Dataset Information

Current practice in the management of new-onset atrial fibrillation in critically ill patients: a UK-wide survey.

ABSTRACT: 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.

SUBMITTER: Chean CS

PROVIDER: S-EPMC5592903 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Current practice in the management of new-onset atrial fibrillation in critically ill patients: a UK-wide survey.

Chean Chung Shen CS McAuley Daniel D Gordon Anthony A Welters Ingeborg Dorothea ID

PeerJ 20170908

<h4>Background</h4>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.<h4>Methods</h4>We designed a short user-friendly online questionnaire. All members of the ...[more]

PMID: 28929012

Similar Datasets

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: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:Atrial fibrillation has been associated with increased mortality in critically ill patients. We sought to determine whether atrial fibrillation in the ICU is an independent risk factor for death. A secondary objective was to determine if patients with new-onset atrial fibrillation have different risk factors or outcomes compared with patients with a previous history of atrial fibrillation.Prospective observational cohort study.Medical and general surgical ICUs in a tertiary academic medical center.One thousand seven hundred seventy critically ill patients requiring at least 2 days in the ICU.None.Demographics, medical history, development of atrial fibrillation, fluid balance, echocardiographic findings, medication administration, and hospital mortality were collected during the first 4 days of ICU admission. Atrial fibrillation occurred in 236 patients (13%) (Any AF). Of these, 123 patients (7%) had no prior atrial fibrillation (New-onset AF) while the remaining 113 (6%) had recurrent atrial fibrillation (Recurrent AF). Any AF was associated with male gender, Caucasian race, increased age, cardiac disease, organ failures, and disease severity. Patients with Any AF had increased mortality compared with those without atrial fibrillation (31% vs 17%; p < 0.001), and Any AF was independently associated with death (odds ratio, 1.62; 95% CI, 1.14-2.29; p = 0.007) in multivariable analysis controlling for severity of illness and other confounders. The association of atrial fibrillation with death was magnified in patients without sepsis (odds ratio, 2.92; 95% CI, 1.52-5.60; p = 0.001). Treatment for atrial fibrillation had no effect on hospital mortality. New-onset AF and Recurrent AF were each associated with increased mortality. New-onset AF, but not Recurrent AF, was associated with increased diastolic dysfunction and vasopressor use and a greater cumulative positive fluid balance.Atrial fibrillation in critical illness, whether new-onset or recurrent, is independently associated with increased hospital mortality, especially in patients without sepsis.

Dataset Information

Current practice in the management of new-onset atrial fibrillation in critically ill patients: a UK-wide survey.

Publications

Current practice in the management of new-onset atrial fibrillation in critically ill patients: a UK-wide survey.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets