A method for continuous rhythm classification and early detection of ventricular fibrillation during CPR.
Aim: We developed a method which continuously classifies the ECG rhythm during CPR in order to guide clinical care.Methods: We conducted a retrospective study of 432 patients treated following out-of-hospital cardiac arrest. Continuous ECG sequences from two-minute CPR cycles were extracted from def...
| Publicado en: | Resuscitation Vol. 176; pp. 90 - 98 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jul2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157618430&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157618430 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03009572 3PQ jtl: Resuscitation issn: 03009572 maglogo: N pubinfo: dt: Jul2022 vid: 176 pid: 1004 pub: Elsevier B.V. artinfo: ui: 157618430 157618430 NLM35662667 157618430 10.1016/j.resuscitation.2022.05.019 NLM35662667 157618430 ppf: 90 ppct: 8 formats: tig: atl: A method for continuous rhythm classification and early detection of ventricular fibrillation during CPR. aug: au: Kwok, Heemun Coult, Jason Blackwood, Jennifer Sotoodehnia, Nona Kudenchuk, Peter Rea, Thomas affil: Department of Emergency Medicine, University of Washington, Seattle, WA, United States sug: subj: Resuscitation, Cardiopulmonary Methods Electrocardiography Methods Ventricular Fibrillation Therapy Ventricular Fibrillation Diagnosis Arrhythmia Therapy Cardioversion Methods Retrospective Design Scales ab: Aim: We developed a method which continuously classifies the ECG rhythm during CPR in order to guide clinical care.Methods: We conducted a retrospective study of 432 patients treated following out-of-hospital cardiac arrest. Continuous ECG sequences from two-minute CPR cycles were extracted from defibrillator recordings and further divided into five-second clips. We developed an algorithm using wavelet analysis, hidden semi-Markov modeling, and random forest classification. The algorithm classifies individual clips as asystole, organized rhythm, ventricular fibrillation, or Inconclusive while integrating information from previous clips. Classifications were compared to manual annotations to estimate accuracy in an independent validation dataset. Continuous sequences were classified as shockable, non-shockable, or Inconclusive; classifications were used to compute shock sensitivity and specificity.Results: Of 432 patient-cases, 290 were used for development and 142 for validation. In the 12,294 validation ECG clips during CPR, accuracies were 0.88 (95% CI 0.85-0.91) for asystole, 0.98 (95% CI 0.98-0.99) for organized rhythm, and 0.97 (95% CI 0.96-0.97) for ventricular fibrillation, with 43% classified as Inconclusive. Of 457 continuous sequences, shock sensitivity was 0.90 (95% CI 0.86-0.93), shock specificity was 0.98 (95% CI 0.93-0.99), and 7% were Inconclusive. Median delay to ventricular fibrillation recognition was 10 (IQR 5-32) seconds.Conclusion: A novel algorithm continuously classified the primary resuscitation rhythms-asystole, organized rhythms, and ventricular fibrillation-with 88-98% accuracy, enabling accurate shock advisory guidance during most two-minute CPR cycles. Additional investigation is required to understand how algorithm implementation could affect rescuer actions and clinical outcomes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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