Pulse oximetry waveform: A non-invasive physiological predictor for the return of spontaneous circulation in cardiac arrest patients ---- A multicenter, prospective observational study

Prognostic value of grey-white matter ratio obtained within two hours after return of spontaneous circulation in out-of-hospital cardiac arrest survivors: A multicenter, observational study

Background

Grey-white matter ratio (GWR) measured by head computed tomography (CT) scan is known as a neurological prognostication tool for out-of-hospital cardiac arrest (OHCA) survivors. The prognostic value of GWR obtained early (within two hours after return of spontaneous circulation [ROSC]) remains a matter of debate.

Methods

We conducted a multicenter, retrospective, observational study at five hospitals. We included adult OHCA survivors who underwent head CT within two hours following ROSC. GWR values were measured using head CT. Average GWR values were calculated by the mean of the GWR-basal ganglia and GWR-Cerebrum. We divided the patients into poor or favorable neurological outcome groups defined by Glasgow-Pittsburgh Cerebral Performance Category scores. The predictive accuracy of GWR performance was assessed using the area under the curve (AUC). The sensitivities and specificities for predicting poor outcome were examined.

Results

Of 377 eligible patients, 281 (74.5%) showed poor neurological outcomes at one month after ROSC. Average GWR values of the poor neurological outcome group were significantly lower than those of the favorable neurological outcome. The average GWR value to predict neurological outcome with Youden index was 1.24 with AUC of 0.799. When average GWR values were 1.15 or lower, poor neurological outcomes could be predicted with 100% specificity.

Conclusions

GWR values measured by head CT scans early (within two hours after ROSC) demonstrated moderate predictive performance for overall ROSC patients. When limited to the patients with GWR values of 1.15 or lower, poor neurological outcomes could be predicted with high specificity.

A novel predictor of unsustained return of spontaneous circulation in cardiac arrest patients through a combination of capnography and pulse oximetry: a multicenter observational study

BACKGROUND

Unsustained return of spontaneous circulation (ROSC) is a critical barrier to survival in cardiac arrest patients. This study examined whether end-tidal carbon dioxide (ETCO2) and pulse oximetry photoplethysmogram (POP) parameters can be used to identify unsustained ROSC.

METHODS

We conducted a multicenter observational prospective cohort study of consecutive patients with cardiac arrest from 2013 to 2014. Patients' general information, ETCO2, and POP parameters were collected and statistically analyzed.

RESULTS

The included 105 ROSC episodes (from 80 cardiac arrest patients) comprised 51 sustained ROSC episodes and 54 unsustained ROSC episodes. The 24-hour survival rate was significantly higher in the sustained ROSC group than in the unsustained ROSC group (29.2% vs. 9.4%, P<0.05). The logistic regression analysis showed that the difference between after and before ROSC in ETCO2 (ΔETCO2) and the difference between after and before ROCS in area under the curve of POP (ΔAUCp) were independently associated with sustained ROSC (odds ratio [OR]=0.931, 95% confidence interval [95% CI] 0.881-0.984, P=0.011 and OR=0.998, 95% CI 0.997-0.999, P<0.001). The area under the receiver operating characteristic curve of ΔETCO2, ΔAUCp, and the combination of both to predict unsustained ROSC were 0.752 (95% CI 0.660-0.844), 0.883 (95% CI 0.818-0.948), and 0.902 (95% CI 0.842-0.962), respectively.

CONCLUSION

Patients with unsustained ROSC have a poor prognosis. The combination of ΔETCO2 and ΔAUCp showed significant predictive value for unsustained ROSC.

Prediction of Return of Spontaneous Circulation in a Pediatric Swine Model of Cardiac Arrest Using Low-Resolution Multimodal Physiological Waveforms

Monitoring physiological waveforms, specifically hemodynamic variables (e.g., blood pressure waveforms) and end-tidal CO2 (EtCO2), during pediatric cardiopulmonary resuscitation (CPR) has been demonstrated to improve survival rates and outcomes when compared to standard depth-guided CPR. However, waveform guidance has largely been based on thresholds for single parameters and therefore does not leverage all the information contained in multimodal data. We hypothesize that the combination of multimodal physiological features improves the prediction of the return of spontaneous circulation (ROSC), the clinical indicator of short-term CPR success. We used machine learning algorithms to evaluate features extracted from eight low-resolution (4 samples per minute) physiological waveforms to predict ROSC. The waveforms were acquired from the 2nd to 10th minute of CPR in pediatric swine models of cardiac arrest (N = 89, 8-12 kg). The waveforms were divided into segments with increasing length (both forward and backward) for feature extraction, and machine learning algorithms were trained for ROSC prediction. For the full CPR period (2nd to 10th minute), the area under the receiver operating characteristics curve (AUC) was 0.93 (95% CI: 0.87-0.99) for the multivariate model, 0.70 (0.55-0.85) for EtCO2 and 0.80 (0.67-0.93) for coronary perfusion pressure. The best prediction performances were achieved when the period from the 6th to the 10th minute was included. Poor predictions were observed for some individual waveforms, e.g., right atrial pressure. In conclusion, multimodal waveform features carry relevant information for ROSC prediction. Using multimodal waveform features in CPR guidance has the potential to improve resuscitation success and reduce mortality.