Measures, Mechanisms and Effects of Spinal and Cerebral Nociceptive Processing during General Anaesthesia

Next to unconsciousness, the suppression of nociception – i. e. the neuronal processing of noxious stimuli – is a central component of general anaesthesia. While unconsciousness can be monitored fairly accurately using electroencephalography (EEG)-derived measures, there is no reliable measure that allows quantifying the level of nociception in unconscious humans available to this day. Therefore, this dissertation aimed at developing a multimodal measure of nociceptive processing in humans and applying this measure to investigate the spinal and cerebral processing of innocuous and noxious somatosensory stimuli during general anaesthesia. Using a setup that combined functional magnetic resonance imaging (fMRI) with simultaneous EEG and spinal nociceptive reflex monitoring, we were able for the first time to (i) concurrently investigate spinal and cerebral effects of general anaesthetics on the processing of somatosensory stimuli and to (ii) investigate intense noxious stimuli at intensities comparable to surgical stimuli. During unconsciousness, we found an anaesthetic dose-dependent change of nociceptive processing in a variety of brain regions including higher-order association cortices. The changes in processing were accompanied by changes in functional connectivity between nociceptive brain regions, in accordance with the notion that general anaesthetics induce unconsciousness by altering the information transfer patterns in the brain. We found that profound spinal and cerebral nociceptive-evoked activation persisted even at levels of general anaesthesia that are deeper than applied in clinical practice. Currently used clinical indicators of analgesic efficacy (e. g. haemodynamic responses to noxious stimuli) were absent at far lower levels of general anaesthesia, demonstrating that the absence of these clinical responses is not indicative of absent nociceptive processing. Due to the unavailability of reliable measures of intraoperative nociception, it is not known whether persisting nociception during general anaesthesia contributes to adverse effects on patient outcomes such as pain chronification. We therefore supplemented the primary experimental research of this dissertation by a clinical study, in which we showed that the level of intraoperative analgesia was related to persistent postoperative pain. As the analgesic dosings were in the range in which we found profound persistent nociceptive processing in our experimental studies, these results suggest that persistent nociception during currently used levels of intraoperative analgesia indeed contributes to long-term harm on patient outcomes.Neben der Bewusstlosigkeit ist die Unterdrückung von Nozizeption – also der neuronalen Verarbeitung von potenziell gewebeschädigenden Reizen – eine zentrale Komponente der Allgemeinanästhesie. Während Bewusstlosigkeit relativ genau mittels Elektroenzephalographie (EEG) überwacht werden kann, existiert bis heute kein zuverlässiges Verfahren, um das Nozizeptionsniveau in bewusstlosen Menschen zu quantifizieren. Ziel dieser Dissertation war es daher ein multimodales Maß der nozizeptiven Verarbeitung im Menschen zu entwickeln und dieses Maß zu verwenden, um die spinale und zerebrale Verarbeitung von nozizeptiven Reizen unter Allgemeinanästhesie zu untersuchen. Durch Kombination von funktioneller Magnetresonanztomographie (fMRT) mit simultaner EEG und spinalen nozizeptiven Reflexen waren wir erstmalig in der Lage (i) gleichzeitig spinale und zerebrale Effekte von Allgemeinanästhetika auf die Verarbeitung somatosensorischer Reize zu untersuchen und (ii) sehr starke nozizeptive Reize, deren Intensität vergleichbar mit der von chirurgischen Reizen ist, zu verwenden. Unter Bewusstlosigkeit konnten wir eine dosisabhängige Veränderung der nozizeptiven Verarbeitung in einer Reihe von Hirnarealen, darunter Assoziationsareale, mit einhergehender Modulation der funktionellen Konnektivität zwischen nozizeptions-assoziierten Hirnarealen finden. Dies bestärkt die Vermutung, dass Allgemeinanästhetika Bewusstlosigkeit durch Veränderung der Informationsverbeitungspfade des Gehirns erzeugen. Unter allen untersuchten Narkosetiefen bis hin zu tieferer Narkose als in der derzeitigen klinischen Praxis verwendet konnten wir umfassende spinale und zerebrale nozizeptive Aktivierungen nachweisen. Klinisch verwendete Indikatoren überschießender Nozizeption (bspw. hämodynamische Reaktionen auf nozizeptive Reize) waren bereits bei wesentlich geringeren Narkosetiefen nicht mehr nachweisbar. Das Ausbleiben dieser klinischen Reaktionen bedeutet daher nicht ein Ausbleiben von nozizeptiver Verarbeitung. Aufgrund des Fehlens von zuverlässigen Maßen intraoperativer Nozizeption ist bisher nicht bekannt, ob bestehende Nozizeption unter Allgemeinanästhesie zu klinisch relevanten Auswirkungen wie bspw. Schmerzchronifizierung beiträgt. In einer klinischen Patientenstudie konnten wir zeigen, dass das Niveau der intraoperativen Analgesie mit dem Auftreten von chronischen postoperativen Schmerzen assoziiert ist. Da die intraoperative Analgesie der Patienten in dem Bereich war, in dem wir noch umfassende nozizeptive Verarbeitung in den experimentellen Studien fanden, deuten diese Resultate darauf hin, dass persistierende Nozizeption bei heute gebräuchlicher intraoperativer Analgesie tatsächlich zu langfristigen Schäden von Patienten beitragen kann

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

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. Keywords: Physiology (medical); Sensory Systems; Neurology; Clinical Neurology; Premedication; Benzodiazepines – midazolam; EEG; Geriatric anesthesia; Propofol anesthesiaSeventh Framework Programme (European Commission) (Grant HEALTH-F2-2014-60246

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 alpha-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 alpha-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 (delta 0.5-3 Hz, theta 4-7 Hz, alpha 8-12 Hz, beta 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 alpha-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 alpha-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 alpha-band power, but not baseline alpha-band power

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

BackgroundClinical 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. ObjectiveThis 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. MethodsWe 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. ResultsWe 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. ConclusionsOur 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

Development of interoperable, domain-specific extensions for the German Corona Consensus (GECCO) COVID-19 research dataset using an interdisciplinary, consensus-based workflow

Background The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, researchers must agree on dataset definitions that not only cover all elements relevant to the respective medical specialty but that are also syntactically and semantically interoperable. Following such an effort, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties. Objective To (i) specify a workflow for the development of interoperable dataset definitions that involves a close collaboration between medical experts and information scientists and to (ii) apply the workflow to develop dataset definitions that include data elements most relevant to COVID-19-related patient research in immunization, pediatrics, and cardiology. Methods We developed a workflow to create dataset definitions that are (i) content-wise as relevant as possible to a specific field of study and (ii) universally usable across computer systems, institutions, and countries, i.e., interoperable. We then gathered medical experts from three specialties (immunization, pediatrics, and cardiology) to the select data elements 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 HL7 FHIR. All steps were performed in close interdisciplinary collaboration between medical domain experts and medical information scientists. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. Results We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected according to the here developed consensus-based workflow by medical experts from the respective specialty to ensure that the contents are aligned with the respective research needs. We defined dataset specifications for a total number of 48 (immunization), 150 (pediatrics), and 52 (cardiology) data elements that complement the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module. Conclusions These here presented GECCO extension modules, which contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for the development of further dataset definitions. The GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties

Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated (|r^g| ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance