Untersuchung der Eignung nozizeptiver Reflexe zur Quantifizierung der endogenen Schmerzmodulation und zur Beurteilung der Analgesie unter Allgemeinanästhesie

Introduction: Nociceptive reflexes like the nociceptive flexion reflex (NFR) or the pupillary dilation reflex (PDR) are used to measure pain and nociception in wake patients and patients under sedation. However the accuracy of these methods regarding their different fields of application is unknown. We therefore conducted three studies: in study A we measured the test-retest reliability of the endogenous pain modulation of the NFR by a conditioning stimulus, in order to examine, if the NFR is able to measure the individual ability to inhibit pain. In study B we investigated, how accurate the NFR can be scored automatically in electromyography (EMG) recordings and optimized this automatic NFR scoring procedure. In study C we investigated, how accurate immediate postoperative pain and delayed tracheal extubation can be predicted by the nociceptive reflexes PDR and NFR measured during general anaesthesia. Method: In study A we measured the conditioned pain modulation (CPM) in 40 healthy participants at two sessions through the changes in size of the NFR and in subjective pain ratings caused by a painful hot water stimulus compared to the changes caused by a painless control stimulus. In study B we compared different EMG scoring criteria and their thresholds for automatic NFR detection with expert ratings in two independent data sets derived from 60 healthy participants each and calculated multivariable classification models to improve the automatic NFR detection. In study C we measured the PDR and NFR of 110 patients before and during general anaesthesia for primary hip arthroplasty. Results: Study A showed a good test-retest reliability for the CPM of the NFR and the CPM of pain ratings as measures of endogenous pain modulation. Study B showed that in automatic NFR detection by single scoring criteria the Interval-Peak-Z-Score matches the expert ratings with highest accuracy, while multivariable EMG scoring criteria further increase this accuracy significantly. Study C showed that the PDR and NFR thresholds measured during general anaesthesia correlate with delayed tracheal extubation and the PDR threshold correlate with the immediate postoperative pain. Conclusions: Our results from study A show that the NFR is a reliable measure of endogenous pain modulation. Our results from study B show that the NFR can be automatically detected with high accuracy, which can be further improved by multivariable EMG classification models. Our results from study C show that nociceptive reflexes measured during general anaesthesia reflect the balance between nociception and analgesia.Einleitung: Nozizeptive Reflexe wie der Nozizeptive Flexorenreflex (NFR) und der Pupillendilatationsreflex (PDR) werden zur Quantifizierung von Schmerz und Nozizeption bei wachen und sedierten Patienten verwendet. Jedoch gibt es bisher über die Genauigkeit dieser Methoden in den unterschiedlichen Einsatzgebieten nur wenige Daten. Wir führten daher drei Studien durch: In Studie A untersuchten wir die Test-Retest-Reliabilität der endogenen Schmerzmodulation des NFR durch einen konditionierenden Stimulus, um zu prüfen, ob sich der NFR als Maß der körpereigenen Schmerzhemmung eignet. In Studie B untersuchten wir, wie genau sich der NFR automatisiert im Elektromyogramm (EMG) erkennen lässt und optimierten diese automatisierte Reflexdetektion. In Studie C untersuchten wir, wie genau sich der akute postoperative Schmerz und die Aufwachdauer nach einer Operation anhand der noch während der Allgemeinanästhesie gemessenen nozizeptiven Reflexe NFR und PDR vorhersagen lassen. Methodik: In Studie A bestimmten wir das Ausmaß der Conditioned Pain Modulation (CPM) von 40 gesunden Probanden zu zwei Zeitpunkten anhand der Änderung von NFR und subjektiver Schmerzbewertung durch einen schmerzhaften Heißwasserstimulus im Vergleich zur Änderung durch einen schmerzlosen Kontrollstimulus. In Studie B verglichen wir in zwei Datensätzen von je 60 gesunden Probanden verschiedene Kriterien und deren Schwellenwerte zur automatisierten NFR-Detektion im EMG mit dem Goldstandard, der Bewertung durch Experten, und entwickelten zur Verbesserung der Reflexdetektion multivariable Modelle. In Studie C untersuchten wir den PDR und den NFR vor und während einer Allgemeinanästhesie an 110 Patienten zur primären Hüftarthroplastik sowie die Aufwachdauer und das Schmerzausmaß nach der Allgemeinanästhesie. Ergebnisse: Studie A zeigte eine gute Test-Retest-Reliabilität der CPM des NFR sowie der CPM der subjektiven Schmerzbewertung. Studie B zeigte für die automatisierte NFR-Detektion durch den EMG-Parameter Interval-Peak-Z-Score eine hohe Übereinstimmung mit der Bewertung durch Experten, die durch Einbezug weiterer EMG-Parameter mittels multivariabler Modelle signifikant verbessert werden konnte. Studie C zeigte, dass die unter Allgemeinanästhesie bestimmten Schwellenwerte des PDR und des NFR mit der Aufwachdauer korrelieren und dass der Schwellenwert des PDR auch mit dem akuten postoperativen Schmerz korreliert. Schlussfolgerung: Die Ergebnisse aus Studie A zeigen, dass der NFR geeignet ist, die endogene Schmerzmodulation reliabel abzubilden. Die Ergebnisse aus Studie B zeigen, dass der NFR mit einer hohen Genauigkeit automatisiert im EMG erkannt werden kann und multivariable Modelle diese Reflexdetektion noch weiter verbessern können. Die Ergebnisse aus Studie C zeigen, dass die nozizeptiven Reflexe auch unter Allgemeinanästhesie in der Lage sind, die Balance zwischen Nozizeption und Analgesie widerzuspiegeln

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

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

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