Low Utility of Short-Term Rhythm Assessment Before Long-Term Rhythm Monitoring in Patients With Cryptogenic Stroke.

Implantable cardiac monitors are routinely placed for long-term monitoring (LTM) after a period of negative short-term monitoring (STM) to increase atrial fibrillation (AF) detection after a cryptogenic stroke or transient ischemic attack (TIA). Optimizing AF monitoring after a cryptogenic stroke is critical to improve outcomes and reduce costs. We sought to compare the diagnostic yield of STM versus LTM, assess the impact of routine STM on hospitalization length of stay, and perform a financial analysis comparing the current model to a theoretical model wherein patients can proceed directly to LTM. Our retrospective observational cohort study analyzed patients admitted to Montefiore Medical Center between May 2017 and June 2022 with a primary diagnosis of cryptogenic stroke or TIA who underwent Holter device monitoring. Of 396 subjects, STM detected AF in 10 (2.5%) compared with a diagnostic yield of 14.6% for LTM (median time to diagnosis of 76 days). Of the 386 patients with negative STM, 130 (33.7%) received an implantable cardiac monitor while an inpatient, and 256 (66.3%) did not. We calculated a point estimate of 1.67 days delay of discharge attributable to the requirement for STM to precede LTM. Our model showed that the expected cost per patient in the STM-first paradigm is $28,615.33 versus $27,111.24 in the LTM-or-STM paradigm. Considering the relatively lower diagnostic yield of STM and its association with a longer length of stay and higher costs, it may be reasonable to proceed directly to LTM to optimize AF detection after a cryptogenic stroke or TIA.

A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices.

Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac implantable electronic devices using chest radiography were recently developed: The PacemakerID algorithm, available as a mobile phone application (PIDa) and a web platform (PIDw) and The Pacemaker Identification with Neural Networks (PPMnn), available via web platform. In this study, we assessed the relative accuracy of these algorithms. The machine learning algorithms (PIDa, PIDw, PPMnn) were used to predict device manufacturer using chest X-rays for patients with implanted devices. Each prediction was considered correct if predicted certainty was >75%. For comparative purposes, accuracy of each prediction was compared to the result using the CARDIA-X algorithm. 500 X-rays were included from a convenience sample. Raw accuracy was PIDa 89%, PIDw 73%, PPMnn 71% and CARDIA-X 85%. In conclusion, machine learning algorithms for identification of cardiac devices are accurate at determining device manufacturer, have capacity for improved accuracy with additional training sets and can utilize simple user interfaces. These algorithms have clinical utility in limiting potential infectious exposures and facilitate rapid identification of devices as needed for device reprogramming.

Determination of Sensitivity and Specificity of Electrocardiography for Left Ventricular Hypertrophy in a Large, Diverse Patient Population.

BACKGROUND: Electrocardiography (ECG) is poorly sensitive, but highly specific for the diagnosis of left ventricular hypertrophy. However, previous studies documenting this were small and lacked patient diversity. Furthermore, little is known about the impact of patient characteristics on the sensitivity and specificity of ECG for left ventricular hypertrophy. To address this issue, the present study was conducted to ascertain the sensitivity and specificity of ECG for left ventricular hypertrophy in a large, diverse patient population. METHODS: We performed a retrospective cohort study using ECG and echocardiography (ECHO) data from a large metropolitan health system. All patients had one ECG and ECHO on file, obtained within 1 week of each other. Sensitivity and specificity of ECG for left ventricular hypertrophy were determined by comparing results from the MUSE® 12-SL (GE Healthcare, Chicago, IL) computer-generated algorithm for ECG to ECHO left ventricular mass index. Subgroup analyses of individual patient characteristics were performed with corresponding chi-squared analyses to determine significance. RESULTS: A total of 13,960 subjects were included in the study. The typical subject was 60 years of age or older, female, overweight, and hypertensive, and demonstrated low socioeconomic status. The sensitivity and specificity of ECG for left ventricular hypertrophy in the overall cohort were 30.7% and 84.4%, respectively, with multiple patient characteristics influencing these results. CONCLUSIONS: This is the first study to confirm the sensitivity and specificity of ECG for left ventricular hypertrophy in a large, diverse patient population with significant minority representation. Furthermore, although these statistical measures are influenced by patient characteristics, such differences are likely not clinically significant.

Changes in atrial fibrillation admissions following daylight saving time transitions.

BACKGROUND: Daylight saving time (DST) imposes a twice-yearly hour shift. The transitions to and from DST are associated with decreases in sleep quality and environmental hazards. Detrimental health effects include increased incidence of acute myocardial infarction (MI) following the springtime transition and increased ischemic stroke following both DST transitions. Conditions effecting sleep are known to provoke atrial fibrillation (AF), however the effect of DST transitions on AF are unknown. METHODS: Admitted patients aged 18-100 with primary ICD9 code of AF between 2009 and 2016 were included. The number of admissions was compiled and means were compared for the Monday to Thursday period and the entire seven day interval following each DST transition and the entire year for the entire cohort and separated by gender. Significance was determined with Wilcoxon nonparametric tests. RESULTS: Admission data for 6089 patients were included, with mean age of 68 years and 53% female. A significant increase was found in mean AF admissions over the Monday to Thursday period (3.09 vs 2.47 admissions/day [adm/d], P = 0.017) and entire week (2.48 vs 2.09 adm/d, P = 0.025) following the DST spring transition compared to the yearly mean. When separated by gender, women exhibited an increase in AF admissions following the DST spring transition (1.78 vs 1.28 adm/d for Monday to Thursday period, P = 0.036 and 1.38 vs 1.11 adm/d for entire week, P = 0.050) while a non-significant increase was seen in men. No significant differences were found following the autumn transition for the entire cohort or when separated by gender. CONCLUSION: An increase in AF hospital admissions was found following the DST springtime transition. When separated by gender, this finding persisted only among women. This finding adds to evidence of negative health effects associated with DST transitions and factors that contribute to AF episodes.

An Overlap Presentation of Pericardial Decompression Syndrome and Stress Cardiomyopathy Following Therapeutic Pericardiocentesis.

Pericardial decompression syndrome, defined as paradoxical hypotension and pulmonary edema after pericardiocentesis, is a rare complication of pericardiocentesis. Stress cardiomyopathy, caused by excess catecholamine response resulting in left ventricular dysfunction and elevated cardiac enzymes, can overlap with pericardial decompression syndrome, and both might belong to the same spectrum of disease. (Level of Difficulty: Intermediate.).

Relative contribution of modifiable risk factors for incident atrial fibrillation in Hispanics, African Americans and non-Hispanic Whites.

BACKGROUND: Contribution of modifiable risk factors for the risk of new onset atrial fibrillation (AF) in minority populations is poorly understood. Our objective was to compare the population attributable risk (PAR) of various risk factors for incident AF between Hispanic, African American and non-Hispanic Whites. METHODS: An ECG/EMR database was interrogated for individuals free of AF for development of subsequent AF from 2000 to 2013. Cox regression analysis controlled for age > 65, male gender, body mass index > 40 kg/m2, systolic blood pressure > 140 mm Hg, diabetes mellitus, heart failure, socioeconomic status less than the first percentile in New York State, and race/ethnicity. PAR was calculated as (prevalence of X) ∗ (HR - 1)/HR, where HR is the hazard ratio, and X is the risk factor. RESULTS: 47,722 persons free of AF (43% Hispanic, 37% Black and 20% White) were followed for subsequent incident AF. Hypertension in African Americans and Hispanics had a 7.93% and 7.66% greater PAR compared with non-Hispanics Whites. Similar findings existed for the presence of heart failure, with a higher PAR in non-Whites compared to Whites. CONCLUSION: In conclusion, modifiable risk factors play an important role in the risk of incident AF. Higher PAR estimates in African Americans and Hispanics were observed for elevated systolic blood pressure and heart failure. Identification of these modifiable risk factors for atrial fibrillation in non-White minorities may assist in targeting better prevention therapies and planning from a public health perspective. No funding sources were used for this study.

Relation of Body Mass Index to Development of Atrial Fibrillation in Hispanics, Blacks, and Non-Hispanic Whites.

No previous studies have examined the interaction between body mass index (BMI) and race/ethnicity with the risk of atrial fibrillation (AF). We retrospectively followed 48,323 persons free of AF (43% Hispanic, 37% black, and 20% white; median age 60 years) for subsequent incident AF (ascertained from electrocardiograms). BMI categories included very severely underweight (BMI <15 kg/m2), severely underweight (BMI 15.1 to 15.9 kg/m2), underweight (BMI 16 to 18.4 kg/m2), normal (BMI 18.5 to 24.9 kg/m2), overweight (BMI 25.0 to 29.9 kg/m2), moderately obese (BMI 30 to 34.9 kg/m2), severely obese (BMI 35 to 39.9 kg/m2), and very severely obese (BMI >40 kg/m2). Cox regression analysis controlled for baseline covariates: heart failure, gender, age, treatment for hypertension, diabetes, PR length, systolic blood pressure, left ventricular hypertrophy, socioeconomic status, use of ß blockers, calcium channel blockers, and digoxin. Over a follow-up of 13 years, 4,744 AF cases occurred. BMI in units of 10 was associated with the development of AF (adjusted hazard ratio 1.088, 95% confidence interval 1.048 to 1.130, p <0.01). When stratified by race/ethnicity, non-Hispanic whites compared with blacks and Hispanics had a higher risk of developing AF, noted in those whom BMI classes were overweight to severely obese. In conclusion, our study demonstrates that there exists a relation between obesity and race/ethnicity for the development of AF. Non-Hispanic whites had a higher risk of developing AF compared with blacks and Hispanics.