Stroke is a leading cause of death and disability globally, with atrial fibrillation (AF) recognized as a significant risk factor due to its association with increased stroke recurrence and mortality. Timely detection of AF is crucial to prevent recurrent strokes and improve outcomes. This study primarily aimed to evaluate the utility of 24-hour Holter monitoring for AF detection in acute ischemic stroke patients.

This is a retrospective observational study utilized data from the Hospital Information System (HIS) in the tertiary care hospital, employing the electronic medical record system after discussion with neurology dept. Inclusion Criteria The study included consecutive patients who were older than 18 years and had been admitted with an acute ischemic stroke / TIA during a specified study period. Stroke diagnosis were confirmed by using established protocols, which included imaging with MRI or CT scans. Exclusion Criteria These include individuals with pre existing AF, Haemorrhagic stroke , Trauma related IC Bleed , unconfirmed cases (patients whose diagnosis of acute ischemic stroke could not be definitively established based on available medical records and imaging studies), and those with missing complete Holter reports (instances where the Holter monitoring data was incomplete or unavailable for the full 24-hour period required for analysis) Data Collection The information about gender, age, risk factors of stroke, and Holter findings were collected through the detailed chart reviews of patients' electronic medical records. The date and time of stroke diagnosis and Holter monitoring were obtained. Risk factors of stroke, such as hypertension, diabetes mellitus, previous stroke, ischemic heart disease (IHD), smoking, alcohol intake, atherosclerosis, heart failure, and dilated cardiomyopathy, were obtained from the clinical notes. Holter recordings noted as the presence of AF, atrial tachycardia (AT), premature atrial complexes (PACs), premature ventricular complexes (PVCs), ventricular and supraventricular tachycardia, AV blocks, and sinus pauses. SAMPLE SIZE P=prevalence 6.4% q=1-P (1-prevalance) α = Significance level 5% d= Absolute precision 5% Sample size=92 STATISTICAL ANALYSIS Collected data were analyzed using Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 28.0, Armonk, NY). The frequency of those detected to have AF was calculated and represented for gender and meaningful age groups.. The qualitative variables are presented as percentages, while the quantitative variables are expressed as mean and standard deviation. To assess the significance the chi square test and independent t test was used. P-value of less than 0.05 was considered statistically significant.

DATA ANALYSIS Mean Std. Deviation Median 25th quartile 75th quartile Age 67.35 10.094 67.00 60.00 75.50 DISTRIBUTION OF AGE Age Frequency Percent ≤50 4 4.0 51-60 23 23.0 61-70 29 29.0 71-80 37 37.0 ≥81 7 7.0 Total 100 100.0 DISTRIBUTION OF GENDER Sex Frequency Percent Female 26 26.0 Male 74 74.0 Total 100 100.0 The average age of participants in this study was 67.35 years, with the majority (37%) falling between the ages of 71 and 80, showing that the study population was largely old. Males made up 74% of the study population, demonstrating a male predominance. DISTRIBUTION OF RISK FACTORS Risk factor Present Absent Frequency Percent Frequency Percent HTN 84 84.0 16 16.0 T2DM 52 52.0 48 48.0 DLP 41 41.0 59 59.0 CAD 35 35.0 65 65.0 In terms of risk factors, hypertension (84%) was the most prevalent co morbidity, followed by type 2 diabetes (52%), dyslipidemia (41%), and coronary artery disease (35%). DISTRIBUTION OF RISK ADDICTIONS Addictions Present Absent Frequency Percent Frequency Percent Smoking 43 43.0 57 57.0 Alcoholism 29 29.0 71 71.0 Participants reported an increased prevalence of addictive habits, with 43% smoking and 29% drinking. DISTRIBUTIONS OF HOLTER FINDINGS Holter findings Present Absent Frequency Percent Frequency Percent VPC 40 40 60 60 AF 34 34 66 66 SINUS TACHYC 16 16 84 84 SVPC 14 14 86 86 FIRST DEGR AV BLOCK 13 13 87 87 SINUS BRADYC 7 7 93 93 ATRIAL ECTOPIC 7 7 93 93 JUNC RHYTHM 7 7 93 93 AFL 4 4 96 96 SINUS PAUSES<2.5 SEC 4 4 96 96 SVT 2 2 98 98 SECOND DEGREE AV BLOCK TYPE1 2 2 98 98 MAT 2 2 98 98 SINUS PAUSES>2.5SEC 1 1 99 99 VT 1 1 99 99 SECOND DEGR AV BLOCK TYPE 2 1 1 99 99 CHB 1 1 99 99 The most prevalent arrhythmias found during Holter monitoring were ventricular premature complexes (VPCs, 40%) and atrial fibrillation (34%). Other anomalies, such as sinus tachycardia (16%), supraventricular premature complexes (14%), and first-degree AV block (13%). ASSOCIATION BETWEEN HOLTER FINDINGS AND HYPERTENSION Risk factors Hypertension Total P value (chi square) Present Absent VPC Present 32(80%) 8(20%) 40 0.373 Absent 52(86.7%) 8(13.3%) 60 AF Present 28(82.4%) 6(17.6%) 34 0.747 Absent 56(84.8%) 10(15.2%) 66 SINUS TACHYC Present 14(87.5%) 2(12.5%) 16 0.669@ Absent 70(83.3%) 14(16.7%) 84 SVPC Present 13(92.9%) 1(7.1%) 14 0.289@ Absent 71(82.6%) 15(17.4%) 86 FIRST DEGR AV BLOCK Present 10(76.9%) 3(23.1%) 13 0.475@ Absent 74(85.1%) 13(14.9%) 87 @ Fisher’s exact test ASSOCIATION BETWEEN HOLTER FINDINGS AND TYPE 2 DIABETICS Risk factors Type 2 diabetics Total P value (chi square) Present Absent VPC Present 20(50%) 20(50%) 40 0.744 Absent 32(53.3%) 28(46.7%) 60 AF Present 18(52.9%) 16(47.1%) 34 0.892 Absent 34(51.5%) 32(48.5%) 66 SINUS TACHYC Present 11(68.8%) 5(31.3%) 16 0.143 Absent 41(48.8%) 43(51.2%) 84 SVPC Present 7(50%) 7(50%) 14 0.872 Absent 45(52.3%) 41(47.7%) 86 FIRST DEGR AV BLOCK Present 8(61.5%) 5(38.5%) 13 0.461 Absent 44(50.6%) 43(49.4%) 87 ASSOCIATION BETWEEN HOLTER FINDINGS AND DYSLIPIDEMIA Risk factors Dyslipidemia Total P value (chi square) Present Absent VPC Present 18(45%) 22(55%) 40 0.507 Absent 23(38.3%) 37(61.7%) 60 AF Present 15(44.1%) 19(55.9%) 34 0.649 Absent 26(39.4%) 40(60.6%) 66 SINUS TACHYC Present 5(31.3%) 11(68.8%) 16 0.387 Absent 36(42.9%) 48(57.1%) 84 SVPC Present 4(28.6%) 10(71.4%) 14 0.308 Absent 37(43%) 49(57%) 86 FIRST DEGR AV BLOCK Present 3(23.1%) 10(76.9%) 13 0.159 Absent 38(43.7%) 49(56.3%) 87 ASSOCIATION BETWEEN HOLTER FINDINGS AND CAD Risk factors CAD Total P value (chi square) Present Absent VPC Present 14(35%) 26(65%) 40 1.000 Absent 21(35%) 39(65%) 60 AF Present 10(29.4%) 24(70.6%) 34 0.400 Absent 25(37.9%) 41(62.1%) 66 SINUS TACHYC Present 3(18.8%) 13(81.3%) 16 0.137 Absent 32(38.1%) 52(61.9%) 84 SVPC Present 6(42.9%) 8(57.1%) 14 0.511@ Absent 29(33.7%) 57(66.3%) 86 FIRST DEGR AV BLOCK Present 3(23.1%) 10(76.9%) 13 0.320@ Absent 32(36.8%) 55(63.2%) 87 When connections between risk variables and Holter data were investigated, there were no statistically significant relationships between hypertension, diabetes, dyslipidemia, or coronary artery disease and Holter findings.

Cardioembolism accounts for 17% to 30% of all ischemic strokes.1,2 Some data suggest that >50% of these are because of atrial fibrillation (AF).3 Paroxysmal AF (PAF) is often undetected because characteristics such as short duration, episodic, and frequently asymptomatic nature make it challenging to diagnose at the bedside, leading to suboptimal secondary prevention.4 It is likely that a proportion of strokes labeled as cryptogenic are cardioembolic in origin because of occult AF.5,6 Furthermore, ≥2 factors contributing to stroke risk may coexist: even patients with an identified risk factor for nonembolic stroke may have occult cardioembolism. Detection rate of new AF from a standard 12-lead ECG after ischemic stroke/transient ischemic attack (TIA) is ≈2% to 5%7,8 and from 24-hour Holter is 2% to 6%.9–11 Despite current guidelines advocating for at least 24 hours of continuous monitoring in acute ischemic stroke cases, there is no consensus on the optimal monitoring methods and durations. In our study, AF was detected in 34% of participants, using 24-hour Holter monitoring. The detection rate in our study was slightly higher than that in a prospective multicenter cohort study, where the rate was 2.6% [12]. This could be due to our study being conducted in a single tertiary-care referral center. Conversely, another study utilizing 24-hour Holter monitoring reported a high detection rate of 25.2% [13]. This significant difference might be due to a non homogeneous distribution of sample size in their study, with the majority of the population being elderly. The gender distribution of our results is analogous to that reported by Goel et al. observed as higher detection rates among males than females, which is consistent with our findings [13]. The detection rate using 24-hour Holter is considered to be low when compared with the expected AF in acute ischemic stroke patients. Various studies have shown that extending the monitoring duration to 72 hours significantly increases the AF detection rate among this group. Grond et al. reported that prolonging the monitoring period from 24 to 72 hours improved the detection rate from 2.6% to 4.3% [12]. Furthermore, employing 14-day event Holter monitoring substantially enhanced AF detection to 14.6% [14]. The relatively low detection rate in our study may not represent the actual AF patients in our sample because of the low sensitivity of the 24-hour Holter monitoring compared with other modalities, such as long-term cardiac monitoring, external loop recorders (ELR), internal loop recorders (ILR), and mobile cardiac outpatient telemetry (MCOT) [12]. Two large-scale randomized controlled trials have demonstrated that ambulatory ECG monitoring with a 30-day event-triggered recorder and implantable cardiac monitors significantly enhance the detection of AF, thus facilitating the timely initiation of anticoagulation therapy [15,16]. Furthermore, studies suggest that loop recorders offer a superior diagnostic yield compared to seven-day ambulatory Holter monitoring [17]. The improved detection rates provided by these advanced devices over conventional 24-hour ECG and seven-day Holter monitoring enable more patients to receive early and appropriate treatment interventions. Early administration of anticoagulants, such as warfarin and direct oral anticoagulants (DOAC), has been shown to reduce the incidence of stroke by 40%, proving more effective than antiplatelet therapy [18]. Ensuring accurate AF diagnosis in stroke patients is crucial for enabling these life-saving therapeutic interventions. Early detection of AF cases could prevent future recurrent strokes, as the stroke recurrence rate in AF patients is 30% higher than in those without AF. This not only results in worse neurological recovery but also leads to increased length of hospital stay, higher total medical care costs, and greater mortality [19 ,20.21] Longer delays in Holter device attachment can potentially impact AF detection rates and patient outcomes, as timely monitoring is crucial for accurate diagnosis.

Detection of AF was highly variable, and the study was limited by small sample sizes and marked heterogeneity. Further studies are required to inform patient selection, optimal timing, methods, and duration of monitoring for detection of AF/paroxysmal AF. Selecting a high-risk population (eg, older age and cryptogenic strokes), using multiple interventions and periodic follow-up (repeating the intervention), is likely to improve detection rates.

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