Deep Neural Networks for ECG-Based Pulse Detection during Out-of-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.This work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque Government through the grant PRE_2018_2_0260

Review of 14 drowning publications based on the Utstein style for drowning

Abstract Background The Utstein style for drowning (USFD) was published in 2003 with the aim of improving drowning research. To support a revision of the USFD, the current study aimed to generate an inventory of the use of the USFD parameters and compare the findings of the publications that have used the USFD. Methods A search in Pubmed, Embase, the Cochrane Library, Web of Science and Scopus was performed to identify studies that used the USFD and were published between 01-10-2003 and 22-03-2015. We also searched in Pubmed, Embase, the Cochrane Library, Web of Science, and Scopus for all publications that cited the two publications containing the original ILCOR advisory statement introducing and recommending the USFD. In total we identified 14 publications by groups that explicitly used elements of the USFD for collecting and reporting their data. Results Of the 22 core and 19 supplemental USFD parameters, 6–19 core (27–86%) and 1–12 (5–63%) supplemental parameters were used; two parameters (5%) have not been used in any publication. Associations with outcome were reported for nine core (41%) and five supplemental (26%) USFD parameters. The USFD publications also identified non-USFD parameters related to outcome: initial cardiac rhythm, time points and intervals during resuscitation, intubation at the drowning scene, first hospital core temperature, serum glucose and potassium, the use of inotropic/vasoactive agents and the Paediatric Index of Mortality 2-score. Conclusions Fourteen USFD based drowning publications have been identified. These publications provide valuable information about the process and quality of drowning resuscitation and confirm that the USFD is helpful for a structured comparison of the outcome of drowning resuscitation

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-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.This work was supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades through Grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), by the Basque Government through Grant IT1229-19 and Grant PRE2020_1_0177, and by the university of the Basque Country (UPV/EHU) under Grant COLAB20/01

Towards the Prediction of Rearrest during Out-of-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.This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades through grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), and by the Basque Government through grants IT1229-19, PRE_2019_2_0100 and PRE_2019_1_0262. A.I. receives research grants from the US National Institutes of Health (NIH)

Estrogen treatment following severe burn injury reduces brain inflammation and apoptotic signaling

<p>Abstract</p> <p>Background</p> <p>Patients with severe burn injury experience a rapid elevation in multiple circulating pro-inflammatory cytokines, with the levels correlating with both injury severity and outcome. Accumulations of these cytokines in animal models have been observed in remote organs, however data are lacking regarding early brain cytokine levels following burn injury, and the effects of estradiol on these levels. Using an experimental animal model, we studied the acute effects of a full-thickness third degree burn on brain levels of TNF-α, IL-1β, and IL-6 and the protective effects of acute estrogen treatment on these levels. Additionally, the acute administration of estrogen on regulation of inflammatory and apoptotic events in the brain following severe burn injury were studied through measuring the levels of phospho-ERK, phospho-Akt, active caspase-3, and PARP cleavage in the placebo and estrogen treated groups.</p> <p>Methods</p> <p>In this study, 149 adult Sprague-Dawley male rats received 3rd degree 40% total body surface area (TBSA) burns. Fifteen minutes following burn injury, the animals received a subcutaneous injection of either placebo (n = 72) or 17 beta-estradiol (n = 72). Brains were harvested at 0.5, 1, 2, 4, 6, 8, 12, 18, and 24 hours after injury from the control (n = 5), placebo (n = 8/time point), and estrogen treated animals (n = 8/time point). The brain cytokine levels were measured using the ELISA method. In addition, we assessed the levels of phosphorylated-ERK, phosphorylated-Akt, active caspase-3, and the levels of cleaved PARP at the 24 hour time-point using Western blot analysis.</p> <p>Results</p> <p>In burned rats, 17 beta-estradiol significantly decreased the levels of brain tissue TNF-α (~25%), IL-1β (~60%), and IL-6 (~90%) when compared to the placebo group. In addition, we determined that in the estrogen-treated rats there was an increase in the levels of phospho-ERK (<it>p </it>< 0.01) and Akt (<it>p </it>< 0.05) at the 24 hour time-point, and that 17 beta-estradiol blocked the activation of caspase-3 (<it>p </it>< 0.01) and subsequent cleavage of PARP (<it>p </it>< 0.05).</p> <p>Conclusion</p> <p>Following severe burn injury, estrogens decrease both brain inflammation and the activation of apoptosis, represented by an increase in the levels of phospho-Akt and inhibition of caspase-3 activation and PARP cleavage. Results from these studies will help further our understanding of how estrogens protect the brain following burn injury, and may provide a novel, safe, and effective clinical treatment to combat remote secondary burn injury in the brain and to preserve cognition.</p

Algoritmo multietapa para la detección de ventilaciones en la impedancia torácica durante la resucitación cardiopulmonar

La resucitación cardiopulmonar (RCP) es clave en el tratamiento de la parada cardiorrespiratoria extra-hospitalaria (PCREH). La impedancia torácica (IT) adquirida a través de los parches de un desfibrilador permite detectar las ventilaciones para proveer al rescatador de realimentación sobre el manejo de la vía aérea, pero presenta artefactos debidos a las compresiones torácicas. El objetivo de este trabajo fue el desarrollo de un algoritmo para la detección de ventilaciones en la IT durante compresiones concurrentes. Se analizaron un total de 152 episodios de PCREH, y se anotaron 9665 ventilaciones de referencia en el capnograma. El método constó de tres etapas: procesado de señal para la extracción de la componente de ventilación de la IT, incluyendo un bloque de filtrado adaptativo para eliminar el artefacto de compresiones, detección y caracterización de formas de onda de ventilación, y clasificación mediante una máquina de vectores de soporte para discriminar falsos positivos. Los pacientes, uno por episodio, fueron divididos en grupos de entrenamiento (70%) y evaluación (30%). Se utilizaron 100 particiones diferentes a fin de reducir el sesgo. Las métricas de desempeño finales mostraron valores medianos por paciente de 87.6% de sensibilidad y 85.0% de valor predictivo positivo. El algoritmo podría utilizarse para proporcionar realimentación al rescatador en términos de tasa de ventilación y volúmenes de aire insuflado.Este trabajo ha recibido ayuda financiera del Ministerio de Ciencia, Innovación y Universidades, proyecto RTI2018- 101475-BI00, junto con el Fondo Europeo de Desarrollo Regional (FEDER), así como del Gobierno Vasco a través de la subvención a grupos de investigación IT-1229-19 y la beca pre-doctoral PRE-2019-1-0209

Algoritmo multietapa para la detección de ventilaciones en la impedancia torácica durante la resucitación cardiopulmonar

La resucitación cardiopulmonar (RCP) es clave en el tratamiento de la parada cardiorrespiratoria extra-hospitalaria (PCREH). La impedancia torácica (IT) adquirida a través de los parches de un desfibrilador permite detectar las ventilaciones para proveer al rescatador de realimentación sobre el manejo de la vía aérea, pero presenta artefactos debidos a las compresiones torácicas. El objetivo de este trabajo fue el desarrollo de un algoritmo para la detección de ventilaciones en la IT durante compresiones concurrentes. Se analizaron un total de 152 episodios de PCREH, y se anotaron 9665 ventilaciones de referencia en el capnograma. El método constó de tres etapas: procesado de señal para la extracción de la componente de ventilación de la IT, incluyendo un bloque de filtrado adaptativo para eliminar el artefacto de compresiones, detección y caracterización de formas de onda de ventilación, y clasificación mediante una máquina de vectores de soporte para discriminar falsos positivos. Los pacientes, uno por episodio, fueron divididos en grupos de entrenamiento (70%) y evaluación (30%). Se utilizaron 100 particiones diferentes a fin de reducir el sesgo. Las métricas de desempeño finales mostraron valores medianos por paciente de 87.6% de sensibilidad y 85.0% de valor predictivo positivo. El algoritmo podría utilizarse para proporcionar realimentación al rescatador en términos de tasa de ventilación y volúmenes de aire insuflado.Este trabajo ha recibido ayuda financiera del Ministerio de Ciencia, Innovación y Universidades, proyecto RTI2018- 101475-BI00, junto con el Fondo Europeo de Desarrollo Regional (FEDER), así como del Gobierno Vasco a través de la subvención a grupos de investigación IT-1229-19 y la beca pre-doctoral PRE-2019-1-0209

Modelo predictivo del retorno de circulación espontánea en la parada cardiorrespiratoria utilizando el ECG y la impedancia torácica

El análisis de los diferentes tipos de ritmo cardíaco durante la parada cardiorrespiratoria y la predicción de su evolución permitiría ajustar la terapia de resucitación a cada paciente. El ritmo con actividad eléctrica sin pulso (AESP) es el ritmo inicial predominante durante la parada cardiorrespiratoria extrahospitalaria, y es de gran interés disponer de modelos que predigan el retorno espontáneo de circulación (RCE). En este trabajo se propone un método automático que discrimina los casos de AESP que evolucionan a RCE de los que no recuperan el pulso. El modelo combina parámetros de las señales de electrocardiograma (ECG) e impedancia torácica (IT) adquiridas con los parches del desfibrilador. La base de datos consiste en 185 pacientes (73 con RCE) de los que se extrajeron 1600 segmentos (432 con RCE). Aplicando una validación cruzada de 10 particiones y un clasificador de máquinas de vectores de soporte (SVM), se demuestra que la IT añade valor discriminativo al modelo basado en ECG. Para un clasificador SVM con un núcleo polinómico de orden 2 se obtuvo una sensibilidad del 79.8%, una especificidad del 85.5% y un área bajo la curva ROC de 0.91.Este trabajo ha sido parcialmente financiado por el Ministerio de Ciencia, Innovación y Universidades a través del proyecto RTI2018-101475-BI00, en conjunto con el Fondo Europeo de Desarrollo Regional (FEDER), y en parte por el Gobierno Vasco por medio del proyecto IT- 1229-19

Three-Month Symptom Profiles Among Symptomatic Adults With Positive and Negative Severe Acute Respiratory Syndrome Coronavirus 2 Tests: A Prospective Cohort Study From the INSPIRE Group.

BACKGROUND: Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood. METHODS: We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID-), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email. RESULTS: Among the first 1000 participants, 722 were COVID+ and 278 were COVID-. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID- group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID- groups at 3 months. CONCLUSIONS: Approximately half of COVID+ participants, as compared with one-quarter of COVID- participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID. CLINICAL TRIALS REGISTRATION: NCT04610515