Informing pandemic management in Germany with trustworthy, living evidence syntheses and guideline development: Lessons learned from the COVID-19 evidence ecosystem (CEOsys).

OBJECTIVE: We present the 'COVID-19 evidence ecosystem' (CEOsys) as a German network to inform pandemic management and to support clinical and public health decision-making. We discuss challenges faced when organizing the ecosystem and derive lessons learned for similar networks acting during pandemics or health-related crises. STUDY DESIGN AND SETTING: Bringing together 18 university hospitals and additional institutions, CEOsys key activities included research prioritization, conducting living systematic reviews, supporting evidence-based (living) guidelines, knowledge translation, detecting research gaps and deriving recommendations, backed by technical infrastructure and capacity building. RESULTS: CEOsys rapidly produced 31 high-quality evidence syntheses and supported three living guidelines on COVID-19-related topics, while also developing methodological procedures. Challenges included CEOsys' late initiation in relation to the pandemic outbreak, the delayed prioritization of research questions, the continuously evolving COVID-19-related evidence, and establishing a technical infrastructure. Methodological-clinical tandems, the cooperation with national guideline groups and international collaborations were key for efficiency. CONCLUSION: CEOsys provided a proof-of-concept for a functioning evidence ecosystem at the national level. Lessons learned include that similar networks should, among others, involve methodological and clinical key stakeholders early on, aim for (inter-)national collaborations, and systematically evaluate their value. We particularly call for a sustainable network.

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.

Interoperable, Domain-Specific Extensions for the German Corona Consensus (GECCO) COVID-19 Research Data Set Using an Interdisciplinary, Consensus-Based Workflow: Data Set Development Study.

Background: The COVID-19 pandemic has spurred large-scale, interinstitutional research efforts. To enable these efforts, researchers must agree on data set definitions that not only cover all elements relevant to the respective medical specialty but also are syntactically and semantically interoperable. Therefore, the German Corona Consensus (GECCO) data set was developed as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As the GECCO data set is a compact core data set comprising data across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include data elements that are the most relevant to the research performed in those individual medical specialties. Objective: We aimed to (1) specify a workflow for the development of interoperable data set definitions that involves close collaboration between medical experts and information scientists and (2) apply the workflow to develop data set definitions that include data elements that are the most relevant to COVID-19-related patient research regarding immunization, pediatrics, and cardiology. Methods: We developed a workflow to create data set definitions that were (1) content-wise as relevant as possible to a specific field of study and (2) universally usable across computer systems, institutions, and countries (ie, interoperable). We then gathered medical experts from 3 specialties-infectious diseases (with a focus on immunization), pediatrics, and cardiology-to select data elements that were the most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications, using Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR). All steps were performed in close interdisciplinary collaboration with medical domain experts and medical information specialists. Profiles and vocabulary mappings were syntactically and semantically validated in a 2-stage process. Results: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains according to pandemic-related requests. The data elements included in each module were selected, according to the developed consensus-based workflow, by medical experts from these specialties to ensure that the contents aligned with their research needs. We defined data set specifications for 48 immunization, 150 pediatrics, and 52 cardiology data elements that complement the GECCO core data set. We created and published implementation guides, example implementations, and data set annotations for each extension module. Conclusions: The GECCO extension modules, which contain data elements that are the most relevant to COVID-19-related patient research on infectious diseases (with a focus on immunization), pediatrics, and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for developing further data set definitions. The GECCO extension modules provide standardized and harmonized definitions of specialty-related data sets that can help enable interinstitutional and cross-country COVID-19 research in these specialties.

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.

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.

Systematic Review and Meta-analysis: Outcomes After Descemet Membrane Endothelial Keratoplasty Versus Ultrathin Descemet Stripping Automated Endothelial Keratoplasty.

PURPOSE: To compare the efficacy and safety of ultrathin Descemet stripping (automated) endothelial keratoplasty (UT-DS(A)EK) versus Descemet membrane endothelial keratoplasty (DMEK) for the treatment of Fuchs endothelial dystrophy (FED) and bullous keratopathy (BK). DESIGN: Systematic review and meta-analysis. METHODS: Literature containing DMEK and UT-DSAEK were searched in the Cochrane Database of Systematic Reviews, PubMed, EMBASE, LILACS, and through manual reference searching. Studies were included that measured the outcome of interventions-including best corrected visual acuity (BCVA), endothelial cell density (ECD), and postoperative complications, especially graft detachment with the need of re-bubbling, graft rejection, graft failure, and postoperative elevated intraocular pressure (IOP)-in patients with FED and BK. Included outcomes were pooled as standardized mean differences (SMD) or risk ratios (RR) using random effects models. Inter-study heterogeneity was assessed using the Q-test and I2 statistic. RESULTS: Seven (of 163) studies met all the inclusion and exclusion criteria. Meta-analysis showed a significantly better BCVA 12 months postoperatively, but an increased re-bubbling rate in eyes after DMEK compared with eyes after UT-DS(A)EK (BCVA: SMD = 0.50 [95% CI 0.27-0.74] and re-bubbling rate: RR = 0.33 [95% CI 0.16-0.67]). All other parameters did not differ significantly between both interventions, although estimates were imprecise (graft failure: RR = 0.65 [95% CI 0.18-2.30], graft rejection: RR = 1.40 [95% CI 0.27-7.30], and postoperative intraocular pressure elevation: RR = 1.14 [95% CI 0.60-2.18]). Postoperative SMDs of ECD could not be evaluated due to significant heterogeneity between studies. CONCLUSIONS: Although the improvement in BCVA was higher after UT-DS(A)EK than after conventional DS(A)EK, the BCVA after DMEK was still superior. The complication rates were comparable for both procedures, except for the higher rate of re-bubbling after DMEK.

Postsynaptic plasticity of cholinergic synapses underlies the induction and expression of appetitive and familiarity memories in Drosophila.

In vertebrates, several forms of memory-relevant synaptic plasticity involve postsynaptic rearrangements of glutamate receptors. In contrast, previous work indicates that Drosophila and other invertebrates store memories using presynaptic plasticity of cholinergic synapses. Here, we provide evidence for postsynaptic plasticity at cholinergic output synapses from the Drosophila mushroom bodies (MBs). We find that the nicotinic acetylcholine receptor (nAChR) subunit α5 is required within specific MB output neurons for appetitive memory induction but is dispensable for aversive memories. In addition, nAChR α2 subunits mediate memory expression and likely function downstream of α5 and the postsynaptic scaffold protein discs large (Dlg). We show that postsynaptic plasticity traces can be induced independently of the presynapse, and that in vivo dynamics of α2 nAChR subunits are changed both in the context of associative and non-associative (familiarity) memory formation, underlying different plasticity rules. Therefore, regardless of neurotransmitter identity, key principles of postsynaptic plasticity support memory storage across phyla.

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.

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.

Cognitive Impairment Is Associated with Absolute Intraoperative Frontal α-Band Power but Not with Baseline α-Band Power: A Pilot Study.

BACKGROUND: Cognitive abilities decline with aging, leading to a higher risk for the development of postoperative delirium or postoperative neurocognitive disorders after general anesthesia. Since frontal α-band power is known to be highly correlated with cognitive function in general, we hypothesized that preoperative cognitive impairment is associated with lower baseline and intraoperative frontal α-band power in older adults. METHODS: Patients aged ≥65 years undergoing elective surgery were included in this prospective observational study. Cognitive function was assessed on the day before surgery using six age-sensitive cognitive tests. Scores on those tests were entered into a principal component analysis to calculate a composite "g score" of global cognitive ability. Patient groups were dichotomized into a lower cognitive group (LC) reaching the lower 1/3 of "g scores" and a normal cognitive group (NC) consisting of the upper 2/3 of "g scores." Continuous pre- and intraoperative frontal electroencephalograms (EEGs) were recorded. EEG spectra were analyzed at baseline, before start of anesthesia medication, and during a stable intraoperative period. Significant differences in band power between the NC and LC groups were computed by using a frequency domain (δ 0.5-3 Hz, θ 4-7 Hz, α 8-12 Hz, ß 13-30 Hz)-based bootstrapping algorithm. RESULTS: Of 38 included patients (mean age 72 years), 24 patients were in the NC group, and 14 patients had lower cognitive abilities (LC). Intraoperative α-band power was significantly reduced in the LC group compared to the NC group (NC -1.6 [-4.48/1.17] dB vs. LC -6.0 [-9.02/-2.64] dB), and intraoperative α-band power was positively correlated with "g score" (Spearman correlation: r = 0.381; p = 0.018). Baseline EEG power did not show any associations with "g." CONCLUSIONS: Preoperative cognitive impairment in older adults is associated with intraoperative absolute frontal α-band power, but not baseline α-band power.

Influence of midazolam premedication on intraoperative EEG signatures in elderly patients.

OBJECTIVE: To investigate the influence of midazolam premedication on the EEG-spectrum before and during general anesthesia in elderly patients. METHODS: Patients aged ≥65 years, undergoing elective surgery were included in this prospective observational study. A continuous pre- and intraoperative frontal EEG was recorded in patients who received premedication with midazolam (Mid, n = 15) and patients who did not (noMid, n = 30). Absolute power within the delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-25 Hz) frequency-bands was analyzed in EEG-sections before (pre-induction), and after induction of anesthesia with propofol (post-induction), as well as during general anesthesia with either propofol or volatile-anesthetics (intra-operative). RESULTS: Pre-induction, α-power of Mid patients was lower compared with noMid-patients (α-power: Mid: -10.75 dB vs. noMid: -9.20 dB; p = 0.036). After induction of anesthesia Mid-patients displayed a stronger increase of frontal α-power resulting in higher absolute α-power at post-induction state, (α-power: Mid -3.56 dB vs. noMid: -6.69 dB; p = 0.004), which remained higher intraoperatively (α-power: Mid: -2.12 dB vs. noMid: -6.10 dB; p = 0.024). CONCLUSION: Midazolam premedication alters the intraoperative EEG-spectrum in elderly patients. SIGNIFICANCE: This finding provides further evidence for the role of GABAergic activation in the induction of elevated, frontal α-power during general anesthesia. TRIAL REGISTRY NUMBER: NCT02265263. 23 September 2014. Principal investigator: Prof. Dr. med. Claudia Spies. (https://clinicaltrials.gov/ct2/show/NCT02265263).

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.

Development, validation and utility of a simulation model of the nociceptive flexion reflex threshold.

OBJECTIVE: A variety of algorithms is used for nociceptive flexion reflex threshold (NFRT) estimation, but their estimation accuracy is unknown. We developed a computer based simulation model of the NFRT to quantify and compare the accuracy of available estimation algorithms. METHODS: This simulation model is based on basic characteristics of the NFRT and specified by data collected from 60 healthy volunteers. We validated the model by comparing simulated data with data obtained independently in another volunteer population. The model was used to quantify the accuracy of previously published NFRT estimation algorithm for three NFRT variabilities representing sensory deprivation, distraction and general anaesthesia. RESULTS: The dynamic staircase algorithm obtained most accurate NFRT estimates during all NFRT variabilities. The number of stimuli applied can be chosen higher to increase estimate precision or lower to reduce measurement time. CONCLUSIONS: Our simulation model is a valid tool to measure the accuracy of NFRT estimation algorithms. It can be applied to analyse and develop algorithms. The dynamic staircase algorithm shows the highest precision in NFRT estimation and is recommended for NFRT studies. SIGNIFICANCE: Using optimized NFRT estimation algorithms increases precision in clinical and experimental NFRT studies and might therefore reduce the measurement effort necessary.