S, Sanders P, Kalman JM. Atrial electrophysiology is altered by acute hypercapnia but not hypoxemia: implications for promotion of atrial fibrillation in pulmonary disease and sleep apnea. Heart Rhythm 2010;7:1263–1270. 10. Sanders P, Morton JB, Davidson NC, Spence SJ, Vohra JK, Sparks PB, Kalman JM. Electrical remodeling of the atria in congestive heart failure: electrophysiological and electroanatomic mapping in humans. Circulation 2003;108:1461–1468. 11. Magnani JW, Zhu L, Lopez F, Pencina MJ, Agarwal SK, Soliman EZ, Benjamin EJ, Alonso A. P-wave indices and atrial fibrillation: cross-cohort assessments from the Framingham Heart Study (FHS) and Atherosclerosis Risk in Communities (ARIC) study. Am Heart J 2015;169(53–61):e51. 12. Schnabel RB, Aspelund T, Li G, et al. Validation of an atrial fibrillation risk algorithm in whites and African Americans. Arch Intern Med 2010;170: 1909–1917. 13. Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc 2013;2:e000102.
Risk assessment for atrial fibrillation: Enter the P-wave Jared W. Magnani, MD, MSc, FAHA From the National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, Boston University School of Medicine, Boston, Massachusetts. The increased attention to risk prediction of atrial fibrillation (AF) is well deserved. AF is associated with multiple adverse outcomes and markedly increased health care utilization. A growing list of diverse exposures has been assessed for their relation to AF, but incorporating the findings into clinical practice is a challenge. Four distinct questions may assist in approaching the clinical and epidemiological literature and identifying the relevance and significance of such myriad investigations into AF risk assessment. First, and most evident, what is the proximity of the exposure to AF pathogenesis? Biological and pathophysiological mechanisms are complex, and AF is a heterogeneous syndrome. Associations that reach statistical significance may reflect unmeasured or unidentified pathophysiology or residual confounding. Epidemiological studies inform us of associations and have limited ability to dissect mechanisms; terms such as predict or mediate inform us of statistical relations, rather than biology or pathophysiology—hence the relevance of evaluating the proximity of the risk factor to mechanisms that may convey risk for AF arrhythmogenesis. Second, how accessible is a putative risk factor for measurement in clinical practice? There is a large literature relating diverse biomarkers to AF. However, biomarkers may be esoteric and not widely available for measurement. The American Heart Association’s statement on the evaluation of markers suggests assessing cost and accessibility as practical aspects in evaluating risk factors.1 Risk assessment may inform us of potential pathways and suggest mechanisms, but have limited “real-world” application. Third, can the putative AF risk factor be addressed in clinical practice? The Heart Rhythm Society consensus document on AF treatment and prevention emphasized “early identification and treatment of modifiable risk factors.”2 Whether a newly identified risk marker can be used to modify AF risk is fundamental metric for gauging its practical significance, no matter how small the P value. A fourth and final question is: What is the additional contribution of a potential risk factor to AF risk Dr Magnani is supported by the National Institutes of Health (grant no. R03AG045075) and a Boston University Evans Department of Medicine Career Investment Award. Address reprint requests and correspondence: Dr Jared W. Magnani, National Heart, Lung, and Blood Institute’s Framingham Heart Study, Section of Cardiovascular Medicine, Boston University School of Medicine, Boston Medical Center, 88 E Newton St, Boston, MA 02118. E-mail address: [email protected].
1547-5271/$-see front matter B 2015 Heart Rhythm Society. All rights reserved.
prediction? Statistical approaches such as discrimination and calibration3 provide a stringent evaluation of the incremental contributions of a risk factor toward AF, as demonstrated by community-based studies in AF risk score development. The work by Nielsen et al4 may be evaluated in this broader context of AF risk assessment. The investigators performed a prospective observational analysis of an ambulatory patient cohort residing in Copenhagen, Denmark, and related median P-wave duration to AF risk and secondary end points of cardiovascular mortality and ischemic stroke. Importantly, P-wave duration was quantified at a core facility using digitized tracings analyzed by contemporary software and was informatively evaluated as both a continuous and a categorical variable. AF was ascertained from International Classification of Diseases coding from national registries. The study’s central finding is that the P wave, though small in amplitude, had a very strong signal in relation to AF. The large size of this clinically based cohort (n ¼ 285,933) facilitated assessing 7 categories of P-wave duration. The analysis identified a convincing U-shaped relation between P-wave duration and AF: the lower and upper 5th percentiles of P-wave duration had a 1.6- to 2-fold increased risk of AF relative to the reference range (40th–60th percentile). The findings were consistent, even though not as strong, for both cardiovascular death and risk of ischemic stroke. Splines demonstrated similarly compelling associations between P-wave duration and AF. The work by Nielsen et al has noteworthy strengths. First, the size of the participant sample, derived from an urban center, increases the statistical power and validity of the findings. At the extremes of P-wave duration, the distributions are comparable to a large community-based cohort examination relating P-waves to AF.5 Second, using a core facility and computerized measurement boosts the reliability of the study. There has been ample literature using manually derived P-waves6; in contrast, automated measurement makes quantitative electrocardiography possible in a sample of this magnitude, conveys strong reliability, and provides the median value that could not otherwise be obtained. Third, evaluating the P-wave as a categorical and continuous measure enhances our understanding of the relation of the P-wave with AF. The U-shaped association, particularly the association of the shorter end of the P-wave with AF, will need further investigation. Fourth, the investigators also http://dx.doi.org/10.1016/j.hrthm.2015.05.007
2 adjusted in their analyses for the segment of the PR interval from the offset of the P-wave to the start of the QRS interval. Hence, the analysis accounted for the segment of the PR interval independent of the P-wave. The robust findings described here merit further consideration. First is the mechanistic proximity of the P-wave to AF. A fundamental premise is the electroanatomic and electrophysiological significance of the P-wave. Alterations in P-wave duration, atrial conduction time, and atrial refractory periods characterize the profuse remodeling of conditions that predispose to AF: sinus node dysfunction, heart failure, hypertension, sleep-disordered breathing.7–10 The work of Nielsen et al capitalizes on the evidence that the P-wave provides a strong noninvasive surrogate of atrial electrophysiology. Second, the electrocardiogram is ubiquitous in clinical practice. Automated P-wave measures are accessible from software algorithms; the work here suggests that using them may have practical value, particularly as a noninvasive, inexpensive barometer for evaluating AF risk. Third, enthusiasm for the P-wave is buttressed, however, by the fact that AF prevention is not directed toward modifying the P-wave. Rather, as emphasized by the Heart Rhythm Society statement, prevention should focus on what is modifiable to decrease AF risk (eg, diet and lifestyle, hypertension, and obesity).2 The P-wave may serve as an intermediate endophenotype between such risk factors and AF, but is a poor target for directing therapeutic interventions. Fourth and finally is the question of how P-waves add to incremental risk metrics. My collaborators and I reported that adding the P-wave to AF risk models did not improve risk prediction.11 However, larger AF risk scores developed in communitybased studies have not included the P-wave in risk prediction models.12,13 Nielsen et al should be encouraged to develop an AF risk prediction tool to assess the contributions of P-wave duration to AF. Investigation into the P-wave, strengthened by digitized electrocardiograms and contemporary software, has increased enormously. The P-wave has a place in the continued, developing epidemiological and observational literature describing AF risk. Bringing the P-wave into such research will enhance models of arrhythmogenesis by using an
Heart Rhythm, Vol 0, No 0, Month 2015 accessible surrogate of atrial electrical function. The work of Nielsen et al should encourage us to integrate the P-wave into AF risk prediction and prevention.
References 1. Hlatky MA, Greenland P, Arnett DK, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 2009;119:2408–2416. 2. Van Wagoner DR, Piccini JP, Albert CM, et al. Progress toward the prevention and treatment of atrial fibrillation: a summary of the Heart Rhythm Society Research Forum on the Treatment and Prevention of Atrial Fibrillation, Washington, DC, December 9-10, 2013. Heart Rhythm 2015;12:e5–e29. 3. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157–172. discussion 207–212. 4. Nielsen JB, Kuhl JT, Pietersen A, et al. P-wave duration and the risk of atrial fibrillation: results from the Copenhagen ECG Study. Heart Rhythm 2015;XX. XXX–XXX. 5. Soliman EZ, Prineas RJ, Case LD, Zhang ZM, Goff DC Jr. Ethnic distribution of ECG predictors of atrial fibrillation and its impact on understanding the ethnic distribution of ischemic stroke in the Atherosclerosis Risk in Communities (ARIC) study. Stroke 2009;40:1204–1211. 6. Magnani JW, Williamson MA, Ellinor PT, Monahan KM, Benjamin EJ. P wave indices: current status and future directions in epidemiology, clinical, and research applications. Circ Arrhythm Electrophysiol 2009;2:72–79. 7. Sanders P, Morton JB, Kistler PM, Spence SJ, Davidson NC, Hussin A, Vohra JK, Sparks PB, Kalman JM. Electrophysiological and electroanatomic characterization of the atria in sinus node disease: evidence of diffuse atrial remodeling. Circulation 2004;109:1514–1522. 8. Medi C, Kalman JM, Spence SJ, Teh AW, Lee G, Bader I, Kaye DM, Kistler PM. Atrial electrical and structural changes associated with longstanding hypertension in humans: implications for the substrate for atrial fibrillation. J Cardiovasc Electrophysiol 2011;22:1317–1324. 9. Stevenson IH, Roberts-Thomson KC, Kistler PM, Edwards GA, Spence S, Sanders P, Kalman JM. Atrial electrophysiology is altered by acute hypercapnia but not hypoxemia: implications for promotion of atrial fibrillation in pulmonary disease and sleep apnea. Heart Rhythm 2010;7:1263–1270. 10. Sanders P, Morton JB, Davidson NC, Spence SJ, Vohra JK, Sparks PB, Kalman JM. Electrical remodeling of the atria in congestive heart failure: electrophysiological and electroanatomic mapping in humans. Circulation 2003;108:1461–1468. 11. Magnani JW, Zhu L, Lopez F, Pencina MJ, Agarwal SK, Soliman EZ, Benjamin EJ, Alonso A. P-wave indices and atrial fibrillation: cross-cohort assessments from the Framingham Heart Study (FHS) and Atherosclerosis Risk in Communities (ARIC) study. Am Heart J 2015;169(53–61):e51. 12. Schnabel RB, Aspelund T, Li G, et al. Validation of an atrial fibrillation risk algorithm in whites and African Americans. Arch Intern Med 2010;170: 1909–1917. 13. Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc 2013;2:e000102.
