Dataset Information

High-Sensitivity Estimate of the Incidence of New-Onset Atrial Fibrillation in Critically Ill Patients.

ABSTRACT: To estimate the incidence of new-onset atrial fibrillation in critically ill patients.

Design

Prospective cohort.

Setting

Medical-surgical ICU.

Subjects

Consecutive patients without a history of atrial fibrillation but with atrial fibrillation risk factors.

Interventions

Electrocardiogram patch monitor until discharge from hospital or up to 14 days.

Measurements and main results

A total of 249 participants (median age of 71 yr [interquartile range] 64-78 yr; 35% female) completed the study protocol of which 158 (64%) were admitted to ICU for medical illness, 78 (31%) following noncardiac surgery, and 13 (5%) with trauma. Median Acute Physiology and Chronic Health Evaluation II score was 16 (interquartile range, 12-22). Median duration of patch electrocardiogram monitoring, ICU, and hospital lengths of stay were 6 (interquartile range, 3-12), 4 (interquartile range, 2-8), and 11 days (interquartile range, 5-23 d), respectively.Atrial fibrillation ? 30 seconds was detected by the patch in 44 participants (17.7%), and three participants (1.2%) had atrial fibrillation detected clinically after patch removal, resulting in an overall atrial fibrillation incidence of 18.9% (95% CI, 14.2-24.3%).Total duration of atrial fibrillation ranged from 53 seconds to the entire monitoring time. The proportion of participants with ?1 episode(s) of ?6 minute, ?1 hour, ?12 hour and ?24 hour duration was 14.8%, 13.2%, 7.0%, and 5.3%, respectively. The clinical team recognized only 70% of atrial fibrillation cases that were detected by the electrocardiogram patch.

Conclusions

Among patients admitted to an ICU, the incidence of new-onset atrial fibrillation is approximately one in five, although approximately one-third of cases are not recognized by the clinical team.

SUBMITTER: McIntyre WF

PROVIDER: S-EPMC7803666 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

High-Sensitivity Estimate of the Incidence of New-Onset Atrial Fibrillation in Critically Ill Patients.

McIntyre William F WF Belley-Côté Emilie P EP Vadakken Maria E ME Rai Anand S AS Lengyel Alexandra P AP Rochwerg Bram B Bhatnagar Akash K AK Deif Bishoy B Um Kevin J KJ Spence Jessica J Connolly Stuart J SJ Bangdiwala Shrikant I SI Rao-Melacini Purnima P Healey Jeff S JS Whitlock Richard P RP

Critical care explorations 20210108 1

To estimate the incidence of new-onset atrial fibrillation in critically ill patients.<h4>Design</h4>Prospective cohort.<h4>Setting</h4>Medical-surgical ICU.<h4>Subjects</h4>Consecutive patients without a history of atrial fibrillation but with atrial fibrillation risk factors.<h4>Interventions</h4>Electrocardiogram patch monitor until discharge from hospital or up to 14 days.<h4>Measurements and main results</h4>A total of 249 participants (median age of 71 yr [interquartile range] 64-78 yr; 35 ...[more]

PMID: 33458680

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Project description:ObjectivesNew-onset atrial fibrillation during critical illness is an independent risk factor for mortality. The ability to identify patients at high risk for new-onset atrial fibrillation is limited. We hypothesized that genetic susceptibility contributes to risk of new-onset atrial fibrillation in the ICU.DesignRetrospective sub-study of a prospective observational cohort study.SettingMedical and general surgical ICUs in a tertiary academic medical center.PatientsOne-thousand three-hundred sixty-nine critically ill patients admitted to the ICU for at least 2 days with no known history of atrial fibrillation who had DNA available for genotyping.InterventionsNone.Measurements and main resultsWe genotyped 21 single-nucleotide polymorphisms associated with atrial fibrillation in ambulatory studies using a Sequenom platform (San Diego, CA). We collected demographics, medical history, and development of new-onset atrial fibrillation during the first four days of ICU admission. New-onset atrial fibrillation occurred in 98 patients (7.2%) and was associated with age, male sex, coronary artery disease, and vasopressor use. Single-nucleotide polymorphisms associated with new-onset atrial fibrillation were rs3853445 (near PITX2, p = 0.0002), rs6838973 (near PITX2, p = 0.01), and rs12415501 (in NEURL, p = 0.03) on univariate testing. When controlling for clinical factors, rs3853445 (odds ratio, 0.47; 95% CI, 0.30-0.73; p = 0.001) and rs12415501 (odds ratio, 1.72; 95% CI, 1.27-2.59; p = 0.01) remained significantly associated with new-onset atrial fibrillation. The addition of genetic variables to clinical factors improved new-onset atrial fibrillation discrimination in a multivariable logistic regression model (C-statistic 0.82 vs 0.78; p = 0.0009).ConclusionsWe identified several single-nucleotide polymorphisms associated with new-onset atrial fibrillation in a large cohort of critically ill ICU patients, suggesting there is genetic susceptibility underlying this common clinical condition. This finding may provide new targets for future mechanistic studies and additional insight into the application of genomic information to identify patients at elevated risk for a common and important condition in the ICU.

Project description:BACKGROUND:New-onset atrial fibrillation (AF) is the most common arrhythmia in critically ill patients. Although evidence base and expert consensus opinion for management have been summarised in several international guidelines, no specific considerations for critically ill patients have been included. We aimed to establish current practice of management of critically ill patients with new-onset AF. METHODS:We designed a short user-friendly online questionnaire. All members of the Intensive Care Society were invited via email containing a link to the questionnaire, which comprised 21 questions. The online survey was conducted between November 2016 and December 2016. RESULTS:The response rate was 397/3152 (12.6%). The majority of respondents (81.1%) worked in mixed Intensive Care Units and were consultants (71.8%). Most respondents (39.5%) would start intervention on patients with fast new-onset AF and stable blood pressure at a heart rate between 120 and 139 beats/min. However, 34.8% of participants would treat all patients who developed new-onset fast AF. Amiodarone and beta-blockers (80.9% and 11.6% of answers) were the most commonly used anti-arrhythmics. A total of 63.8% of respondents do not regularly anti-coagulate critically ill patients with new-onset fast AF, while 30.8% anti-coagulate within 72 hours. A total of 68.0% of survey respondents do not routinely use stroke risk scores in critically ill patients with new-onset AF. A total of 85.4% of participants would consider taking part in a clinical trial investigating treatment of new-onset fast AF in the critically ill. DISCUSSION:Our results suggest a considerable disparity between contemporary practice of management of new-onset AF in critical illness and treatment recommendations for the general patient population suffering from AF, particularly with regard to anti-arrhythmics and anti-coagulation used. Amongst intensivists, there is a substantial interest in research for management of new-onset AF in critically ill patients.

Project description:Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.

Project description:ImportanceData on the burden of new-onset atrial fibrillation after transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement (AVR) is limited mostly to small series or post hoc analyses of clinical trials.ObjectivesTo evaluate the incidence of new-onset atrial fibrillation and assess the incidence of in-hospital mortality associated with new-onset atrial fibrillation after TAVI and AVR.Design, setting, and participantsIn this population-based observational study using the National Inpatient Sample and a validation cohort from the New York state inpatient database, the National Inpatient Sample was queried from January 1, 2012, to September 30, 2015, and the New York state inpatient database was queried from January 1, 2012, to December 31, 2014. Hospitalizations of adults undergoing TAVI or isolated AVR were examined. The incidence of in-hospital mortality across groups with new-onset atrial fibrillation was assessed in the National Inpatient Sample cohort using multivariable logistic regression modeling. Statistical analysis was conducted from August 20, 2018, to March 19, 2019.Main outcomes and measuresThe primary outcome was the occurrence of new-onset atrial fibrillation, which was identified by excluding hospitalizations in which atrial fibrillation was present on admission. The secondary outcome was in-hospital mortality in TAVI and AVR hospitalizations with and without new-onset atrial fibrillation.ResultsA total of 48 715 TAVI hospitalizations (47.4% women and 52.6% men; mean [SD] age, 81.3 [8.1] years; 82.3% white) and 122 765 AVR hospitalizations (39.0% women and 61.0% men; mean [SD] age, 67.8 [12.0] years; 78.0% white) were identified. New-onset atrial fibrillation occurred in 50.4% of TAVI hospitalizations and 50.1% of AVR hospitalizations. In the multivariable-adjusted model, TAVI and AVR hospitalizations with new-onset atrial fibrillation had higher odds of in-hospital mortality compared with hospitalizations without new-onset atrial fibrillation (TAVI: odds ratio, 1.57; 95% CI, 1.21-2.04; and AVR: odds ratio, 1.36; 95% CI, 1.08-1.70). The results were then confirmed with the New York state inpatient database, which contains a present on arrival indicator. The incidence of new-onset atrial fibrillation was 14.1% (244 of 1736 hospitalizations) after TAVI and 30.6% (1573 of 5141 hospitalizations) after AVR in the New York state inpatient database.Conclusions and relevanceIn this large nationwide study, a substantial burden of new-onset atrial fibrillation was observed after TAVI and AVR. The incidence of new-onset atrial fibrillation was higher after AVR than after TAVI in a patient-level state inpatient database.

Dataset Information

High-Sensitivity Estimate of the Incidence of New-Onset Atrial Fibrillation in Critically Ill Patients.

Design

Setting

Subjects

Interventions

Measurements and main results

Conclusions

Publications

High-Sensitivity Estimate of the Incidence of New-Onset Atrial Fibrillation in Critically Ill Patients.

Similar Datasets

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