Associations Between End-Tidal Carbon Dioxide During Pediatric Cardiopulmonary Resuscitation, Cardiopulmonary Resuscitation Quality, and Survival.

BACKGROUND: Supported by laboratory and clinical investigations of adult cardiopulmonary arrest, resuscitation guidelines recommend monitoring end-tidal carbon dioxide (ETCO2) as an indicator of cardiopulmonary resuscitation (CPR) quality, but they note that "specific values to guide therapy have not been established in children." METHODS: This prospective observational cohort study was a National Heart, Lung, and Blood Institute-funded ancillary study of children in the ICU-RESUS trial (Intensive Care Unit-Resuscitation Project; NCT02837497). Hospitalized children (≤18 years of age and ≥37 weeks postgestational age) who received chest compressions of any duration for cardiopulmonary arrest, had an endotracheal or tracheostomy tube at the start of CPR, and evaluable intra-arrest ETCO2 data were included. The primary exposure was event-level average ETCO2 during the first 10 minutes of CPR (dichotomized as ≥20 mm Hg versus <20 mm Hg on the basis of adult literature). The primary outcome was survival to hospital discharge. Secondary outcomes were sustained return of spontaneous circulation, survival to discharge with favorable neurological outcome, and new morbidity among survivors. Poisson regression measured associations between ETCO2 and outcomes as well as the association between ETCO2 and other CPR characteristics: (1) invasively measured systolic and diastolic blood pressures, and (2) CPR quality and chest compression mechanics metrics (ie, time to CPR start; chest compression rate, depth, and fraction; ventilation rate). RESULTS: Among 234 included patients, 133 (57%) had an event-level average ETCO2 ≥20 mm Hg. After controlling for a priori covariates, average ETCO2 ≥20 mm Hg was associated with a higher incidence of survival to hospital discharge (86/133 [65%] versus 48/101 [48%]; adjusted relative risk, 1.33 [95% CI, 1.04-1.69]; P=0.023) and return of spontaneous circulation (95/133 [71%] versus 59/101 [58%]; adjusted relative risk, 1.22 [95% CI, 1.00-1.49]; P=0.046) compared with lower values. ETCO2 ≥20 mm Hg was not associated with survival with favorable neurological outcome or new morbidity among survivors. Average 2 ≥20 mm Hg was associated with higher systolic and diastolic blood pressures during CPR, lower CPR ventilation rates, and briefer pre-CPR arrest durations compared with lower values. Chest compression rate, depth, and fraction did not differ between ETCO2 groups. CONCLUSIONS: In this multicenter study of children with in-hospital cardiopulmonary arrest, ETCO2 ≥20 mm Hg was associated with better outcomes and higher intra-arrest blood pressures, but not with chest compression quality metrics.

MLWAVE: A novel algorithm to classify primary versus secondary asphyxia-associated ventricular fibrillation.

INTRODUCTION/HYPOTHESIS: The outcome of cardiopulmonary resuscitation (CPR) depends on timely recognition of the underlying cause of cardiac arrest. Ventricular fibrillation (VF) waveform analysis to differentiate primary VF from secondary asphyxia-associated VF may allow tailoring of therapies to improve cardiac arrest outcomes. Therefore, the primary goal of this investigation was to develop a novel technique utilizing wavelet synchrosqueezed transform (WSST) and decision-tree classifier that was specifically adapted to discriminate between these two incidents of VF. METHODS: Secondary analytical investigation of electrocardiography (ECG) data obtained from swine models of either primary VF (n=18) or secondary asphyxia-associated VF (7min of asphyxia prior to VF induction; n=12). In the primary analysis, WSST technique was applied to the first 35s of the VF ECG signal to identify the most differentiating characteristics of the signal for use as features to develop a machine learning algorithm to classify the arrest as either primary VF vs. secondary asphyxia-associated VF. The performance of this new interactive Machine Learning algorithm with Wavelet Energy features of ECG (MLWAVE) was assessed using both classification accuracy and area under the receiver operating characteristic curve (AUCROC). To evaluate the validity of the new technique, the amplitude spectrum area (AMSA)-based technique, a well-established defibrillation classification method, was also applied to the same ECG signals. The classification accuracy and AUCROC were then compared between the two techniques. RESULTS: For the primary analysis evaluating the first 35s of the VF waveform, the MLWAVE technique classified the type of VF with high accuracy (28/28 [100%], AUCROC: 1.00). The MLWAVE technique performed better than the AMSA technique across all comparisons, but given the small sample sizes, differences were not statistically significant (accuracy: 100% vs. 85.7%; p=0.24; AUCROC: 1.00 vs. 0.82; p=0.24). CONCLUSION: This analytical investigation illustrates the advantages of the MLWAVE signal processing method which was associated with 100% accuracy in classifying the type of VF waveform: primary vs. asphyxia-associated. Such classification could lead to personalized tailoring of resuscitation (e.g., immediate defibrillation vs. continued CPR and treatment of reversible cardiac arrest causes before defibrillation) to improve outcomes for cardiac arrest.

A Novel Embedded Feature Selection and Dimensionality Reduction Method for an SVM Type Classifier to Predict Periventricular Leukomalacia (PVL) in Neonates.

This paper is concerned with the prediction of the occurrence of periventricular leukomalacia (PVL) in neonates after heart surgery. Our prior work shows that the Support Vector Machine (SVM) classifier can be a powerful tool in predicting clinical outcomes of such complicated and uncommon diseases, even when the number of data samples is low. In the presented work, we first illustrate and discuss the shortcomings of the traditional automatic machine learning (aML) approach. Consequently, we describe our methodology for addressing these shortcomings, while utilizing the designed interactive ML (iML) algorithm. Finally, we conclude with a discussion of the developed method and the results obtained. In sum, by adding an additional (Genetic Algorithm) optimization step in the SVM learning framework, we were able to (a) reduce the dimensionality of an SVM model from 248 to 53 features, (b) increase generalization that was confirmed by a 100% accuracy assessed on an unseen testing set, and (c) improve the overall SVM model's performance from 65% to 100% testing accuracy, utilizing the proposed iML method.

Advanced analytics for outcome prediction in intensive care units.

In this paper we present a new expert knowledge based clinical decision support system for prediction of intensive care units outcome based on the physiological measurements collected during the first 48 hours of the patient's admission to the ICU. The developed CDSS algorithm is composed of several stages. First, we categorize the collected data based on the physiological organ that they represent. We then extract clinically relevant features from each data category and then rank these features based on their mutual information with the outcome. Then, we design an artificial neural network to serve as a classifier to detect patients at high risk of critical deterioration. We use the eight-fold cross validation method to test the developed CDSS classifier. The results from the classification show that the newly designed CDSS outperforms the widely used acuity scoring systems, SOFA and SAPS-III. The F-score classification result of our developed algorithms is 42% while the F-score results for SOFA and SAPS-III are 26% and 29% respectively.