Behnood Gholami

Although electroencephalography (EEG) is used to assess neonatal pain in research settings, EEG assessments have not been sufficiently characterized for use as end points to assess the efficacy of analgesics in regulatory-endorsed, industry-sponsored trials. We aimed to identify all studies conducted in neonates with EEG recordings during acute somatic nociceptive skin-breaking procedures, and to create a network of authors who will be invited to contribute their individual participant data (IPD) to an IPD meta-analysis to establish the validity, reliability, and clinical interpretability of an EEG-based neonatal pain measure. To identify literature, we searched MEDLINE, Embase, CINAHL, Web of Science, Scopus, Google Scholar, ClinicalTrials.gov, and the WHO ICTRP from database inception to July 2, 2025. Eligible studies were primary empirical studies that included neonates with EEG recordings during acute skin-breaking procedures. We identified 55 studies across 11 countries. Heel lance was the most common painful procedure; others included venipuncture, immunization, and lumbar puncture. The impact of 12 analgesic interventions has been studied to date, mostly nonpharmacological interventions. Individual-electrode EEG is more common than EEG caps. We noted relatively high data loss due to EEG data-quality concerns. A wide range of non-EEG pain-relevant measures have been recorded alongside EEG (eg, behavior, vital signs). Coauthorship network analysis highlighted that authors commonly work within discrete authorship hubs, with limited coauthorship across hubs. The predominance of studies was from European and American institutions, which limits generalizability. We conclude that sufficient data are available to undertake an IPD meta-analysis.

Observer‐dependent infant pain scales have limitations including discontinuous assessments and the lack of healthcare professionals' availability. We hypothesized that applying agnostic machine learning approaches to neonatal electroencephalographic (EEG) analysis may reveal features of the infant response to acute pain. EEG was recorded from 30 neonates undergoing acutely painful procedures (18 males, 34.0–41.7 weeks gestation at birth). EEG recordings were randomly assigned to training (n = 20) and testing (n = 10) datasets. Functional connectivity measures were calculated for each infant before and after pain‐inducing procedures. A grid search including five machine learning models was conducted on the training dataset, and each model was evaluated using leave‐one‐subject‐out cross‐validation. An optimal model, having the highest F‐1 score, was obtained and evaluated on the independent testing dataset. A gradient boosting model with 12 features showed optimal performance, with 90% area under the receiver operating characteristic curve suggesting high specificity (0.90) and precision (0.90). The five highest ranked features corresponded to EEG electrode pairs: T7‐P4, Fz‐CP5, FC1‐TP10, CP6‐Cz, and Fz‐F3, suggesting involvement of the contralateral temporal gyrus, opercular cortex, thalamus, and bilateral insula in infant pain processing. Preliminary changes in functional connectivity indicate infant pain processing. Future machine learning algorithms can integrate physiological and behavioral parameters with EEG changes to accurately assess the complexity of infant pain responses.Trial Registration: ClinicalTrials.gov identifier: NCT03330496

Fluid therapy is extensively used to treat traumatized patients as well as patients during surgery. The fluid therapy process is complex due to interpatient variability in response to therapy as well as other complicating factors such as comorbidities and general anesthesia. These complexities can result in under- or over-resuscitation. Given the complexity of the fluid management process as well as the increased capabilities in hemodynamic monitoring, closed-loop fluid management can reduce the workload of the overworked clinician while ensuring specific constraints on hemodynamic endpoints are met with higher accuracy. The goal of this paper is to provide an overview of closed-loop control systems for fluid management and highlight several key steps in transitioning such a technology from bench to the bedside.

We compare the sensitivity and specificity of clinician visual waveform analysis against an automated system’s waveform analysis in detecting ineffective triggering in mechanically ventilated intensive care unit patients when compared against a reference label set based upon analysis of respiratory muscle activity. Electrical activity of the diaphragm or esophageal/transdiaphragmatic pressure waveforms were available to a single clinician for the generation of a reference label set indicating the ground truth, that is, presence or absence of ineffective triggering, on a breath-by-breath basis. Pressure and flow versus time tracings were made available to (i) a group of three clinicians; and (ii) the automated Syncron-E™ system capable of detecting patient-ventilator asynchrony in real-time, in order to obtain breath-by-breath labels indicating the presence or absence of ineffective triggering. The clinicians and the automated system did not have access to other waveforms such as electrical activity of the diaphragm or esophageal/transdiaphragmatic pressure. In total, 926 breaths were analyzed across the seven patients. Specificity for clinicians and the automated system were high (99.3% for clinician and 98.5% for the automated system). The automated system had a significantly higher sensitivity (83.2%) compared to clinicians (41.1%). Ineffective triggering detected by the automated system, which has access only to airway pressure and flow versus time tracings, is in substantial agreement with a reference detection derived from analysis of invasively measured patient effort waveforms.

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algorithm based on the Cluster Newton method, namely the constrained Cluster Newton method, where the initial points of the parameters are constrained by the database. The algorithm was tested with the compartmental model of propofol on a database of 59 subjects. The average overall absolute percentage error based on constrained Cluster Newton method is 12.10% with the threshold approach, and 13.42% with the nearest-neighbor approach. The average computation time of one estimation is 13.10 seconds. Using parallel computing, the average computation time is reduced to 1.54 seconds, achieved with 12 parallel workers. The results suggest that the proposed framework can effectively improve the prediction accuracy of the pharmacokinetic parameters with limited observations in comparison to the conventional methods. Computation cost analyses indicate that the proposed framework can take advantage of parallel computing and provide solutions within practical response times, leading to fast and accurate parameter identification of pharmacokinetic problems.

Objective: To evaluate and determine the performance of a partially automated as well as a fully automated closed‐loop fluid resuscitation system during states of absolute and relative hypovolemia.Design: Prospective experimental trial.Setting: Research laboratory.Animals: Five adult Beagle dogs.Methods: Isoflurane anesthetized mechanically ventilated dogs were subjected to absolute hypovolemia (controlled: 2 trials; uncontrolled: 3 trials), relative hypovolemia (2 trials), and the combination of relative and absolute controlled hypovolemia (2 trials). Controlled and uncontrolled hypovolemia were produced by withdrawing blood from the carotid or femoral artery. Relative hypovolemia was produced by increasing the isoflurane concentration (1 trial) or by infusion of intravenous sodium nitroprusside (1 trial). Relative hypovolemia combined with controlled absolute hypovolemia was produced by increasing the isoflurane concentration (1 trial) and infusion of IV sodium nitroprusside (1 trial). Hemodynamic parameters including stroke volume variation (SVV) were continuously monitored and recorded in all dogs. A proprietary closed‐loop fluid administration system based on fluid distribution and compartmental dynamical systems administered a continuous infusion of lactated Ringers solution in order to restore and maintain SVV to a predetermined target value.Measurements and Main Results: A total of 9 experiments were performed on 5 dogs. Hemodynamic parameters deteriorated and SVV increased during controlled or uncontrolled hypovolemia, relative hypovolemia, and during relative hypovolemia combined with controlled hypovolemia. Stroke volume variation was restored to baseline values during closed‐loop fluid infusion.Conclusions: Closed‐loop fluid administration based on IV fluid distribution and compartmental dynamical systems can be used to provide goal directed fluid therapy during absolute or relative hypovolemia in mechanically ventilated isoflurane anesthetized dogs.