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European Heart Journal Advance Access originally published online on September 19, 2006
European Heart Journal 2007 28(6):685-691; doi:10.1093/eurheartj/ehl197
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© The European Society of Cardiology 2006. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Predictors of stroke in high-risk patients after acute myocardial infarction: insights from the VALIANT trial

Uchechukwu K. Sampson1, Marc A. Pfeffer1, John J.V. McMurray2, Yuliya Lokhnygina3, Harvey D. White4, Scott D. Solomon for the Valsartan in Acute Myocardial iNfarcTion (VALIANT) Trial Investigators1,*

1 Division of Cardiovascular Diseases, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
2 Department of Cardiology, Western Infirmary, Glasgow, Scotland
3 Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, USA
4 Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand

Received 21 February 2006; revised 22 June 2006; accepted 26 July 2006; online publish-ahead-of-print 20 September 2006.

* Corresponding author. Tel: +1 617 732 7182; fax: +1 617 277 4981. E-mail address: ssolomon{at}rics.bwh.harvard.edu


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Conclusion
 Appendix
 Acknowledgements
 References
 
Aims We sought to determine risk models for predicting early and late stroke in a large cohort of high-risk post-myocardial infarction (MI) patients.

Methods and results We prospectively analysed data from 14 703 patients in the VALIANT trial with acute MI complicated by heart failure, left ventricular (LV) systolic dysfunction, or both. Patients were randomized 0.5–10 days after acute MI to valsartan, captopril, or their combination. We evaluated risk factors for early (<45 days) and late (>45 days) stroke by using multivariable Cox proportional hazards regression analyses with stepwise variable selection techniques applied to 92 pre-specified potential predictor variables. After randomization, 463 (3.2%) patients had fatal (n = 124) or non-fatal (n = 339) strokes, with 134 strokes occurring in the first 45 days. The strokes were classified as ischaemic (348), haemorrhagic (40), or of indeterminate cause (75). Estimated glomerular filtration rate and heart rate when in sinus rhythm were the most powerful predictors of early stroke (<45 days after MI), whereas diastolic blood pressure (DBP) >90 mmHg, prior stroke, and atrial fibrillation (AF) were the most powerful predictors of stroke overall. Ejection fraction and sex were not predictive of stroke in this cohort.

Conclusion Among high-risk patients presenting with MI but without initial neurological symptoms, the risk of stroke 6 weeks thereafter is 0.94% (95% CI 0.78–1.09). Of the most powerful baseline predictors of stroke, DBP and AF are amenable to therapeutic interventions and thus merit special attention in these patients.

Key Words: Acute coronary syndromes • Acute myocardial infarction • Acute cerebral haemorrhage • Acute cerebral infarction • Embolic stroke • Risk factors for stroke

This paper was guest edited by Prof. Maarten L. Simoons, Erasmus Medical Center Rotterdam, The Netherlands


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Conclusion
 Appendix
 Acknowledgements
 References
 
The development of a stroke in the acute phase of myocardial infarction (MI) is devastating and is associated with a very high risk of death.1 The risk factors for early stroke have been well documented, especially in the recent era of reperfusion therapy. The long-term risk of this complication and its predictors, however, is not well described, especially in patients receiving contemporary evidence-based treatments for secondary prevention of coronary artery disease. Similarly, whether the early and late risk factors for stroke after MI are similar is unknown. In prior studies, left ventricular (LV) dysfunction (LV ejection fraction ≤ 40%) and heart failure were predictors of increased risk of stroke.2–4 In a recent study of 5573 patients enrolled in the VALsartan In Acute myocardial iNfarcTion (VALIANT) registry, the incidence of in-hospital stroke was 1.5%, and the presence of heart failure on admission for an acute MI was associated with increased in-hospital risk of stroke.5 The VALIANT registry, which collected data on consecutive MI patients at specific clinical sites, was created to provide a better estimate of the proportion of the general MI population to whom the VALIANT trial results would apply. However, the registry did not provide data to evaluate the long-term predictors of stroke as there was no follow-up after hospital discharge. In contrast, the VALIANT trial randomized a large international cohort of patients with heart failure, LV systolic dysfunction, or both following MI and provides a unique opportunity to determine the contemporary predictors of stroke in high-risk patients. An understanding of contemporary short- and long-term predictors of stroke is essential for developing preventive strategies.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Conclusion
 Appendix
 Acknowledgements
 References
 
The VALIANT trial
VALIANT was a prospective randomized double-blind study of 14 703 patients with acute MI. From 0.5 to 10 days after MI, eligible patients who had clinical or radiological signs of heart failure, evidence of LV systolic dysfunction (LV ejection fraction ≤35% on echocardiography or contrast angiography and ≤40% on radionuclide ventriculography), or both, were randomly assigned to receive treatment with valsartan, captopril, or a combination of the two drugs. Major exclusion criteria included systolic blood pressure <100 mmHg, a serum creatinine concentration >2.5 mg/dL, a history of intolerance or contraindication to angiotensin-converting enzyme (ACE)-inhibitors or angiotensin receptor blockers (ARBs), clinically significant valvular disease, severe limitation of life expectancy attributable to any other disease, and the absence of written informed consent. The primary endpoint of VALIANT was all-cause mortality, and the median follow-up was 24.7 months. Details of rationale and design,6 as well as the primary study results7 have been described.

Stroke endpoint
The endpoint of stroke was prospectively defined as an acute focal neurological deficit lasting more than 24 h or resulting in death. Imaging was not required for the diagnosis of stroke, but was performed when deemed appropriate by the treating physician. For consistency in the application of the endpoint definition, a committee blinded to treatment assignment adjudicated all potential strokes.

Statistical analysis
Univariate comparisons of baseline characteristics between patients who had stroke and those who did not were performed by using the {chi}2 test for categorical variables; the non-parametric Wilcoxon signed-rank test was used for baseline comparison of age and estimated glomerular filtration rate (eGFR) given the non-normal distributions of these variables. eGFR was calculated using the four-component MDRD equation as previously described.8

Models for stroke, early (<45 days) and late (46 days–3 years) were derived from 92 potential predictors (appendix) identified a priori among demographic, history, and laboratory variables. The 45-day cutoff point was dictated by trial protocol and actually reflects the 30-day visit data plus the pre-specified 15-day window. The role of various candidate predictors was evaluated by multivariable Cox proportional hazards regression analyses using a stepwise forward variable selection technique. For each continuous potential predictor, the linearity of the log hazard ratio (HR) was verified; where appropriate, the relation between continuous variables and stroke was examined by using spline functions with inflection points (appendix). The independent splines were incorporated into the model to provide information on the full range of these variables.

Event curves were computed according to the method of Kaplan and Meier. In all analyses, a P-value of 0.01 was accepted as the set level for statistical significance. All analyses were performed by using SAS statistical software version 8.2 (SAS Institute, Inc., Cary, NC, USA).


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Conclusion
 Appendix
 Acknowledgements
 References
 
Incidence and timing of stroke
Of the 14 703 patients enrolled in the VALIANT trial, 463 (3.2%) suffered a stroke; 124 (26.8%) of these strokes were fatal. Of the 463 patients who suffered stroke, 388 underwent cerebral imaging and were classified as ischaemic (348) or haemorrhagic (40). Seventy-five stroke cases were not classified because of an absence of cerebral imaging. The baseline characteristics of patients with and without stroke are presented in Table 1. The cumulative rate of stroke in the entire population is illustrated in Figure 1. The Kaplan–Meier estimated rate of stroke in the first 45 days was 0.94% (95% CI 0.78–1.09); the cumulative rates were 2.33% (95% CI 2.08–2.58), 3.41% (95% CI 3.09–3.73), and 4.21% (95% CI 3.73–4.68) for the first, second, and third years, respectively. The Kaplan–Meier stroke rates according to treatment allocation were 4.3% (95% CI 3.5–5.1), 4.4% (95% CI 3.6–5.2), and 3.9% (95% CI 3.0–4.7) for valsartan, captopril, or both; there was no statistically significant difference in the incidence of stroke in these groups (log-rank statistic = 2.1; P = 0.35).


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  		Table 1 Baseline characteristics of patients according to stroke event/non-event in the VALIANT study population

 

Figure 1
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  		Figure 1 Cumulative incidence of stroke in the VALIANT study population.

 
Risk factors for stroke
The predictors of stroke in the multivariable model are presented in Figure 2. For patients under 70 years of age, there was a 39% increase in the risk of stroke with each decade increase in age; the same trend was seen in patients older than 70 years, but it was not statistically significant. With each 10 mmHg rise in diastolic blood pressure (DBP) above 90 mmHg, there was a 79% increase in the risk of stroke; DBP below 90 mmHg was not significantly associated with stroke. Other significant independent predictors include atrial fibrillation(AF) post-qualifying MI, black race, anterior wall location of MI, and a history of stroke, transient ischaemic attack, angina, or diabetes. Percutaneous coronary intervention (PCI) after the qualifying MI and the baseline use of statins were negative risk factors for stroke. In the large subpopulation (n = 11 338) of VALIANT patients with a recorded LV ejection fraction, addition of LV ejection fraction to the multivariate model for stroke developed for the entire population did not significantly improve the model (P-value for LV ejection fraction with all selected variables in the model was 0.92; HR 1.00; 95% CI 0.99–1.01). There was no difference in the cumulative incidence of stroke among patients with LV systolic dysfunction, heart failure, or both (adjusted P-value of 0.59).


Figure 2
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  		Figure 2 Risk factors for all strokes in the multivariate analysis of the VALIANT study population. MI, myocardial infarction; TIA, transient ischaemic attack.

 
The predictors in the multivariable models for early and late stroke are presented in Figures 3 and 4. The predictors of early stroke (Figure 3) included eGFR, heart rate (sinus rhythm), body surface area, and radiological evidence of heart failure. Body mass index was not predictive of stroke (HR 0.98; 95% CI 0.93–1.03; P = 0.40; {chi}2 = 0.70). The model for late stroke (Figure 4) was similar to the overall model. eGFR and body surface area were powerful predictors of stroke in the early post-MI period, but were not long-term predictors.


Figure 3
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  		Figure 3 Risk factors for early stroke (<45 days) in the multivariate analysis of the VALIANT study population.

 

Figure 4
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  		Figure 4 Risk factors for late stroke (46 days–3 years) in the multivariate analysis of the VALIANT study population. MAP, mean arterial pressure.

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Conclusion
 Appendix
 Acknowledgements
 References
 
We have shown that in a large cohort of patients with LV systolic dysfunction, heart failure, or both complicating MI, the risk of stroke was nearly 1% in the first 6 weeks following MI, with a subsequent decline in the cumulative incidence of stroke (Figure 1). Baseline estimated GFR and heart rate were the most powerful risk predictors for early stroke, whereas elevated DBP above 90 mmHg, a history of stroke prior to qualifying MI, and AF were the most powerful overall predictors. By examining all incidents of stroke during the complete follow-up period, we confirmed that the established non-modifiable risk factors for stroke, age and ethnicity, were also independent predictors of stroke in VALIANT.9–11 Hypertension, diabetes, AF, and a history of angina were additional predictors, as recognized previously.11,12 Our study corroborates the established association between anterior wall MI and an increased risk of stroke.13

In contrast to prior studies, we did not confirm that LV ejection fraction was a predictor of stroke in VALIANT;2,3 this observation was consistent across decreasing ejection fraction subgroups. One potential explanation for this apparent discrepancy is that all VALIANT patients were treated with inhibitors of the renin–angiotensin system and other modern therapies, with a high rate of post-MI beta-blocker and lipid-lowering medications. Another potential explanation is that patients with higher ejection fractions (>40%) enrolled in VALIANT were required by protocol to have heart failure, a known risk factor for stroke, complicating their infarction. Of note, anticoagulation, with either oral agents or heparin, at randomization was not a predictor of stroke in VALIANT. This finding is in conflict with that reported by the SOLVD2 investigators where the use of oral anticoagulants was associated with fewer strokes in patients with LV systolic dysfunction.

There is clinical evidence14,15 that the inhibition of the renin–angiotensin system by ACE-inhibitors or angiotensin II type 1 (AT1) receptor blockers is effective in reducing the incidence of stroke in patients with notable cardiovascular risk. In addition, animal and clinical studies have led to the postulate that ARBs may have additional neuroprotective attributes.16–19 However, in the VALIANT population, the rate of stroke did not differ significantly between treatment groups, a finding that is consistent with the main findings of VALIANT. The role of ARBs in reduction of stroke will be further clarified by the Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET) and the Telmisartan Randomized Assessment Study in ACE-Intolerant Subjects with Cardiovascular Disease (TRANSCEND) trials, designed to compare the ARB, telmisartan, the ACE-inhibitor, ramipril, or both in the reduction of cardiovascular and cerebral risk.20

The use of PCI was associated with a 36% reduction in the risk of stroke in VALIANT (P = 0.002). Nevertheless, these findings likely represent significant selection bias; a similar consequence of selection bias is noted in our univariate analysis of the use of thrombolytics with qualifying MI. Although the use of thrombolytics was not an independent predictor of stroke, the patients with stroke were less likely to have received thrombolytics (26.1 vs. 35.5%; P < 0.001); however, it is important to note that those who had stroke as a consequence of thrombolytics would not have been randomized into the parent trial. Another negative risk factor for stroke was the use of statins (24% risk reduction; P = 0.012). Indeed, clinical21–24 and basic science25 evidence supports the role of statins in preventing ischaemic stroke by a variety of mechanisms, including plaque stabilization and reduction in endothelial dysfunction. Our findings add to the existing body of evidence that supports this concept.

Because the risk of stroke in the early period after MI may have a distinct underlying pathophysiological mechanism from the long-term risk factors, we analysed the risk for early and late stroke by constructing a 45-day model for stroke (Figure 3) and a conditional model for stroke from 46 days to 3 years (Figure 4). In our evaluation, the risk of stroke was greatest in the early period after MI and was strongly predicted by eGFR, heart rate when in sinus rhythm, body surface area, and radiological evidence of heart failure. Although a history of chronic renal failure was not a predictor of stroke, eGFR was. Although eGFR may simply represent a marker of severity of illness and may reflect an overall atherosclerotic burden, it is possible that acute renal dysfunction in the early period after MI may itself predispose to stroke. Similarly, heart rate, a known predictor of mortality after MI, likely reflects the haemodynamic burden associated with reduced pump function and thus may be a better predictor of stroke than measures of LV function.

Additional limitations of this study should be noted. We could not evaluate the possible risk factors for all types of stroke because some were of undetermined aetiology due to lack of imaging studies; thus, we cannot exclude the possibility of systematic bias against imaging confirmation. In addition, the criteria for trial enrolment precluded randomization of patients who had had strokes in the very early post-MI period; the average number of days [mean (SD)] from qualifying MI to randomization in the VALIANT trial was 5 (2.5). Therefore, it is likely that we may not have reliably evaluated the ‘true’ predictors of early stroke following MI. Of note in the VALIANT registry, the incidence of in-hospital stroke was 1.5% whereas the incidence of early stroke was 0.94% in the current study, suggesting that indeed we could have underestimated the number of strokes in the VALIANT trial cohort due to systematic preferences built into trial design. However, the association between heart failure and stroke in the VALIANT trial cohort is consistent with the findings in VALIANT registry.5 The fact that the VALIANT registry reported greater in-hospital mortality in patients with stroke suggests that competing risks may have diminished the sensitivity of potential independent risk factors for stroke in the VALIANT trial cohort. Finally, the difference in the models for early and late stroke could possibly be attributed to other factors: a real difference in the population, the subsequent reduction in power arising from the stratification of events, or a misrepresentation of the population that may attend the exclusion of patients from statistical analysis based on the temporal relation of their outcomes.


   Conclusion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
Appendix
 Acknowledgements
 References
 
In a population of patients who suffered MI complicated by LV systolic dysfunction, heart failure, or both, but without neurological symptoms within 0.5–10 days following the event, the risk of subsequent stroke 6 weeks thereafter is ~1%. eGFR and heart rate were the most powerful risk predictors for early stroke, whereas DBP >90 mmHg, prior stroke, and AF were the most powerful overall predictors. In contrast to prior studies, LV ejection fraction was not a predictor of stroke and there was no difference in the adjusted cumulative incidence of stroke among patients with LV systolic dysfunction, heart failure, or both. Whereas eGFR may only serve as a marker of bad outcomes, some of these outcome predictors, including elevated heart rate (sinus rhythm), AF, and blood pressure, are potentially modifiable by therapeutic approaches and may deserve attention in these high-risk patients. However, whether specific intervention would lead to a reduction of stroke in post-MI patients will require further study. Further understanding of the risk factors for stroke may lead to strategies for better identifying or reducing the occurrence of stroke in high-risk patients.


   Appendix
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 Appendix
Acknowledgements
 References
 
The following 92 potential predictor variables were used in the stroke event analysis. Continuous variables: age (years), weight (lbs), height (cm), BMI (kg/m2), body surface area (m2), SBP (mmHg), DBP (mmHg), MAP (mmHg), pulse pressure (mmHg), heart rate (bpm), estimated eGFR (mL/min), and time to randomization (days).

Categorical variables: region of the world, sex, race, clinical evidence of heart failure, quantitative image evidence of LV systolic dysfunction, radiological evidence of LV failure, Killip class, ECG type (Q-wave), ECG site (anterior), ECG type (new LBBB), previous hospitalization, previous smoker, current smoker, new diabetes, prior diabetes, angina pre-qualifying MI, unstable angina pre-qualifying MI, MI pre-qualifying MI, PCI pre-qualifying MI, CABG pre-qualifying MI, CHF pre-qualifying MI, TIA pre-qualifying MI, stroke pre-qualifying MI, AF pre-qualifying MI, PVD pre-qualifying MI, dyslipidemia pre-qualifying MI, chronic obstructive pulmonary disease pre-qualifying MI, renal insufficiency pre-qualifying MI, alcohol abuse pre-qualifying MI, cancer pre-qualifying MI, hypertension pre-qualifying MI, diabetes pre-qualifying MI, aspirin use associated with qualifying MI, ACE-inhibitor use associated with qualifying MI, GP IIb/IIIa inhibitor use associated with qualifying MI, ARB use associated with qualifying MI, beta-blocker use associated with qualifying MI, thrombolytic therapy associated with qualifying MI, primary PCI associated with qualifying MI, angina post-qualifying MI, catheterization post-qualifying MI, PCI post-qualifying MI, CABG post-qualifying MI, CHF post-qualifying MI, AF post-qualifying MI, sustained ventricular tachycardia post-qualifying MI, ventricular fibrillation post-qualifying MI, AICD post-qualifying MI, pacemaker use post-qualifying MI, IABP use post-qualifying MI, renal insufficiency post-qualifying MI, dyslipidaemia post-qualifying MI, hypertension post-qualifying MI, diabetes post-qualifying MI, GP IIb/IIa inhibitor use at randomization, ACE-inhibitor use at randomization, amiodarone use at randomization, ARB use at randomization, aspirin use at randomization, beta-blocker use at randomization, calcium channel blocker use at randomization, antidepressant use at randomization, digoxin use at randomization, heparin use at randomization, IV inotropic/vasopressor use at randomization, insulin use at randomization, potassium-sparing diuretic use at randomization, potassium supplement at randomization, nitrate use at randomization, non-steroid anti-inflammatory at randomization, oral anticoagulant use at randomization, other antiarrhythmic use at randomization, other vasodilator use at randomization, other diuretic use at randomization, oral hypoglycaemic use at randomization, other lipid agent use at randomization, other antiplatelet use at randomization, serotonin re-uptake inhibitor at randomization, statin use at randomization, and study medication assignment.

Linearity of log HR for continuous variables
In building the overall model for stroke, linearity was rejected for age, DBP (diabpb), and mean arterial blood pressure (mapress). Each of these variables seemed to present two linear trends: the log HR function for age appeared to increase up until the age of 70 and remained virtually the same afterwards; for the DBP and mean arterial blood pressure, the relation was flat but increased after thresholds of 90 and 115 mmHg, respectively. To account for this, new variables were created for model selection using age, diabpb, and mapress: agele (if age ≤70 years) and ageg (if age >70 years); diabpble (if diabpb ≤90 mmHg), and diabpbg (if diabpb > 90 mmHg); mapressle (if mapress ≤115 mmHg) and mapressg (if mapress >115 mmHg). These new variables are loosely referred to as ‘spline’ variables.

For age, height, weight, systolic blood pressure (sysbpb), DBP, and mean arterial pressure, we wanted to determine the changes in the log HR for each 10 U increase rather than 1 U (e.g. change in magnitude of the log HR for each 10-year increase in age instead of 1-year increase); therefore, the variables (agele, ageg, diabpble, diabpbg, mapressle, and mapressg) were each divided by 10. Similarly, new variables were derived from systolic blood pressure (sysbpb), height, and weight: sysbpb10 = sysbpb/10, Hgt10 = height/10, Wgt10 = weight/10. A similar approach was adopted in other models of stroke.


   Acknowledgements
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 Appendix
 Acknowledgements
References
 
The authors are indebted to the VALIANT trial investigators and coordinators for their critical roles in the successful completion of this project. We also acknowledge Elizabeth E. Schramm for her editorial support and assistance in the preparation of this manuscript.

Conflict of interest: J.J.V.McM., M.A.P., H.D.W., S.D.S. report receiving research support for conduct of the VALIANT Trial from Novartis Pharmaceuticals.


   Footnotes
 
This paper was guest edited by Prof. Maarten L. Simoons, Erasmus Medical Center Rotterdam, The Netherlands


   References
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 Introduction
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 Results
 Discussion
 Conclusion
 Appendix
 Acknowledgements
 References
 

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