Electroencephalographic indices for clinical endpoints during propofol anesthesia in infants-an early phase propofol biomarker-finding study.

BACKGROUND: Unlike expired sevoflurane concentration, propofol lacks a biomarker for its brain effect site concentration (Ce), leading to dosing imprecision particularly in infants. Electroencephalography (EEG) monitoring can serve as a biomarker for propofol Ce, yet proprietary EEG indices are not validated in infants. We evaluated spectral edge frequency (SEF95) as a propofol anesthesia biomarker in infants. We hypothesized that the SEF95 targets will vary for different clinical stimuli and an inverse relationship existed between SEF95 and propofol plasma concentration. METHODS: This prospective study enrolled infants (3-12 months) to determine the SEF95 ranges for three clinical endpoints of anesthesia (consciousness-pacifier placement, pain-electrical nerve stimulation, and intubation-laryngoscopy) and correlation between SEF95 and propofol plasma concentration at steady state. Dixon's Up-Down method was used to determine target SEF95 for each clinical endpoint. Centered isotonic regression determined the dose-response function of SEF95 where 50% and 90% of infants (ED50 and ED90) did not respond to the clinical endpoint. Linear mixed-effect model determined the association of propofol plasma concentration and SEF95. RESULTS: Of 49 enrolled infants, 44 evaluable (90%) showed distinct SEF95 for endpoints: pacifier (ED50 21.4Hz, ED90 19.3Hz), electrical stimulation (ED50 12.6Hz, ED90 10.4Hz), and laryngoscopy (ED50 8.5Hz, ED90 5.2Hz). From propofol 0.5-6 µg/ml, a 1 Hz SEF95 increase was linearly correlated to a 0.24 (95% CI: 0.19 - 0.29, p<0.001) µg/mL decrease in plasma propofol concentration (marginal R 2 = 0.55). CONCLUSIONS: SEF95 can be a biomarker for propofol anesthesia depth in infants, potentially improving dosing accuracy and utilization of propofol anesthesia in this population.

Quantitative electroencephalogram in term neonates under different sleep states.

Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46 weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness.

The seizure severity score: a quantitative tool for comparing seizures and their response to therapy.

Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.

Reducing the environmental impact of mask inductions in children: A quality improvement report.

BACKGROUND: Inhalational anesthetic agents are potent greenhouse gases with global warming potential that far exceed that of carbon dioxide. Traditionally, pediatric inhalation inductions are achieved with a volatile anesthetic delivered to the patient in oxygen and nitrous oxide at high fresh gas flows. While contemporary volatile anesthetics and anesthesia machines allow for a more environmentally conscious induction, practice has not changed. We aimed to reduce the environmental impact of our inhalation inductions by decreasing the use of nitrous oxide and fresh gas flows. METHODS: Through a series of four plan-do-study-act cycles, the improvement team used content experts to demonstrate the environmental impact of the current inductions and to provide practical ways to reduce this, by focusing on nitrous oxide use and fresh gas flows, with visual reminders introduced at point of delivery. The primary measures were the percentage of inhalation inductions that used nitrous oxide and the maximum fresh gas flows/kg during the induction period. Statistical process control charts were used to measure improvement over time. RESULTS: 33 285 inhalation inductions were included over a 20-month period. nitrous oxide use decreased from 80% to <20% and maximum fresh gas flows/kg decreased from a rate of 0.53 L/min/kg to 0.38 L/min/kg, an overall reduction of 28%. Reduction in fresh gas flows was greatest in the lightest weight groups. Induction times and behaviors remained unchanged over the duration of this project. CONCLUSIONS: Our quality improvement group decreased the environmental impact of inhalation inductions and created cultural change within our department to sustain change and foster the pursuit of future environmental efforts.