Electronic Resource
A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest
| العنوان: | A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest |
|---|---|
| المؤلفون: | Ingeniería de comunicaciones, Matemáticas, Komunikazioen ingeniaritza, Matematika, Urteaga Urizarbarrena, Jon, Aramendi Ecenarro, Elisabete, Elola Artano, Andoni, Irusta Zarandona, Unai, Idris, Ahamed |
| بيانات النشر: | MDPI 2021-06-30 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | 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. |
| مصطلحات الفهرس: | out-of-hospital cardiac arrest (OHCA), electrocardiogram (ECG), thoracic impedance (TI), pulseless electrical activity (PEA), return of spontaneous circulation (ROSC), info:eu-repo/semantics/article |
| URL: | 1099-4300 info:eu-repo/grantAgreement/MICINN/RTI2018-101475-BI00 |
| الاتاحة: | Open access content. Open access content |
| ملاحظة: | 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. English |
| Other Numbers: | ESUPV oai:addi.ehu.es:10810/52624 1286734279 |
| المصدر المساهم: | REPOSITORIO DE LA UNIVERSIDAD DEL PAIS From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1286734279 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |