scholarly journals Automated Electronic Phenotyping of Cardioembolic Stroke

Stroke ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 181-189 ◽  
Author(s):  
Wyliena Guan ◽  
Darae Ko ◽  
Shaan Khurshid ◽  
Ana T. Trisini Lipsanopoulos ◽  
Jeffrey M. Ashburner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Regular Expression ◽  
Massachusetts General Hospital ◽  
Cardioembolic Stroke ◽  
Stroke Registry

Background and Purpose: Oral anticoagulation is generally indicated for cardioembolic strokes, but not for other stroke causes. Consequently, subtype classification of ischemic stroke is important for risk stratification and secondary prevention. Because manual classification of ischemic stroke is time-intensive, we assessed the accuracy of automated algorithms for performing cardioembolic stroke subtyping using an electronic health record (EHR) database. Methods: We adapted TOAST (Trial of ORG 10172 in Acute Stroke Treatment) features associated with cardioembolic stroke for derivation in the EHR. Using administrative codes and echocardiographic reports within Mass General Brigham Biobank (N=13 079), we iteratively developed EHR-based algorithms to define the TOAST cardioembolic stroke features, revising regular expression algorithms until achieving positive predictive value ≥80%. We compared several machine learning-based statistical algorithms for discriminating cardioembolic stroke using the feature algorithms applied to EHR data from 1598 patients with acute ischemic strokes from the Massachusetts General Hospital Ischemic Stroke Registry (2002–2010) with previously adjudicated TOAST and Causative Classification of Stroke subtypes. Results: Regular expression-based feature extraction algorithms achieved a mean positive predictive value of 95% (range, 88%–100%) across 11 echocardiographic features. Among 1598 patients from the Massachusetts General Hospital Ischemic Stroke Registry, 1068 had any cardioembolic stroke feature within predefined time windows in proximity to the stroke event. Cardioembolic stroke tended to occur at an older age, with more TOAST-based comorbidities, and with atrial fibrillation (82.3%). The best model was a random forest with 92.2% accuracy and area under the receiver operating characteristic curve of 91.1% (95% CI, 87.5%–93.9%). Atrial fibrillation, age, dilated cardiomyopathy, congestive heart failure, patent foramen ovale, mitral annulus calcification, and recent myocardial infarction were the most discriminatory features. Conclusions: Machine learning-based identification of cardioembolic stroke using EHR data is feasible. Future work is needed to improve the accuracy of automated cardioembolic stroke identification and assess generalizability of electronic phenotyping algorithms across clinical settings.

Abstract 17308: Leukoaraiosis is Associated With Atrial Fibrillation and Greater Disability at 90-days in Patients Hospitalized With a Cardioembolic Stroke

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Ameeta Karmarkar ◽  
Johanna Helenius ◽  
Nils Henninger ◽  
David D McManus
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Infarct Volume ◽  
Cardioembolic Stroke ◽  
Post Discharge ◽  
Stroke Registry ◽  
Diffusion Weighted Images ◽  
Degree Of Disability ◽  
University Of Massachusetts ◽  
Fazekas Scale

Introduction: Atrial fibrillation (AF) has been associated with a poor post-stroke prognosis. AF has been linked to increased leukoaraiosis (LA) burden, which is associated with greater functional impairment after stroke. Hypothesis: We hypothesized that AF is associated with the severity of LA burden and thus impacts 90-day outcomes in patients hospitalized with acute cardioembolic ischemic stroke. Methods: We retrospectively analyzed 317 consecutive patients with acute cardioembolic ischemic stroke enrolled in the University of Massachusetts Medical Center’s Stroke Registry between 2010 and 2014. Stroke mechanism was determined according to the Causative Classification System. LA burden was graded on MRI using the Fazekas scale. Infarct volume was quantified on diffusion weighted images. Neurological deficit severity was assessed at admission according to the National Institutes of Health Stroke Scale (NIHSS). We evaluated the degree of disability using the modified Rankin score (mRS) at 90 days. Participants were categorized as having excellent (mRS 0-1), or poor (mRS 2-6) status. Results: A total of 132 patients were classified as having a definite (n=81) or probable (n=51) cardioembolic stroke. Of these, 54 (41%) had AF (previously known or newly documented). Compared to patients without AF, patients with AF were older (p=0.001), more likely to be receiving antihypertensives(p=0.008) and anticoagulants (p<0.05 each), were associated with a poor outcome at 90 days. Conclusions: We observed that AF was related to severity of LA and that LA burden was an independent predictor of significant disability 90-days post-discharge. Further study is needed to investigate the links between AF, LA, and outcomes after cardioembolic stroke.

Usefulness of the ACTEL Score to Predict Atrial Fibrillation in Patients with Cryptogenic Stroke

Cardiology ◽  
2020 ◽  
Vol 145 (3) ◽  
pp. 168-177 ◽  
Author(s):  
Antonio Muscari ◽  
Pietro Barone ◽  
Luca Faccioli ◽  
Marco Ghinelli ◽  
Marco Pastore Trossello ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Cryptogenic Stroke ◽  
Left Ventricular ◽  
Control Group ◽  
End Diastolic Volume ◽  
Electrocardiogram Monitoring

Introduction: To assess the probability of undetected atrial fibrillation (AF) in patients with ischemic stroke, we previously compared patients who were first diagnosed with AF with patients with large or small artery disease and obtained the MrWALLETS 8-item scoring system. In the present study, we utilized cryptogenic strokes (CS) as the control group, as AF is normally sought among CS patients. Methods: We retrospectively examined 191 ischemic stroke patients (72.5 ± 12.6 years), 68 with first diagnosed AF and 123 with CS, who had undergone 2 brain CT scans, echocardiography, carotid/vertebral ultrasound, continuous electrocardiogram monitoring and anamnestic/laboratory search for cardiovascular risk factors. Results: In logistic regression, 5 variables were independently associated with AF, forming the “ACTEL” score: Age ≥75 years (OR 2.42, 95% CI 1.18–4.96, p = 0.02; +1 point); hyperCholesterolemia (OR 0.38, 95% CI 0.18–0.78, p = 0.009; –1 point); Tricuspid regurgitation ≥ mild-to-moderate (OR 4.99, 95% CI 1.63–15.27, p = 0.005; +1 point); left ventricular End-diastolic volume <65 mL (OR 7.43, 95% CI 2.44–22.6, p = 0.0004; +1 point); Left atrium ≥4 cm (OR 4.57, 95% CI 1.97–10.62, p = 0.0004; +1 point). The algebraic sum of these points may range from –1 to +4. For AF identification, the area under the receiver operating characteristic curve was 0.80 (95% CI 0.73–0.87). With a cutoff of ≥2, positive predictive value was 80.8%, specificity 92.7% and sensitivity 55.9%. Conclusions: The ACTEL score, a simplified and improved version of the MrWALLETS score, allows the identification of patients with first diagnosed AF, in the context of CSs, with a high positive predictive value.

Abstract WP302: Atrial Fibrillation First Diagnosed at the Time of Ischemic Stroke: A Missed Opportunity

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Daniel E Singer ◽  
Yuchiao Chang ◽  
Leila H Borowsky ◽  
Susan Regan ◽  
Steven M Greenberg
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Stroke Risk ◽  
Massachusetts General Hospital ◽  
Oral Anticoagulant Therapy ◽  
Oral Anticoagulants ◽  
Newly Diagnosed ◽  
Stroke Event ◽  
Stroke Registry ◽  
Missed Opportunity

Introduction: Atrial fibrillation (AF) is a major cause of ischemic stroke. Individuals with undiagnosed AF lack the stroke risk reduction afforded by oral anticoagulant therapy. In 1983 Wolf documented that 24% (95% CI 14-37) of AF-related strokes had AF first diagnosed at the time of stroke. Given increased medical and lay attention to AF-stroke, we sought to determine whether this percentage had decreased in contemporary care. Hypothesis: Less than 20% of patients with AF-related stroke have their AF first diagnosed at the time of stroke. Methods: We identified patients admitted to Massachusetts General Hospital from 01-01-2010 to 12-31-2013 with a new ischemic stroke and either previously or newly diagnosed AF by searching comprehensive hospital databases for stroke and AF ICD-9 codes in conjunction with a hospital stroke registry. Physician reviewers screened 1037 potentially eligible patients, categorized AF as previously known or newly diagnosed, and performed a structured chart review of the stroke event. To confirm the diagnosis of AF was new, we conducted automated searches for AF terms in the patients’ electronic medical records (EMRs) prior to the stroke admission. Results: We validated 856 cases (83%) as AF and ischemic stroke. AF was considered newly diagnosed in 156/856 (18%; 95%CI: 16-21). In the newly diagnosed group, no patient was on oral anticoagulants and the strokes were consequential (median NIHSS=12; 60% with mRankin of ≥3 at discharge, including 15% deaths). Pre-stroke CHA 2 DS 2 -VASc score was ≥2 in 89%. About half (76/156) had a prior medical encounter in the EMR. Evidence of pre-stroke AF was found in 8/76 records, often peri-procedural, but the AF diagnosis was not carried forward. Conclusions: In this large, contemporary cohort, nearly one in five AF-related strokes occurred in patients who did not carry a pre-stroke AF diagnosis, similar to Wolf’s 1983 finding. The vast majority would have been at high enough pre-stroke risk to merit anticoagulation. Our findings support screening for AF in patients before they have strokes. Further, patients with past transient AF identified via automated EMR searches might merit more intensive screening.

First Diagnosis of Atrial Fibrillation at the Time of Stroke

2017 ◽  
Vol 43 (3-4) ◽  
pp. 192-199 ◽  
Author(s):  
Leila H. Borowsky ◽  
Susan Regan ◽  
Yuchiao Chang ◽  
Alison Ayres ◽  
Steven M. Greenberg ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Medical Records ◽  
Stroke Risk ◽  
Massachusetts General Hospital ◽  
Oral Anticoagulants ◽  
Outcome Data ◽  
Newly Diagnosed ◽  
Stroke Registry ◽  
Code Searches

Background: Atrial fibrillation (AF) is a major cause of ischemic stroke. Individuals with undiagnosed AF lack the stroke protection afforded by oral anticoagulants. We obtained a contemporary estimate of the percentage of AF patients newly diagnosed at the time of stroke. Methods: We identified patients admitted to the Massachusetts General Hospital (MGH) from January 1, 2010 to December 31, 2013 with acute ischemic stroke and either previously or newly diagnosed AF using hospital stroke registry data and stroke and AF ICD-9 code searches of hospital databases. Reviewers categorized AF as previously known or newly diagnosed, and collected comorbidity and outcome data. To confirm AF as newly diagnosed, we searched patients' pre-event electronic medical records (EMRs) for AF terms. Results: AF was considered newly diagnosed in 156/856 patients (18%; 95% CI 16-21). In 136/156 cases, AF was diagnosed using 12-lead EKG, telemetry, or rhythm strips. New AF strokes had a median NIH stroke scale of 12; 60% had mRankin ≥3 at discharge, including 15% deaths. Pre-stroke CHA2DS2-VASc score was ≥2 in 89%. About half (76/156) had prior records in the MGH EMR. Evidence of pre-stroke AF, often peri-procedural, was found in 8/76, but the AF diagnosis was not carried forward. Conclusions: In this contemporary cohort, nearly one in 5 AF-related strokes occurred without a pre-stroke AF diagnosis. AF was readily diagnosed using standard rhythm monitoring. The vast majority of patients with newly diagnosed AF were at high enough pre-stroke risk to merit anticoagulation. In conclusion, our findings support screening for AF before stroke. Patients with past transient AF may merit more intensive screening.

Abstract TP221: Using Berlin Questionnaire and Epworth Sleepiness Scale to Identify Stroke Patients at Risk for the Sleep Apnea Syndrome

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Millene Camilo ◽  
Alan Eckeli ◽  
Heidi Sander ◽  
Regina Fernandes ◽  
Joao Leite ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Ischemic Stroke ◽  
Sleep Apnea ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Epworth Sleepiness Scale ◽  
Berlin Questionnaire ◽  
Stroke Patients ◽  
Obstructive Sleep

Background: Sleep-disordered breathing (SDB) is frequent in the acute phase of stroke. Obstructive sleep apnea (OSA) has been found in 62% of stroke patients. The impact of OSA is significant after ischemic stroke, including early neurological deterioration, poor functional outcome and increased long-term mortality. However, performing polysomnography (PSG) for all patients with acute stroke for diagnose OSA is still impracticable. Therefore clinical tools to select patients at higher risk for OSA would be essential. The aim of this study was to determine the validity of the Berlin Questionnaire (BQ) and the Epworth Sleepiness Scale (ESS) to identify stroke patients in whom the PSG would be indicated. Methods: Subjects with ischemic stroke were stratified into high and low risk groups for SDB using a BQ. The ESS ≥ 10 was used to define excessive daytime sleepiness. The BQ and ESS were administered to the relatives of stroke patients at hospital admission. All patients were submitted to a full overnight PSG at the first night after symptoms onset. OSA severity was measured by the apnea-hypopnea index (AHI). Results: We prospectively studied 40 ischemic stroke patients. The mean age was 62 ± 12.1 years and the obstructive sleep apnea (AHI ≥ 15) was present in 67.5%. On stratifying risk of OSA in these patients based on the QB, 77.5% belonged to the high-risk and 50% to the ESS ≥ 10. The sensitivity of QB was 85%, the specificity 35%, the positive predictive value 74% and the negative predictive value 55%. For ESS was respectively 63%, 85%, 89% and 52%. The diagnostic value of the BQ and ESS in combination to predict OSA had a sensitivity of 58%, a specificity of 89%, a positive predictive value of 95% and a negative predictive value of 38%. Conclusions: The QB even applied to the bed-partners of stroke patients is a useful screening tool for OSA.

scholarly journals Cadence-based Classification of Minimally Moderate Intensity During Overground Walking in 21- to 40-Year-Old Adults

2019 ◽  
Vol 16 (12) ◽  
pp. 1092-1097
Author(s):  
Elroy J. Aguiar ◽  
Zachary R. Gould ◽  
Scott W. Ducharme ◽  
Chris C. Moore ◽  
Aston K. McCullough ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Classification Accuracy ◽  
Moderate Intensity ◽  
Overground Walking ◽  
Cross Sectional ◽  
Old Adults ◽  
Metabolic Equivalents ◽  
Sensitivity Specificity

Background: A walking cadence of ≥100 steps/min corresponds to minimally moderate intensity, absolutely defined as ≥3 metabolic equivalents (METs). This threshold has primarily been calibrated during treadmill walking. There is a need to determine the classification accuracy of this cadence threshold to predict intensity during overground walking. Methods: In this laboratory-based cross-sectional investigation, participants (N = 75, 49.3% women, age 21–40 y) performed a single 5-minute overground (hallway) walking trial at a self-selected preferred pace. Steps accumulated during each trial were hand tallied and converted to cadence (steps/min). Oxygen uptake was measured using indirect calorimetry and converted to METs. The classification accuracy (sensitivity, specificity, overall accuracy, and positive predictive value) of ≥100 steps/min to predict ≥3 METs was calculated. Results: A cadence threshold of ≥100 steps/min yielded an overall accuracy (combined sensitivity and specificity) of 73.3% for predicting minimally moderate intensity. Moreover, for individuals walking at a cadence ≥100 steps/min, the probability (positive predictive value) of achieving minimally moderate intensity was 80.3%. Conclusions: Although primarily developed using treadmill-based protocols, a cadence threshold of ≥100 steps/min for young adults appears to be a valid heuristic value (evidence-based, rounded, practical) associated with minimally moderate intensity during overground walking performed at a self-selected preferred pace.

scholarly journals Identification of newborns at risk for autism using electronic medical records and machine learning

2019 ◽  
Author(s):  
Rayees Rahman ◽  
Arad Kodesh ◽  
Stephen Z Levine ◽  
Sven Sandin ◽  
Abraham Reichenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Positive Predictive Value ◽  
Electronic Medical Records ◽  
Predictive Value ◽  
False Positive ◽  
Medical Records ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Positive Rate

AbstractImportanceCurrent approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, where most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome.ObjectiveDevelop a machine learning (ML) method predicting the diagnosis of ASD in offspring in a general population sample, using parental electronic medical records (EMR) available before childbirthDesignPrognostic study of EMR data within a single Israeli health maintenance organization, for the parents of 1,397 ASD children (ICD-9/10), and 94,741 non-ASD children born between January 1st, 1997 through December 31st, 2008. The complete EMR record of the parents was used to develop various ML models to predict the risk of having a child with ASD.Main outcomes and measuresRoutinely available parental sociodemographic information, medical histories and prescribed medications data until offspring’s birth were used to generate features to train various machine learning algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross validation, by computing C statistics, sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value, PPV).ResultsAll ML models tested had similar performance, achieving an average C statistics of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85% for predicting ASD in this dataset.Conclusion and relevanceML algorithms combined with EMR capture early life ASD risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.Key pointsQuestionCan autism risk in children be predicted using the pre-birth electronic medical record (EMR) of the parents?FindingsIn this population-based study that included 1,397 children with autism spectrum disorder (ASD) and 94,741 non-ASD children, we developed a machine learning classifier for predicting the likelihood of childhood diagnosis of ASD with an average C statistic of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85%.MeaningThe results presented serve as a proof-of-principle of the potential utility of EMR for the identification of a large proportion of future children at a high-risk of ASD.

Abstract T P47: Response to Thrombolysis in Ischemic Stroke Depends on the Presence of Initial Arterial Occlusion

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Freidrich Medlin ◽  
Michael Amiguet ◽  
Peter Vanacker ◽  
Patrik Michel
Keyword(s):  
Ischemic Stroke ◽  
Randomized Trials ◽  
Primary Outcome ◽  
Interaction Analysis ◽  
Arterial Occlusion ◽  
Intravenous Thrombolysis ◽  
Class Iii ◽  
Unfavourable Outcome ◽  
Stroke Registry

Objective: We aimed to assess effectiveness of intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) depending on the presence or absence of cervical or intracranial arterial occlusion on acute CT angiography (CTA). Methods: Patients from the Acute STroke Registry and Analysis of Lausanne (ASTRAL) were included in the analysis if they had an onset-to-door-time ≤ 4hours, CTA within 24 hours of onset, premorbid modified Rankin scale (mRS) ≤ 2, and a National Institute of Health Stroke Scale score (NIHSS) >4. Patients having significant intracranial stenosis (50-99%) or receiving endovascular treatment were excluded. The primary outcome was a 3 month handicap of mRS >2. We used an interaction analysis of IVT and initial arterial occlusion after adjusting for potential confounders for the primary outcome. Results: Of 655 included patients, 382 patients (58%) showed arterial occlusion, of whom 263 (69%) received IVT. Of the 273 patients without arterial occlusion, 139 (51%) received IVT. In patients with initial arterial occlusion and after multiple adjustments, IVT was associated with lower likelihood of unfavourable outcome (adjusted OR 0.33, 95% CI 0.12-0.91, p=0.03) whereas it had no significant effect in non-occluded patients (OR 1.32, 95% CI 0.36-4.76, p=0.67). Similarly, the presence of arterial occlusion did not significantly worsen the outcome in thrombolysed patients (OR 1.99, 95% CI 0.68-5.81, p=0.21), whereas it did so in non-thrombolysed patients (OR 7.89, 95% CI 2.29-27.25, p<0.01). Conclusions: IVT for AIS is more effective in the setting of visible arterial occlusions on acute imaging. If confirmed in other studies, this information may influence thrombolysis decisions and planning of further randomized trials. Classification of evidence: This retrospective analysis provides class III evidence that IVT has less benefit in patients without visible occlusion on acute CTA.

P6540Opportunistic screening for atrial fibrillation in an ambulatory geriatric population with a hand held single lead ECG device

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
L A R Zwart ◽  
M E W Hemels ◽  
J R Ruiter ◽  
T Germans ◽  
S Simsek ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
High Risk ◽  
Positive Predictive Value ◽  
Outpatient Clinic ◽  
Predictive Value ◽  
Geriatric Patients ◽  
Poor Quality ◽  
Opportunistic Screening ◽  
Geriatric Population ◽  
Handheld Device

Abstract Introduction Atrial fibrillation (AF) is the most common arrhythmia and its incidence rises with age. Especially geriatric patients are at high risk for the development of AF as well as its complications. These high risk patients might benefit the most of oral anticoagulation. AF is however often asymptomatic in these patients and might stay undiagnosed. Purpose To assess the outcomes of opportunistic screening on AF on a geriatric outpatient clinic with a hand held single lead ECG device. Methods All consecutive patients 60 years and older that visited the outpatient clinic between the 1st of June 2017 and the 1st of June 2018 were eligible. Patients who were not able or willing to give informed consent, or had a pacemaker (PM) or internal cardioverter defibrillator (ICD) were excluded. Patients were screened 2 or 3 times at every visit with the MyDiagnostick©, a hand held single lead ECG device with inbuilt algorithm that identifies AF [1]. At baseline all patients underwent a comprehensive geriatric assessment (CGA), including a 12 lead ECG, physical, cognitive and functional assessment and medication review. All baseline ECGs were reviewed by 1 cardiologist and all measurements with the single lead device were reviewed by 2 independent cardiologists. Disagreement about the rhythm on the measurements was resolved by discussion between the cardiologists. Results 498 consecutive patients were eligible for inclusion. We excluded 39 patients: 20 patients had a PM or ICD, 17 did not want to participate and of 2 the medical files were incomplete. A total of 459 patients participated in this study. The mean age was 78±7.3 years and ranged from 60 to 100 years, 245 patients (53%) were female. Patients were known with 5±3 morbidities and used 6±4 different drugs. At baseline 88 (19%) patients were known with AF and AF was first diagnosed in 24 (5%) patients, constituting to an overall prevalence of 23% within this ambulatory geriatric population. Of these 24 patients, 4 (1%) showed AF on their baseline ECG and in 20 (4%) patients AF was found using the handheld device. A total of 1345 measurement with the handheld device were performed, 14 measurements (1%) were of too low quality to use, 32 (2%) were of poor quality, 148 (11%) acceptable and 1151 (86%) were of good quality. Sensitivity of the hand held device for detecting AF is 83.9%, specificity 99.2%, negative predictive value 99.6%, and positive predictive value 72.2%. Conclusions Opportunistic screening for AF with a hand held ECG device has a 5 times higher yield than the standard CGA with an 12 lead ECG at the first visit only. Also, AF can be reliably excluded after a negative measurement. Because of the potential benefit of OAC we advocate screening geriatric patients for AF at every doctor's visit. However, considering the positive predictive value, a confirmatory ECG remains necessary to confirm the diagnosis of AF. Acknowledgement/Funding None

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