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Atrial Fibrillation Detection During Sepsis: Study on MIMIC III ICU Data.

ABSTRACT: Sepsis is defined by life-threatening organ dysfunction during infection and is one of the leading causes of critical illness. During sepsis, there is high risk that new-onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. As a result, computer aided automated and reliable detection of new-onset AF during sepsis is crucial, especially for the critically ill patients in the intensive care unit (ICU). In this paper, a novel automated and robust two-step algorithm to detect AF from ICU patients using electrocardiogram (ECG) signals is presented. First, several statistical parameters including root mean square of successive differences, Shannon entropy, and sample entropy were calculated from the heart rate for the screening of possible AF segments. Next, Poincaré plot-based features along with P-wave characteristics were used to reduce false positive detection of AF, caused by the premature atrial and ventricular beats. A subset of the Medical Information Mart for Intensive Care (MIMIC) III database containing 198 subjects was used in this study. During the training and validation phases, both the simple thresholding as well as machine learning classifiers achieved very high segment-wise AF classification performance. Finally, we tested the performance of our proposed algorithm using two independent test data sets and compared the performance with two state-of-the-art methods. The algorithm achieved an overall 100% sensitivity, 98% specificity, 98.99% accuracy, 98% positive predictive value, and 100% negative predictive value on the subject-wise AF detection, thus showing the efficacy of our proposed algorithm in critically ill sepsis patients. The annotations of the data have been made publicly available for other investigators.

SUBMITTER: Bashar SK

PROVIDER: S-EPMC7670858 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe severity of sepsis is associated with systemic clotting activation. Atrial fibrillation (AF) is the most commonly observed arrhythmia in patients with sepsis and can lead to a poor prognosis. The aim of this study is to elucidate the association between oral anticoagulants and survival from septic patients complicated with AF.MethodsThe data of 8,828 septic patients, including 2,955 AF and 5,873 without AF, were all originated from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients with sepsis and AF are divided into OAC- group (n = 1,774) and OAC+ group (n = 1,181) based on OAC therapy. Septic patients with no AF were considered as the control group (n = 5,873, sepsis and no AF group). The main outcome endpoint was the survival rate of 30 day. The secondary outcome endpoint was the length of stay (LOS) from intensive care unit and hospital. Propensity score matching (PSM) was used to adjust the influence of superfluous factors, and a restricted mean survival time (RMST) analysis was used for calculating the benefit of survival time and survival rate. Analysis including univariate and multivariate logistic regression analysis was conducted to find prognosis-related predictors.ResultsAfter PSM, the OAC+group had a higher 30-day survival rate compared to the OAC- group (81.59% vs. 58.10%; P < 0.001) in the ICU. Despite the higher survival, the hospital LOS (14.65 days vs. 16.66 days; P = 0.15) and ICU LOS (6.93 days vs. 5.92 days; P = 0.02) were prolonged at OAC+ group than OAC- group. No difference was found in survival rate of 30 day between the sepsis patients using warfarin and patients using NOAC (85.60% vs. 79.84%, P = 0.12). The sepsis patients using warfarin had a prolonged LOS in ICU and hospital compared with the sepsis patients using NOAC. In the vasopressor subgroup, patients who received NOAC therapy were associated with a reduced 30-day survival rate (73.57% vs. 84.03%; P = 0.04) and reduced LOS in ICU and hospital than those on warfarin therapy.ConclusionThis study demonstrated that oral anticoagulants may increase the 30-day survival rate of patients with sepsis and AF.

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.

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Atrial Fibrillation Detection During Sepsis: Study on MIMIC III ICU Data.

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