Neurocardiovascular coupling in congenital diaphragmatic hernia patients undergoing different types of surgical treatment.

BACKGROUND: The effect of peri-operative management on the neonatal brain is largely unknown. Triggers for perioperative brain injury might be revealed by studying changes in neonatal physiology peri-operatively. OBJECTIVE: To study neonatal pathophysiology and cerebral blood flow regulation peri-operatively using the neuro-cardiovascular graph. DESIGN: Observational, prospective cohort study on perioperative neuromonitoring. Neonates were included between July 2018 and April 2020. SETTING: Multicentre study in two high-volume tertiary university hospitals. PATIENTS: Neonates with congenital diaphragmatic hernia were eligible if they received surgical treatment within the first 28 days of life. Exclusion criteria were major cardiac or chromosomal anomalies, or syndromes associated with altered cerebral perfusion or major neurodevelopmental impairment. The neonates were stratified into different groups by type of peri-operative management. INTERVENTION: Each patient was monitored using near-infrared spectroscopy and EEG in addition to the routine peri-operative monitoring. Neurocardiovascular graphs were computed off-line. MAIN OUTCOME MEASURES: The primary endpoint was the difference in neurocardiovascular graph connectivity in the groups over time. RESULTS: Thirty-six patients were included. The intraoperative graph connectivity decreased in all patients operated upon in the operation room (OR) with sevoflurane-based anaesthesia ( P  < 0.001) but remained stable in all patients operated upon in the neonatal intensive care unit (NICU) with midazolam-based anaesthesia. Thoracoscopic surgery in the OR was associated with the largest median connectivity reduction (0.33 to 0.12, P  < 0.001) and a loss of baroreflex and neurovascular coupling. During open surgery in the OR, all regulation mechanisms remained intact. Open surgery in the NICU was associated with the highest neurovascular coupling values. CONCLUSION: Neurocardiovascular graphs provided more insight into the effect of the peri-operative management on the pathophysiology of neonates undergoing surgery. The neonate's clinical condition as well as the surgical and the anaesthesiological approach affected the neonatal physiology and CBF regulation mechanisms at different levels. TRIAL REGISTRATION: NL6972, URL: https://www.trialre-gister.nl/trial/6972 .

Interrater agreement in visual scoring of neonatal seizures based on majority voting on a web-based system: The Neoguard EEG database.

OBJECTIVE: To assess interrater agreement based on majority voting in visual scoring of neonatal seizures. METHODS: An online platform was designed based on a multicentre seizure EEG-database. Consensus decision based on 'majority voting' and interrater agreement was estimated using Fleiss' Kappa. The influences of different factors on agreement were determined. RESULTS: 1919 Events extracted from 280h EEG of 71 neonates were reviewed by 4 raters. Majority voting was applied to assign a seizure/non-seizure classification. 44% of events were classified with high, 36% with moderate, and 20% with poor agreement, resulting in a Kappa value of 0.39. 68% of events were labelled as seizures, and in 46%, all raters were convinced about electrographic seizures. The most common seizure duration was <30s. Raters agreed best for seizures lasting 60-120s. There was a significant difference in electrographic characteristics of seizures versus dubious events, with seizures having longer duration, higher power and amplitude. CONCLUSIONS: There is a wide variability in identifying rhythmic ictal and non-ictal EEG events, and only the most robust ictal patterns are consistently agreed upon. Database composition and electrographic characteristics are important factors that influence interrater agreement. SIGNIFICANCE: The use of well-described databases and input of different experts will improve neonatal EEG interpretation and help to develop uniform seizure definitions, useful for evidence-based studies of seizure recognition and management.

Application of kernel principal component analysis for single-lead-ECG-derived respiration.

Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by nonlinear mapping of the data, using a kernel function, into a higher dimensional space in which PCA is carried out. The comparison of several kernels suggests that a radial basis function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried out by tuning the parameter σ(2) that represents the variance of the RBF kernel. The performance of kPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation and the magnitude squared coherence coefficients. When comparing the coefficients of the tuned EDR signals using kPCA to EDR signals obtained using PCA and the algorithm based on the R peak amplitude, statistically significant differences are found in the correlation and coherence coefficients (both p<0.0001), showing that kPCA outperforms PCA and R peak amplitude in the extraction of a respiratory signal from single-lead ECGs.