The variability in nociceptive flexion reflex threshold measurement is mostly caused by probabilistic effects of the estimation algorithms: a simulation study.

OBJECTIVES: The nociceptive flexion reflex (NFR) and its threshold are frequently used to investigate spinal nociception in humans. Since this threshold (NFRT) is a probabilistic measure, specific algorithms are used for NFRT estimation based on the stochastic occurrence of reflexes at different stimulus intensities. We used a validated simulation model of the NFR to investigate the amount of NFRT measurement variability induced by different estimation algorithms in a steady setting of reduced external influences. METHODS: We simulated the behavior of different estimation algorithms in subjects with an artificially steady baseline NFRT variability (standard deviation: 0 mA) or low baseline NFRT variability (standard deviation: 0.156 mA), equaling a quiet experimental setting. The obtained data were analyzed for NFRT measurement variability caused by the algorithms compared to the baseline variability reflecting other physiological influences. RESULTS: The standard deviation of the NFRT estimated by the different algorithms ranged between 0.381 and 3.464 mA with 96.8% to 99.6% of the measurement variability attributed to the algorithm used. Out of the investigated algorithms the dynamic staircase algorithm was most precise. CONCLUSION: The NFRT measurement variability observed during quiet and steady experimental sessions is mostly caused by the properties of the estimation algorithms, due to the probabilistic nature of the reflex occurrence. Our results give reference for choosing the optimal estimation algorithm to improve measurement precision.

[Functional requirements of patient data management systems in intensive care medicine]. / Funktionelle Anforderungen an Patientendatenmanagementsysteme in der Intensivmedizin.

BACKGROUND: As part of the German government's digitization initiative, the paper-based documentation that is still present in many intensive care units is to be replaced by digital patient data management systems (PDMS). In order to simplify the implementation of such systems, standards for basic functionalities that should be part of basic configurations of PDMS would be of great value. PURPOSE: This paper describes functional requirements for PDMS in several categories. METHODS: Criteria for standardized data documentation were defined by the authors and derived functional requirements were classified into two priority categories. RESULTS: Overall, general technical requirements, functionalities for intensive care patient care, and additional functionalities for PDMS were defined and prioritized. DISCUSSION: Using this paper as a starting point for a discussion about basic functionalities of PDMS, it is planned to develop and obtain consensus on definitive standards with representatives from medical societies, medical informatics and PDMS manufacture.

[Digital guidelines of the twenty-first century : Requirements for the structured development, application and evaluation of medical knowledge]. / Digitale Leitlinien des 21. Jahrhunderts : Anforderungen an die strukturierte Entwicklung, Anwendung und Evaluation medizinischen Wissens.

Until now, clinical guidelines have been understood as generalized representations of clinical knowledge that, based on best available evidence, show the requirements for patient care in specific patient situations. This expert opinion article is intended to discuss how digital guidelines should be designed and which requirements must be met for the structured development, application and evaluation of such guidelines. The digitalization of guidelines must take into account the transformation of analogue text-based guideline information into formats that enable human-machine interaction via user interfaces, that show physicians the requirements for guideline-compliant patient care and that also enable machine storage, machine execution and machine processing of patient data.

Automated Monitoring of Adherence to Evidenced-Based Clinical Guideline Recommendations: Design and Implementation Study.

BACKGROUND: Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients. OBJECTIVE: This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype's utility in treatment recommendations. METHODS: We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital. RESULTS: We designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources-based format for the representation of clinical practice guidelines to provide an interoperable, standards-based guideline recommendation exchange format. CONCLUSIONS: Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the system.

Representation of evidence-based clinical practice guideline recommendations on FHIR.

BACKGROUND: Various formalisms have been developed to represent clinical practice guideline recommendations in a computer-interpretable way. However, none of the existing formalisms leverage the structured and computable information that emerge from the evidence-based guideline development process. Thus, we here propose a FHIR-based format that uses computer-interpretable representations of the knowledge artifacts that emerge during the process of evidence-based guideline development to directly serve as the basis of evidence-based recommendations. METHODS: We identified the information required to represent evidence-based clinical practice guideline recommendations and reviewed the knowledge artifacts emerging during the evidence-based guideline development process. We then conducted a consensus-based design process with domain experts to develop an information model for guideline recommendation representation that is structurally aligned to the evidence-based guideline recommendation development process and a corresponding representation based on FHIR resources developed for evidence-based medicine (EBMonFHIR). The resulting recommendations were modelled and represented in conformance with the FHIR Clinical Guidelines (CPG-on-FHIR) implementation guide. RESULTS: The information model of evidence-based clinical guideline recommendations and its EBMonFHIR-/CPG-on-FHIR-based representation contain the clinical contents of individual guideline recommendations, a set of metadata for the recommendations, the ratings for the recommendations (e.g., strength of recommendation, certainty of overall evidence), the ratings of certainty of evidence for individual outcomes (e.g., risk of bias) and links to the underlying evidence (systematic reviews based on primary studies). We created profiles and an implementation guide for all FHIR resources required to represent the knowledge artifacts generated during evidence-based guideline development and their re-use as the basis for recommendations and used the profiles to implement an exemplary clinical guideline recommendation. CONCLUSIONS: The FHIR implementation guide presented here can be used to directly link the evidence assessment process of evidence-based guideline recommendation development, i.e. systematic reviews and evidence grading, and the underlying evidence from primary studies to the resulting guideline recommendations. This not only allows the evidence on which recommendations are based on to be evaluated transparently and critically, but also enables guideline developers to leverage computable evidence in a more direct way to facilitate the generation of computer-interpretable guideline recommendations.

Performance of the SEDLine Monitor: Age Dependency and Time Delay.

BACKGROUND: Devices monitoring the hypnotic component of general anesthesia can help to guide anesthetic management. The main purposes of these devices are the titration of anesthesia dose. While anesthesia at low doses can result in awareness with intraoperative memory formation, excessive administration of anesthetics may be associated with an increased risk of postoperative neurocognitive disorder. We have previously shown for various indices that they are significantly influenced by the patient's age and that the monitors have a significant time delay. Here, we evaluated the influence of patient's age and time delay on the patient state index (PSI) of the SEDLine monitor. METHODS: To analyze the influence of the patient's age, we replayed 2 minutes of electroencephalography (EEG) of 141 patients (19-88 years, ASA I-IV) undergoing general anesthesia maintained with desflurane, sevoflurane, or propofol to the SEDLine monitor. We extracted the PSI as well as the spectral edge frequency (SEF) and performed a linear regression analysis. For evaluation of the time delay, we replayed 5 minutes of EEG of stable episodes of adequate anesthesia (PSI between 25 and 50) or light sedation/wake (PSI >70) in different orders to the SEDLine to simulate sudden changes between the states. Time delays were defined as the required time span of the monitor to reach the stable target index. RESULTS: PSI and SEF increased significantly with the patient's age. These findings did not depend on the administered anesthetic. The evaluation of the correlation between SEF and PSI showed a strong correlation with Spearman's correlation coefficient of ρ = 0.86 (0.82; 0.89). The time delays depended on the type of transition. We found a median time delay of 54 (Min: 46; Max: 61) seconds for the important transition between adequate anesthesia and awake and 55 (Min: 50; Max: 67) seconds in the other direction. CONCLUSIONS: With our analyses, we show that the indices presented on the SEDLine display, the PSI and the SEF, increase with age for patients under general anesthesia. Additionally, a delay of the PSI to react to sudden neurophysiologic changes due to dose of the maintenance anesthetic is of a time course that is clinically significant. These factors should be considered when navigating anesthesia relying on only the proprietary index for the SEDLine monitor.

Electroencephalogram-based prediction and detection of responsiveness to noxious stimulation in critical care patients: a retrospective single-centre analysis.

BACKGROUND: Monitoring of pain and nociception in critical care patients unable to self-report pain remains a challenge, as clinical signs are neither sensitive nor specific. Available technical approaches are limited by various constraints. We investigated the electroencephalogram (EEG) for correlates that precede or coincide with behavioural nociceptive responses to noxious stimulation. METHODS: In this retrospective study, we analysed frontal EEG recordings of 64 critical care patients who were tracheally intubated and ventilated before, during, and after tracheal suctioning. We investigated EEG power bands for correlates preceding or coinciding with behavioural responses (Behavioural Pain Scale ≥7). We applied the Mann-Whitney U-test to calculate corresponding P-values. RESULTS: Strong behavioural responses were preceded by higher normalised power in the 2.5-5 Hz band (+17.1%; P<0.001) and lower normalised power in the 0.1-1.5 Hz band (-10.5%; P=0.029). After the intervention, strong behavioural responses were associated with higher normalised EEG power in the 2.5-5 Hz band (+16.6%; P=0.021) and lower normalised power in the 8-12 Hz band (-51.2%; P=0.037) CONCLUSIONS: We observed correlates in EEG band power that precede and coincide with behavioural responses to noxious stimulation. Based on previous findings, some of the power bands could be linked to processing of nociception, arousal, or sedation effects. The power bands more closely related to nociception and arousal could be used to improve monitoring of nociception and to optimise analgesic management in critical care patients. CLINICAL TRIAL REGISTRATION: DRKS00011206.

Behaviour-based pain scales: Validity and interrater reliability of BPS-NI and PAINAD-G on general wards.

BACKGROUND: Measuring pain intensity in patients unable to communicate is a challenge on general wards. Observation-based pain scoring tools have been used for patients with dementia or in critically ill intensive care unit (ICU) patients. However, there is no established or validated assessment tool for non-ICU patients without dementia related cognitive deficiencies who cannot communicate. The "Behavioural Pain Scale Non-Intubated" (BPS-NI) and the "Pain Assessment In Advanced Dementia-German" (PAINAD-G) are potential tools to fill this gap. METHODS: This study was performed with verbal non-ICU patients on general wards at Charité Berlin. Two assessors independently rated pain intensity using the BPS-NI and the PAINAD-G along with patients' self-ratings on the Numeric Rating Scale (NRS). The interrater-reliability of BPS-NI and PAINAD-G was calculated and ROC-analyses were performed to identify cut-off values for medium and intense pain for each score. Effectiveness was calculated using percentage agreement. In total, 126 patients were included into analysis. RESULTS: The BPS-NI showed substantial congruence in interrater-reliability (Cohens-Kappa 0.71), whereas the PAINAD-G showed moderate congruence (Kappa 0.48). Based on ROC-analyses, for medium pain levels a cut-off 4 (BPS-NI) or 2 (PAINAD-G) and for severe pain levels cut-off 5 (BPS-NI) and 3 (PAINAD-G) would result in good accordance with self-reported NRS for pain. CONCLUSION: The BPS-NI shows a good validity in measuring pain intensity in patients on general wards and may possibly be used for patients unable to communicate. Using defined cut-off values for BPS-NI and PAINAD-G, clinically relevant pain intensities in patients can reliably be detected. SIGNIFICANCE: Measuring the pain intensity in patients unable to communicate is a common challenge on general wards. This study assessed reliability and applicability BPS-NI and the PAINAD-G in a general ward setting. Furthermore, it provides cut-off values in order to estimate pain intensity and support analgesic response.

Investigation of Behavioural Pain Scale, Critical Care Pain Observation Tool, nociceptive flexion reflex and pupillary dilatation reflex as predictors of behavioural reactions to nociceptive procedures in critically ill patients unable to self-report pain.

BACKGROUND: Procedural pain is a common burden in critical care treatment and the prediction of nociceptive reactions remains challenging. Thus, we investigated the Behavioural Pain Scale (BPS), the Critical Pain Observational Tool (CPOT), the nociceptive flexion reflex (NFR), the pupillary dilation reflex (PDR) and the Richmond Agitation-Sedation Scale (RASS) as predictors of behavioural reactions to nociceptive procedures. METHODS: In this monocentric, prospective, observational study, we analysed data of 128 critically ill adults unable to self-report pain to investigate the predictability of behavioural reactions to two procedures: endotracheal suctioning and turning. Next to routine clinical data, CPOT, BPS, PDR, NFR, RASS, propofol and sufentanil doses were recorded before the procedures. RESULTS: For endotracheal suctioning, NFR, BPS, CPOT and RASS showed predictive performances significantly better than chance, but none of them performed significantly better than the sufentanil dose rate. For turning, BPS, CPOT and RASS showed predictive performances significantly better than chance, but only the RASS performed significantly better than the propofol dose rate. CONCLUSIONS: Behavioural reactions to both investigated clinical procedures can be predicted by observational scales or nociceptive reflexes. For endotracheal suctioning, none of the predictors performed superior to using the sufentanil dose rate as a predictor. As using sufentanil as a predictor requires no extra effort in contrast to the other predictors, none of the here investigated tools seem advisable for predicting behavioural reactions to endotracheal suctioning. For patient turning, the RASS predicts reactions better than any other tool. SIGNIFICANCE: In this observational study, we demonstrate that behavioural reactions to potentially nociceptive procedures in critical care treatment can be predicted by observational scales and nociceptive reflexes. However, for endotracheal suctioning, none of the predictors is superior to using the opioid dose rate as a predictor. For patient turning, the RASS predicts reactions better than any other parameters.

Comparison of volume-controlled, pressure-controlled, and chest compression-induced ventilation during cardiopulmonary resuscitation with an automated mechanical chest compression device: A randomized clinical pilot study.

AIM OF THE STUDY: Automated mechanical chest compression devices (AMCCDs) can help performing high-quality cardiopulmonary resuscitation (CPR). Guidelines for CPR are lacking information about the optimal ventilation mode during CPR using AMCCDs. Aim of this pilot study was to compare three common ventilation modes during CPR using AMCCD. METHODS: In this randomized controlled trial, we included patients with an out-of-hospital cardiac arrest arriving at the resuscitation room receiving chest compressions via AMCCD with an expected continuation of at least 15 min. Patients were randomly assigned to three groups: biphasic positive airway pressure with assisted spontaneous ventilation (BIPAP) with assisted spontaneous breathing, continuous positive airway pressure (CPAP) and volume-controlled ventilation (VCV). Outcomes were tidal volume, respiratory minute volume, and end-tidal CO2 during the study period. Groups were compared using generalized linear models. Data is given as median and interquartile ranges. RESULTS: Of 53 screened patients, 30 were randomized. The tidal volume was significantly (p < 0.05) lower in patients of the CPAP group (68 [64-83] ml) compared with those of the BIPAP (349 [137-500] ml), while the respiratory minute volume differed between the CPAP group (6.2 [5.3-8.1] l/min) and both the BIPAP (7.1 [6.7-10.2] l/min) and VCV group (7.2 [3.7-8.4] l/min). CONCLUSIONS: All ventilation modes achieved an adequate respiratory minute volume during CPR with an AMCCD. However, BIPAP seems to be superior due to the higher tidal volume. Therefore, we recommend starting mechanical ventilation when using AMCCD with BIPAP ventilation to avoid risks related to dead space ventilation.

Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia.

In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86-0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61-0.65]), SAPS2 (0.72 [95% CI 0.71-0.74]) and SOFA (0.76 [95% CI 0.75-0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73-0.78]) and Wang (laboratory: 0.62 [95% CI 0.59-0.65]; clinical: 0.56 [95% CI 0.55-0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70-0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.

Resting state brain network functional connectivity is not associated with inflammatory markers and blood cell counts in older adults.

OBJECTIVE: Systemic inflammation and monocyte counts have previously been associated with changes in resting state functional connectivity (rsFC) in cross-sectional neuroimaging studies. We therefore investigated this association in a longitudinal study of older patients. METHODS: We performed a secondary analysis of longitudinal data from older patients who underwent functional magnet resonance imaging (fMRI) scans before and 3 months after elective surgery. Additionally, serum levels of C-reactive protein and Interleukin-6 as markers of inflammation and leukocyte, lymphocyte and monocyte counts were determined. Correlations between these markers and pre- or postoperative rsFC between regions previously associated with inflammatory markers were investigated using general linear regression models. RESULTS: We found no significant correlations between inflammatory markers or blood cell counts and mean connectivity within four resting state networks (RSNs), neither preoperatively nor postoperatively. Significant inter-region rsFC was found within these RSNs between a few regions either pre- or postoperatively, but no inter-region connections were consistently observed in both pre- and postoperative fMRI scans. CONCLUSIONS: Inflammatory markers and monocyte counts were not associated with rsFC in our study, contrasting previous results. SIGNIFICANCE: Multiple measurements in the same individuals, as performed here, provide a way to reduce the high risk of false positive results in fMRI studies. TRIAL REGISTRATION: Clinicaltrials.gov (registration number NCT02265263).

Perioperative predictability of unsatisfactory functional outcomes 6 months after hip arthroplasty.

BACKGROUND: Aiming at an early identification of patients with an unsatisfactory outcome after hip arthroplasty we investigated the pre-, intra- and early postoperative predictability. METHODS: Using logistic regression models at nine different time points we calculated the area under the curve of the receiver operating characteristic (ROC-AUC) to compare the predictability of an unsatisfactory outcome. RESULTS: The predictability of unsatisfactory outcomes increased significantly from a ROC-AUC (95% CI) of 0.73 (0.62-0.84) in the preoperative setting to 0.85 (0.77-0.94) on day 2 after surgery. CONCLUSION: Most of the patients with unsatisfactory outcomes can be identified during the first days after surgery.

Technical considerations when using the EEG export of the SEDLine Root device.

Electroencephalographic (EEG) patient monitoring during general anesthesia can help to assess the real-time neurophysiology of unconscious states. Some monitoring systems like the SEDLine Root allow export of the EEG to be used for retrospective analysis. We show that changes made to the SEDLine display during recording affected the recorded EEG. These changes can strongly impact retrospective analysis of EEG signals. Real-time changes of the feed speed in the SEDLine Root device display modifies the sampling rate of the exported EEG. We used a patient as well as a simulated EEG recording to highlight the effects of the display settings on the extracted EEG. Therefore, we changed EEG feed and amplitude resolution on the display in a systematic manner. To visualize the effects of these changes, we present raw EEG segments and the density spectral array of the recording. Changing the display's amplitude resolution affects the amplitudes. If the amplitude resolution is too fine, the exported EEG contains clipped amplitudes. If the resolution is too coarse, the EEG resolution becomes too low leading to a low-quality signal making frequency analysis impossible. The proportion of clipped or zero-line data caused by the amplitude setting was > 60% in our sedated patient. Changing the display settings results in undocumented changes in EEG amplitude, sampling rate, and signal quality. The occult nature of these changes could make the analysis of data sets difficult if not invalid. We strongly suggest researchers adequately define and keep the EEG display settings to export good quality EEG and to ensure comparability among patients.

Investigation of the Usability of Computerized Critical Care Information Systems in Germany.

INTRODUCTION: The term "usability" describes how effectively, efficiently, and with what level of user satisfaction an information system can be used to accomplish specific goals. Computerized critical care information systems (CCISs) with high usability increase quality of care and staff satisfaction, while reducing medication errors. Conversely, systems lacking usability can interrupt clinical workflow, facilitate errors, and increase charting time. The aim of this study was to investigate and compare usability across CCIS currently used in Germany. METHODS: In this study, German intensive care unit (ICU) nurses and physicians completed a specialized, previously validated, web-based questionnaire. The questionnaire assessed CCIS usability based on three rating models: an overall rating of the systems, a model rating technical usability, and a model rating task-specific usability. RESULTS: We analyzed results from 535 survey participants and compared eight different CCIS commonly used in Germany. Our results showed that usability strongly differs across the compared systems. The system ICUData had the best overall rating and technical usability, followed by the platforms ICM and MetaVision. The same three systems performed best in the rating of task-specific usability without significant differences between each other. Across all systems, overall ratings were more dependent on ease-of-use aspects than on aspects of utility/functionality, and the general scope of the functions offered was rated better than how well the functions are realized. DISCUSSION: Our results suggest that manufacturers should shift some of their effort away from the development of new features and focus more on improving the ease-of-use and quality of existing features.

Functional requirements of critical care information systems (CCIS) from the users' perspective.

INTRODUCTION: Critical care information systems (CCIS) are computer software systems specialized for supporting the data processing tasks of clinical staff in intensive care units (ICUs). Reports on the impact of CCIS vary strongly from large benefits to harmful impact. One factor causing these inconsistent results is a large functional heterogeneity of the currently available systems, as no standards exist. Therefore, the aim of this study was to derive recommendations for a minimum range of functions that CCIS should incorporate from the perspective of clinical users. MATERIALS AND METHODS: We performed a web-based survey targeting clinical ICU staff in Germany. Participants rated the usefulness of pre-defined CCIS functions (36 for physicians / 31 for nurses) between 0-5. To capture confounders that might influence the ratings, we recorded 18 individual and workplace characteristics. RESULTS: The 912 participants rated all but three of the investigated functions as useful with median scores of 4-5. A multivariable analysis showed that the ratings were influenced by whether CCIS were used on the participants' wards, by the rating of the function quality of the used CCIS, the years of work experience and whether the participant was a physician or a nurse. CONCLUSIONS: Clinical ICU staff considers all but three of the here investigated CCIS functions as useful. Even though we identified a variety of factors influencing the usefulness ratings, their influence is rather small and thus does not limit the validity of our findings. Accordingly, we commend the functions identified as useful to be incorporated in CCIS.

Acute antiepileptic drug use in intensive care units.

BACKGROUND: In intensive care units (ICUs), antiepileptic drugs (AEDs) are used for manifold indications. This is the first study to assess the prevalence of acute AED use in ICUs and to identify associated clinical variables. METHODS: All patients in seven adult ICUs of a German university hospital in 2016 were retrospectively evaluated. Data were extracted from the computerized critical care information system and manually reviewed. Acute AED treatments were defined as initiated during ICU treatment or ≤ 6 h before ICU admission, excluding benzodiazepines and sedatives. RESULTS: Among 2335 patients evaluated, 8.8% received acutely started AEDs: 5.1% due to epileptic seizures, mostly acute symptomatic, and 3.7% for other indications like pain, post-hypoxic myoclonus, and singultus. Following multivariable analyses, acute AED use was independently associated with intracranial reasons for ICU admission and long durations of ICU stay, but not with increased disease severity scores or mortality. Levetiracetam was the substance most frequently used to treat epileptic seizures (88%) as was pregabalin for other conditions (49%). Among surviving patients, acute AEDs were continued beyond ICU discharge in 86% if seizure-related and in 78% if not seizure-related, even if there was no evident need for long-term AED treatment. CONCLUSIONS: One out of eleven ICU patients receives acute AEDs, in almost half of cases for non-seizure indications. Acute AED use is a marker for intracranial ICU indications and prolonged ICU treatments. Usually, newer-generation AEDs are employed with favourable pharmacokinetic and safety profiles. However, whenever possible, acutely started AED should be discontinued before discharge from ICU.

Effects of propofol anesthesia on the processing of noxious stimuli in the spinal cord and the brain.

Drug-induced unconsciousness is an essential component of general anesthesia, commonly attributed to attenuation of higher-order processing of external stimuli and a resulting loss of information integration capabilities of the brain. In this study, we investigated how the hypnotic drug propofol at doses comparable to those in clinical practice influences the processing of somatosensory stimuli in the spinal cord and in primary and higher-order cortices. Using nociceptive reflexes, somatosensory evoked potentials and functional magnet resonance imaging (fMRI), we found that propofol abolishes the processing of innocuous and moderate noxious stimuli at low to medium concentration levels, but that intense noxious stimuli evoked spinal and cerebral responses even during deep propofol anesthesia that caused profound electroencephalogram (EEG) burst suppression. While nociceptive reflexes and somatosensory potentials were affected only in a minor way by further increasing doses of propofol after the loss of consciousness, fMRI showed that increasing propofol concentration abolished processing of intense noxious stimuli in the insula and secondary somatosensory cortex and vastly increased processing in the frontal cortex. As the fMRI functional connectivity showed congruent changes with increasing doses of propofol - namely the temporal brain areas decreasing their connectivity with the bilateral pre-/postcentral gyri and the supplementary motor area, while connectivity of the latter with frontal areas is increased - we conclude that the changes in processing of noxious stimuli during propofol anesthesia might be related to changes in functional connectivity.