scholarly journals Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 758
Author(s):  
Andoni Elola ◽  
Elisabete Aramendi ◽  
Enrique Rueda ◽  
Unai Irusta ◽  
Henry Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Patient Outcomes ◽  
Learning Model ◽  
Spontaneous Circulation ◽  
Operating Characteristics ◽  
Machine Learning Model ◽  
Validation Procedure ◽  
Electrocardiogram Ecg ◽  
Hospital Cardiac Arrest

A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision–recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.

scholarly journals A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 847
Author(s):  
Jon Urteaga ◽  
Elisabete Aramendi ◽  
Andoni Elola ◽  
Unai Irusta ◽  
Ahamed Idris
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Electrical Activity ◽  
Area Under The Curve ◽  
Pulseless Electrical Activity ◽  
Learning Model ◽  
Spontaneous Circulation ◽  
Machine Learning Model ◽  
Balance Accuracy ◽  
Hospital Cardiac Arrest

Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.

scholarly journals Use of a simulation-based advanced resuscitation training curriculum: Impact on cardiopulmonary resuscitation quality and patient outcomes

2019 ◽  
Vol 21 (1) ◽  
pp. 57-63 ◽  
Author(s):  
Amanda K Young ◽  
Michael J Maniaci ◽  
Leslie V Simon ◽  
Philip E Lowman ◽  
Ryan T McKenna ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Cardiopulmonary Resuscitation ◽  
Patient Outcomes ◽  
Spontaneous Circulation ◽  
Good Neurological Outcome ◽  
Odds Ratios ◽  
Rates Of Return ◽  
Return Of Spontaneous Circulation ◽  
Resuscitation Training ◽  
Hospital Cardiac Arrest

Background Despite a continued focus on improved cardiopulmonary resuscitation quality, survival remains low from in-hospital cardiac arrest. Advanced Resuscitation Training has been shown to improve survival to hospital discharge and survival with good neurological outcome following in-hospital cardiac arrest at its home institution. We sought to determine if Advanced Resuscitation Training implementation would improve patient outcomes and cardiopulmonary resuscitation quality at our institution. Methods This was a prospective, before–after study of adult in-hospital cardiac arrest victims who had cardiopulmonary resuscitation performed. During phase 1, standard institution cardiopulmonary resuscitation training was provided. During phase 2, providers received the same quantity of training, but with emphasis on Advanced Resuscitation Training principles. Primary outcomes were return of spontaneous circulation, survival to hospital discharge, and neurologically favorable survival. Secondary outcomes were cardiopulmonary resuscitation quality parameters. Results A total of 156 adult in-hospital cardiac arrests occurred during the study period. Rates of return of spontaneous circulation improved from 58.1 to 86.3% with an adjusted odds ratios of 5.31 (95% CI: 2.23–14.35, P < 0.001). Survival to discharge increased from 26.7 to 41.2%, adjusted odds ratios 2.17 (95% CI: 1.02–4.67, P < 0.05). Survival with a good neurological outcome increased from 24.8 to 35.3%, but was not statistically significant. Target chest compression rate increased from 30.4% of patients in P1 to 65.6% in P2, adjusted odds ratios 4.27 (95% CI: 1.72–11.12, P = 0.002), and target depth increased from 23.2% in P1 to 46.9% in P2, adjusted odds ratios 2.92 (95% CI: 1.16–7.54, P = 0.024). Conclusions After Advanced Resuscitation Training implementation, there were significant improvements in cardiopulmonary resuscitation quality and rates of return of spontaneous circulation and survival to discharge.

Abstract 290: The Association of Regional Intra-arrest Transport Practices for Out-of-hospital Cardiac Arrest with Survival and Neurological Status at Hospital Discharge

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Brian E Grunau ◽  
Takahisa Kawano ◽  
Masashi Okubo ◽  
Joshua Reynolds ◽  
Matthieu Heidet ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Hospital Discharge ◽  
Patient Outcomes ◽  
Patient Survival ◽  
Secondary Analysis ◽  
Neurological Status ◽  
Spontaneous Circulation ◽  
Regional Level ◽  
Regional Clusters ◽  
Hospital Cardiac Arrest

Background: There is substantial regional variation in out-of-hospital cardiac arrest (OHCA) outcomes. We investigated whether regional-level intra-arrest transport practices were associated with patient outcomes. Methods: We performed a secondary analysis of the “CCC Trial” dataset, which included EMS-treated adult non-traumatic OHCA enrolled from 49 regional clusters. The exposure of interest was regional-level intra-arrest transport practices (RIATP), calculated as the proportion of cases within the enrolling cluster transported prior to return of spontaneous circulation (“intra-arrest transport”), divided into quartiles. We fit a multilevel mixed-effects logistic regression model to estimate the association of RIATP quartile and both survival and favorable neurologic status (mRS ≤ 3) at hospital discharge, adjusted for patient-level Utstein variables. Results: We included all 26,148 CCC-enrolled patients, 36% of whom were female, 97% were treated with prehospital ALS, and 23% had shockable initial rhythms. The median RIATP of the 49 clusters was 20% (IQR 6.2 - 30%). The figure shows outcomes stratified by RIATP quartile. Compared to the first quartile (<6.2%), increasing RIATP had the following adjusted associations with: (i) favourable neurological status: OR 0.87 (95% CI 0.60-1.26), 0.74 (95% CI 0.51-1.07), 0.36 (95% CI 0.25-0.53); and (ii) survival: 0.63 (95% CI 0.47-0.85), 0.60 (95% CI 0.45-0.79), 0.44 (95% CI 0.33-0.59). Conclusion: Treatment within a region that utilizes intra-arrest transport less frequently was associated with improved patient survival. These results may, in part, explain differences between regional OHCA survival outcomes.

scholarly journals Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 305 ◽  
Author(s):  
Andoni Elola ◽  
Elisabete Aramendi ◽  
Unai Irusta ◽  
Artzai Picón ◽  
Erik Alonso ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiac Arrest ◽  
State Of The Art ◽  
Early Recognition ◽  
Pulseless Electrical Activity ◽  
Spontaneous Circulation ◽  
Bayesian Optimization ◽  
Pulse Detection ◽  
Electrocardiogram Ecg ◽  
Hospital Cardiac Arrest

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.

scholarly journals Development of machine learning model for predicting hospitalization in the prehospital setting

2021 ◽  
Author(s):  
Kenichiro Morisawa ◽  
Tadahiro Goto ◽  
Shigeki Fujitani
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Patient Outcomes ◽  
Vital Signs ◽  
Learning Model ◽  
Prehospital Setting ◽  
Early Warning Score ◽  
Prediction Ability ◽  
Machine Learning Model ◽  
Better Than

Background: Studies have developed models for predicting patient outcomes for successful risk stratification in the prehospital setting. However, these models generally require many predictors to achieve high prediction ability, resulting in a bar for implementing models in the real clinical setting. Objective: We aimed to develop a simple and implementable machine learning model using automatically-collected data (age, sex, vital signs) to predict patient outcomes during transportation in comparison with National Early Warning Score (NEWS). Methods: This is a retrospective cohort study using data from the ED of three tertiary care hospitals in Japan from April 2017 to March 2020. We included adult patients (aged over 18 years) who were transported to the ED of participating hospitals. We excluded patients with trauma/injury, cardiac arrest, transferred from other hospitals, patients with missing vital signs data, or having data of obvious outliers. The predictors were patient age, sex, mental status evaluated with Japan Coma Scale, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, and oxygen saturation. The primary outcome was hospitalization. We developed a model using XGBoost. Results: During the study period, 3528 visits transported by emergency medical services were eligible. The median NEWS was 4.0, and 2081 patients were hospitalized. The discrimination ability of the newly developed model was 0.70 (95%CI 0.67-0.73), which was better than those of NEWS 0.64 (95%CI 0.61-0.68). The newly developed models performance measures (e.g., sensitivity, specificity) were comparable with NEWS. Conclusions: Our newly developed machine learning model using routinely available data has moderate prediction ability and was better than NEWS.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

Sign in / Sign up

Export Citation Format

Share Document