Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Abstract

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.

Development of a Risk Prediction Model for New Episodes of Atrial Fibrillation in Medical-Surgical Critically Ill Patients Using the AmsterdamUMCdb

“…To date the availability of sophisticated risk prediction models in critical care is limited. The few existing models focus on septic patients only (16) and do not include the large proportion of critically ill patients with non-infectious pathologies. Advanced models for prediction of AF during critical illness, but before its clinical onset, would allow early interventions with a view to preventing serious AF-associated complications, such as haemodynamic instability, stroke and thromboembolic events.…”

A deep learning model for the classification of atrial fibrillation in critically ill patients

“…This would aid in quantifying the burden of NOAF that is clinically important and contribute to a uniform definition of NOAF to be used in future studies. Continuous monitoring with computational classification may enable prediction of the development of atrial fibrillation [31] and enable the stratification of patients with varied duration of NOAF, potentially allowing predictive enrichment in clinical trials. Clinically, enhanced detection of these arrhythmias has the potential to markedly enhance our understanding of the true burden of disease and management of this arrhythmia in critically ill patients.…”

Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information

“…With the exception of two studies focused in critically ill patients ( 30 , 31 ), machine learning-based predictions of incident AF have been restricted to community practice settings using administrative health record data ( 32 , 33 ). Despite the limited translation of these models to cardiovascular disease referral populations, these studies provided foundational evidence for machine learning to provide incremental value for the prediction of incident AF.…”