Neuroprotection after cardiac arrest with 2-iminobiotin: a single center phase IIa study on safety, tolerability, and pharmacokinetics

Background

Brain injury is a serious problem in patients who survive out-of-hospital cardiac arrest (OHCA). Neuroprotective drugs could reduce hypoxic-ischemic reperfusion injury. The aim of this study was to investigate the safety, tolerability, and pharmacokinetics (PK) of 2-iminobiotin (2-IB), a selective inhibitor of neuronal nitric oxide synthase.

Methods

Single-center, open-label dose-escalation study in adult OHCA patients, investigating three 2-IB dosing schedules (targeting an AUC_0-24h of 600-1,200 ng*h/m in cohort A, of 2,100-3,300 ng*h/mL in cohort B, and 7,200-8,400 of ng*h/mL in cohort C). Safety was investigated by monitoring vital signs until 15 min after study drug administration and adverse events up to 30 days after admission. Blood sampling for PK analysis was performed. Brain biomarkers and patient outcomes were collected 30 days after OHCA.

Results

A total of 21 patients was included, eight in cohort A and B and five in cohort C. No changes in vital signs were observed, and no adverse events related to 2-IB were reported. A two-compartment PK model described data the best. Exposure in group A (dosed on bodyweight) was three times higher than targeted (median AUC_0-24h 2,398 ng*h/mL). Renal function was an important covariate; therefore, in cohort B, dosing was performed on eGFR on admission. In cohort B and C, the targeted exposure was met (median AUC_0-24h 2,917 and 7,323 ng*h/mL, respectively).

Conclusion

The administration of 2-IB to adults after OHCA is feasible and safe. PK can be well predicted with correction for renal function on admission. Efficacy studies with 2-IB after OHCA are needed.

Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest

OBJECTIVE

To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA).

METHODS

Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3-5 within 6 months.

RESULTS

The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80-86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40-51) and 89% specificity (95%-CI 86-92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83-88) at 24 h after CA, with 62% sensitivity (95%-CI 57-67) and 84% specificity (95%-CI 79-88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives.

CONCLUSIONS

Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data.

SIGNIFICANCE

Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.

Electroencephalographic reactivity testing in unconscious patients: a systematic review of methods and definitions

Electroencephalographic (EEG) reactivity testing is often presented as a clear-cut element of electrophysiological testing. Absence of EEG reactivity is generally considered an indicator of poor outcome, especially in patients after cardiac arrest. However, guidelines do not clearly describe how to test for reactivity and how to evaluate the results. In a quest for clear guidelines, we performed a systematic review aimed at identifying testing methods and definitions of EEG reactivity. We systematically searched the literature between 1970 and May 2016. Methodological quality of the studies was assessed using the QUality In Prognostic Studies tool. Quality of the descriptions of stimulus protocol and reactivity definition was rated on a four-category grading scale based on reproducibility. We found that protocols for EEG reactivity testing vary greatly and descriptions of protocols are almost never replicable. Furthermore, replicable definitions of presence or absence of EEG reactivity are never provided. In order to draw firm conclusions on EEG reactivity as a prognostic factor, future studies should include a precise stimulation protocol and reactivity definition to facilitate guideline formation.

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine: Brussels, Belgium. 15-18 March 2016

[This corrects the article DOI: 10.1186/s13054-016-1208-6.].