European Journal of Cardiovascular Medicine

Every year, more than half a million people in the world suffer from acute cerebrovascular events, including ischemic stroke, intracerebral and subarachnoid hemorrhage, giving a mortality of nearly 20%. ^[1] Acute strokes, especially subarachnoid hemorrhage is frequently accompanied by a variety of electrocardiographic (ECG) abnormalities, some of which may be indistinguishable from those seen in association with an episode of severe myocardial ischemia and/or infarction. In addition, patients often have simultaneous hypertension or coronary atherosclerosis, leading to ECG abnormalities. ^[2]

In addition, many primary cardiac disorders, like myxoma, mural thrombus, endocarditis, and atrial septal defect with deep venous thrombosis, can lead to cerebral emboli; arrhythmias, heart block, and decreased cardiac output, which may precipitate cerebral ischemia. Then, healthcare professionals presented with this clinical/electrocardiographic picture are confronted with special challenges, and it is crucial to distinguish stroke induced ECG changes from ECG changes due to concomitant ischemic heart disease. ^[2]

There is a very considerable heterogeneity in how ECG changes in stroke patients are presented in the literature. ^[3] This electrocardiographic spectrum seems to be related to the type of cerebrovascular disease and its localization. The autonomic and cardiovascular effects of stroke; however, are modulated by concomitant factors such as pre-existent cardiac diseases and electrolyte disorders. Although many subsequent reports have described ECG abnormalities and rhythm disturbances in stroke, especially subarachnoid hemorrhage, few have included an adequate number of patients to statistically assess the relative frequencies of these abnormalities among the pathophysiologic categories of stroke. ^[4]

Furthermore, few previous studies have evaluated ECG changes and rhythm disturbances in ischemic stroke patients without primary heart disease to distinguish abnormalities specifically associated with acute stroke. In view of the varied explanations for the ECG abnormalities in acute CVA, the present study was undertaken to review the pattern of ECG changes associated with pathophysiologic categories of acute stroke among patients with/without cardiovascular disease and to determine if specific ECG changes are related to the location of the lesion. ^[4]

This is a Prospective and observational study conducted in the Department of Medicine, Tertiary Care Teaching Hospital over a period of 1.5 years.

Selection of study subjects - After admission, based on clinical history and Physical Examination, a presumptive diagnosis is made and later the patient will be subjected to Serial ECGs after informed consent.

INCLUSION CRITERIA

• Patients admitted in the NICU and various medical wards within 24 hours after the onset of neurological deficit.
• Patients who developed stroke during their stay in hospital.

EXCLUSION CRITERIA

• Patients who had a history of previous CAD or structural heart diseases or atrial fibrillation or whose previous ECG showed abnormalities.
• Infective, neoplastic and traumatic causes producing neurological deficit.
• Patients who recovered from the deficit within 24 hours are not included in view of exclusion of TIA from the study.
• Patients who had suffered from sub-arachnoid hemorrhage are not included in the study in view of the emergency and surgical line of management.

In the present study, majority of the patients i.e. 16 (32%) were from the age group of 65-74 years age group followed by 13 (26%) from 55-64 years of age group. Mean age of the stroke patients was 65.4±12.6 years with the range being 52-78 years. In this study, 35 (70%) patients were males and 15 (30%) patients were females. Major type of stroke was ischemic i.e. among 32 (64%) patients and 18(36%) patients were having hemorrhagic type. In this study, 15 (30%) patients were addicted to smoking and 13 (26%) to alcohol. Amongst comorbidity, we have found that 12 (24%) were having hypertension, 7 (14%) were having diabetes and 3 (6%) were having DM with hypertension. (Table 1)

Table 1: Distribution of stroke patients according to baseline characteristics.

In the current study, total 35 (70%) stroke patients had some form of ECG change. Majority i.e. 21 (42%) patients had QTc prolongation followed by 20 (40%) patients had T wave changes. 

Table 2: ECG changes and type of stroke.

Other ECG changes among stroke patients were sinus bradycardia (24%), ST changes (elevation or depression=26%), Atrial fibrillation (26%), Ventricular tachycardia (14%), Ventricular fibrillation (12%) and AV block (4%). Out of these ECG abnormalities, QTc prolongation and Atrial fibrillation were significantly more among hemorrhagic stroke patients (p<0.05) and T wave changes and ST changes (elevation or depression) were significantly more among ischemic stroke patients (p<0.05). Other ECG abnormalities did not show significant difference between the two groups (p>0.05). (Table 2).

In the present study, we found that PWDis and PTFV1 parameters as measured on surface ECG were independent predictors for the presence of PAF in patients with ischemic stroke.

Some patients with AF describe palpitations, shortness of breath and fatigue while some may be completely asymptomatic and present with complications such as ischemic stroke or tach cardiomyopathy. ^[3] Non-valvular AF is responsible for about half of all cardioembolic events. ^[4] The incidence of occult or subclinical AF is not known. Therefore, patients with symptomatic AF that are observed in daily practice may be considered the tip of the iceberg. The development of new devices and applications has led to an increase in diagnosis rates of asymptomatic and subclinical AF. The rates of subclinical AF were reported to be 35% in a group of patients with implanted cardiac devices that were followed for 2.5 years. ^[5] In patients with cryptogenic stroke, 12.5% were found to have PAF attacks during one-year rhythm monitorization. ^[6] It is important to identify patients who do not have arrhythmia on surface ECG, but who are at high risk for the development of AF and to perform long-term rhythm monitoring in these patients to prevent ischemic stroke.

Many scoring methods have been utilized to predict the development of AF in those with normal surface ECG. ^[7] The CHADS2 and the CHA2DS2VASc risk scores have been reported for prediction of new occurrence of AF, ischemic stroke and long-term outcomes after AF ablation. ^[8] Christophersen, reported that CHARGE-AF scoring was better at predicting AF, compared to CHA2DS2-VASc. On the other hand, some studies have used the HATCH score for prediction of AF recurrence and persistence. ^[9] The main feature of these scoring methods is that they predict the development of AF according to the clinical characteristics of the patients. However, AF is an ECG disorder and using ECG findings for its prediction may be a more plausible way. Electrocardiographic evaluation is also a simpler, cheaper, and easily accessible method than the aforementioned scoring systems. Furthermore, it has been reported that P-wave indices are as effective as clinical scoring methods for the prediction of AF and ischemic stroke. ^[10] Several ECG indices thought to represent atrial remodeling have been independently associated with stroke and AF. These measures include the (i) PWD; (ii) PWDis; (iii) PTFV1 in the precordial lead V1; (iv) P-wave axis; and (v) interatrial blocks (IABs).[16] Previous studies have identified several P-wave indices that are markers of LA dysfunction and are associated with ischemic stroke with or without AF. Previous studies have reported that maximum PWD may be used for the prediction of AF. However, we did not detect PWD to be a predictor for the presence of AF in our study.

P wave dispersion is considered to reflect impaired and heterogeneous interatrial conduction, which is a specific and sensitive marker of AF in a wide variety of conditions. ^[11] Dilaveris, found that PWDis was significantly higher in patients with paroxysmal AF compared to the control group, and a PWDis value of 40 ms distinguished paroxysmal AF patients from the control group with a sensitivity of 83% and a specificity of 85%. Aytemir, reported PWDis >36 ms to be an independent predictor for the development of AF with a sensitivity of 77% and specificity of 82%. The PWD is has been used for the prediction of AF in several clinical situations such as hyperthyroidism, chronic obstructive pulmonary disease, acute ischemic stroke and hypertrophic cardiomyopathy. ^[12] Doğan, reported PWDis as an independent predictor for the development of AF in patients with acute ischemic stroke. Similarly, we also found PWD is to be a predictor of PAF in patients with ischemic stroke, with a sensitivity of 71% and specificity of 69%.

The PTFV1 was first used by Morris, in 1964 as a representative of LA overload in several valvular heart diseases. Later, PTFV1 was found to be an indicator of various pathologies such as increased LA pressure, LA hypertrophy, LA enlargement, and abnormal interatrial conduction. ^[13] Since AF development is also associated with these structural changes and electrical remodeling, PTFV1 may be a good predictor of AF development. PTFV1 >4000 µV·ms is accepted to be abnormal. An abnormal PTFV1 level has been shown to negatively affect prognosis in heart failure and myocardial infarction. ^[14] It was reported that a 1-SD increase of PTFV1 increased the risk of AF occurrence by 27%. Additionally, PTFV1 was found to be a better predictor in hemodialysis and stroke patients compared to the normal population. The PTFV1 is indicative of LA volume overload and it has, therefore, been frequently used for AF prediction in patients undergoing hemodialysis. Goda, found PTFV1 to be a strong predictor of AF in patients with acute ischemic stroke. In addition, PTFV1 was reported to be a good predictor of stroke, regardless of AF in a meta-analysis by He, However, Sajeev, suggested that PTFV1 was a weak predictor of ischemic stroke. Similarly, we found that PTFV1 had a lower sensitivity and specificity in the detection of AF compared to PWD.

In conclusion, PWDis and PTFV1 in lead V1 are independent predictors for the presence of PAF in patients with ischemic stroke. These simple and easily accessible predictors, which can be detected by surface ECG, may help in identifying patients that require longer rhythm monitoring to detect occult PAFs, thereby preventing recurrent strokes.

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