title: Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes.
abstract: OBJECTIVE
  The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals.
  
  
  METHODS
  Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique.
  
  
  RESULTS
  358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent > 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity.
  
  
  CONCLUSIONS
  At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO2) signal improves accuracy and sensitivity of the MDI prediction model.
authors.affiliation: Virginia Commonwealth University, Richmond, Virginia, United States of America.
authors.forename: Sharad
authors.initials: S
authors.lastname: Shandilya
authors.affiliation: Department of Emergency Medicine, University of Alabama School of Medicine, Birmingham, Alabama, United States of America.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Kurz
authors.affiliation: Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.|Michigan Center for Integrative Research in Critical Care, Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Kevin R
authors.initials: KR
authors.lastname: Ward
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.|Michigan Center for Integrative Research in Critical Care, Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Kayvan
authors.initials: K
authors.lastname: Najarian
mesh_terms: D000465:Algorithms; D002245:Carbon Dioxide / Q000378:metabolism; D016887:Cardiopulmonary Resuscitation / Q000379:methods*; D004554:Electric Countershock; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006321:Heart / Q000503:physiopathology; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D058687:Out-of-Hospital Cardiac Arrest / Q000378:metabolism / Q000503:physiopathology / Q000628:therapy*; D012372:ROC Curve; D012189:Retrospective Studies; D013990:Tidal Volume; D013997:Time Factors; D016896:Treatment Outcome; D014693:Ventricular Fibrillation / Q000378:metabolism / Q000503:physiopathology / Q000628:therapy*
chemical_list: D002245:Carbon Dioxide