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Background: Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refractory VF/VT in OHCA patients. Methods: This was a retrospective study examining adult non-traumatic OHCA patients brought into the emergency department by Singapore EMS from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. Data from April 2010 to March 2020 were extracted for this study. Refractory VF/VT was defined as VF/VT persisting or recurring after at least one shock. Features were selected based on expert clinical opinion and availability to dispatch prior to arrival at scene. Multivariable logistic regression (MVR), LASSO and random forest (RF) models were investigated. Model performance was evaluated using receiver operator characteristic (ROC) area under curve (AUC) analysis and calibration plots. Results: 20,713 patients were included in this study, of which 860 (4.1%) fulfilled the criteria for refractory VF/VT. All models performed comparably and were moderately well-calibrated. ROC-AUC were 0.732 (95% CI, 0.695 - 0.769) for MVR, 0.738 (95% CI, 0.701 - 0.774) for LASSO, and 0.731 (95% CI, 0.690 - 0.773) for RF. The shared important predictors across all models included male gender and public location. Conclusion: The machine learning models developed have potential clinical utility to improve outcomes in cases of refractory VF/VT OHCA. Prediction of refractory VF/VT prior to arrival at patient's side may allow for increased options for intervention both by EMS and tertiary care centres.
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BACKGROUND: The DOSE VF randomized controlled trial (RCT) employed a pragmatic definition of refractory ventricular fibrillation (VF after three successive shocks). However, it remains unclear whether the underlying rhythm during the first three shocks was shock-refractory or recurrent VF. OBJECTIVE: To explore the relationship between alternate defibrillation strategies employed during the DOSE VF RCT and the type of VF, either shock-refractory VF or recurrent VF, on patient outcomes. METHODS: We performed a secondary analysis of the DOSE VF RCT. We categorized cases as shock-refractory or recurrent VF based on pre-randomization shocks (shocks 1-3). We then analyzed all subsequent (post-randomization) shocks to assess the impact of standard, vector change (VC) or double sequential external defibrillation (DSED) shocks on clinical outcomes employing logistic regression adjusted for Utstein variables, antiarrhythmics, and epinephrine. RESULTS: We included 345 patients; 60 (17%) shock-refractory VF, and 285 (83%) recurrent VF. Patients in recurrent VF had greater survival than shock-refractory VF (OR: 2.76 95% CI [1.04, 7.27]). DSED was superior to standard defibrillation for survival overall, and for patients with shock-refractory VF (28.6% vs 0%, p = 0.041) but not for those in recurrent VF. DSED was superior to standard defibrillation for return of spontaneous circulation (ROSC) and neurologic survival for shock-refractory and recurrent VF. VC defibrillation was not superior for survival or ROSC overall, for shock-refractory, or recurrent VF groups, but was superior for VF termination across all groups. CONCLUSION: DSED appears to be the superior defibrillation strategy in the DOSE VF trial, irrespective of whether the preceding VF is shock-refractory or recurrent.
Asunto(s)
Cardioversión Eléctrica , Paro Cardíaco Extrahospitalario , Recurrencia , Fibrilación Ventricular , Humanos , Fibrilación Ventricular/terapia , Fibrilación Ventricular/complicaciones , Cardioversión Eléctrica/métodos , Masculino , Femenino , Persona de Mediana Edad , Anciano , Paro Cardíaco Extrahospitalario/terapia , Paro Cardíaco Extrahospitalario/mortalidad , Reanimación Cardiopulmonar/métodosRESUMEN
OBJECTIVES: We evaluated the feasibility of optimising coronary perfusion pressure (CPP) during cardiopulmonary resuscitation (CPR) with a closed-loop, machine-controlled CPR system (MC-CPR) that sends real-time haemodynamic feedback to a set of machine learning and control algorithms which determine compression/decompression characteristics over time. BACKGROUND: American Heart Association CPR guidelines (AHA-CPR) and standard mechanical devices employ a "one-size-fits-all" approach to CPR that fails to adjust compressions over time or individualise therapy, thus leading to deterioration of CPR effectiveness as duration exceeds 15-20 âmin. METHODS: CPR was administered for 30 âmin in a validated porcine model of cardiac arrest. Intubated anaesthetised pigs were randomly assigned to receive MC-CPR (6), mechanical CPR conducted according to AHA-CPR (6), or human-controlled CPR (HC-CPR) (10). MC-CPR directly controlled the CPR piston's amplitude of compression and decompression to maximise CPP over time. In HC-CPR a physician controlled the piston amplitudes to maximise CPP without any algorithmic feedback, while AHA-CPR had one compression depth without adaptation. RESULTS: MC-CPR significantly improved CPP throughout the 30-min resuscitation period compared to both AHA-CPR and HC-CPR. CPP and carotid blood flow (CBF) remained stable or improved with MC-CPR but deteriorated with AHA-CPR. HC-CPR showed initial but transient improvement that dissipated over time. CONCLUSION: Machine learning implemented in a closed-loop system successfully controlled CPR for 30 âmin in our preclinical model. MC-CPR significantly improved CPP and CBF compared to AHA-CPR and ameliorated the temporal haemodynamic deterioration that occurs with standard approaches.