[Potential effect of the stimulus threshold level of the nociceptive flexion reflex (NFRT) on mortality and delirium incidence in the critically ill patient: a retrospective cohort analysis]. / Potenzieller Einfluss der Reizschwellenhöhe des nozizeptiven Flexorenreflex (NFRT) auf die Mortalitäts- und Delirinzidenz beim kritisch kranken Patienten: eine retrospektive Kohortenanalyse.

BACKGROUND: Mortality and delirium in critically ill patients are affected by the provided analgesics and sedatives. The deeper the sedation and the higher the dose of analgesics applied, the more difficult it is to assess pain and the depth of sedation. Therefore, instrumental measurement methods, such as the measurement of the stimulus threshold of the nociceptive flexion reflex (NFRT), are becoming increasingly more important. OBJECTIVE: The aim of the present study is to investigate a potential association between the level of the nociceptive flexion reflex, mortality, and the occurrence of delirium. MATERIAL AND METHODS: By retrospectively analyzing a pilot data set of 57 ICU patients from the interdisciplinary surgical ICU of Ulm University Hospital surveyed between 11/2018 and 03/2020, a possible association between the NFRT, mortality, and the occurrence of delirium was calculated in an adjusted logistic regression model. Depending on the cut-off value, the stimulus threshold corridors result in the following comparison pairs: < 20 mA vs. 20-40 mA/20-50 mA/20-60 mA, > 40 mA vs. 20-40 mA, > 50 mA vs. 20-50 mA and > 60 mA vs. 20-60 mA. Results are presented as odds ratios (OR) adjusted for age, sex, height, TISS-28, SAPS II, RASS, BPS, and applied analgesics. Pain assessment was performed, in addition to the Behavioral Pain scale, ≥ 3 times daily by measuring NFRT. RESULTS: A statistically nonsignificant tendency for an increase in mortality incidence occurred with an NFRT > 50 mA, versus a stimulus threshold corridor of 20-50 mA (OR 3.3, CI: 0.89-12.43, p = 0.07). A trend toward a reduction in delirium incidence occurred at an NFRT < 20 mA, versus a stimulus threshold corridor of 20-40 mA (OR 0.40, CI: 0.18-0.92, p = 0.03). CONCLUSION: Based on the level of the NFRT, no recommendation can be made at this point to adjust the analgesic regimen of critically ill patients, who are unable to communicate. The observation of a tendency towards an increase in mortality at high stimulus thresholds or a reduction in the occurrence of delirium at low stimulus thresholds of the NFRT must be verified in standardized studies.

[From external assessment of pain to automated multimodal measurement of pain intensity : Narrative review of state of research and clinical perspectives]. / Von der Fremdbeurteilung des Schmerzes zur automatisierten multimodalen Messung der Schmerzintensität : Narrativer Review zum Stand der Forschung und zur klinischen Perspektive.

BACKGROUND: In patients with limited communication skills, the use of conventional scales or external assessment is only possible to a limited extent or not at all. Multimodal pain recognition based on artificial intelligence (AI) algorithms could be a solution. OBJECTIVE: Overview of the methods of automated multimodal pain measurement and their recognition rates that were calculated with AI algorithms. METHODS: In April 2018, 101 studies on automated pain recognition were found in the Web of Science database to illustrate the current state of research. A selective literature review with special consideration of recognition rates of automated multimodal pain measurement yielded 14 studies, which are the focus of this review. RESULTS: The variance in recognition rates was 52.9-55.0% (pain threshold) and 66.8-85.7%; in nine studies the recognition rate was ≥80% (pain tolerance), while one study reported recognition rates of 79.3% (pain threshold) and 90.9% (pain tolerance). CONCLUSION: Pain is generally recorded multimodally, based on external observation scales. With regard to automated pain recognition and on the basis of the 14 selected studies, there is to date no conclusive evidence that multimodal automated pain recognition is superior to unimodal pain recognition. In the clinical context, multimodal pain recognition could be advantageous, because this approach is more flexible. In the case of one modality not being available, e.g., electrodermal activity in hand burns, the algorithm could use other modalities (video) and thus compensate for missing information.

[Multimodal recognition of pain intensity and pain modality with machine learning]. / Multimodale Erkennung von Schmerzintensität und -modalität mit maschinellen Lernverfahren.

BACKGROUND: The objective recording of subjectively experienced pain is a problem that has not been sufficiently solved to date. In recent years, data sets have been created to train artificial intelligence algorithms to recognize patterns of pain intensity. The multimodal recognition of pain with machine learning could provide a way to reduce an over- or undersupply of analgesics, explicitly in patients with limited communication skills. OBJECTIVES: This study investigated the methodology of automated multimodal recognition of pain intensity and modality using machine-learning techniques of artificial intelligence. Multimodal recognition rates of experimentally induced phasic electrical and heat pain stimuli were compared with uni- and bimodal recognition rates. MATERIAL AND METHODS: On the basis of the X­ITE Pain Database, healthy subjects were stimulated with phasic electro-induced pain and heat pain, and their corresponding pain responses were recorded with multimodal sensors (acoustic, video-based, physiological). After complex signal processing, machine-learning methods were used to calculate recognition rates with respect to pain intensity (baseline vs. pain threshold, pain tolerance, mean value of pain threshold and tolerance) and pain modality (electrical vs. heat). Finally, a statistical comparison of uni- vs. multimodal and bi- vs. multimodal detection rates was performed. RESULTS: With few exceptions, multimodal recognition of pain intensity rates was statistically superior to unimodal recognition rates, regardless of the pain modality. Multimodal pain recognition distinguished significantly better between heat and electro-induced pain. Further, multimodal recognition rates were predominantly superior to bimodal recognition rates. CONCLUSION: Priority should be given to the multimodal approach to the recognition of pain intensity and modality compared with unimodality. Further clinical studies should clarify whether multimodal automated recognition of pain intensity and modality is in fact superior to bimodal recognition.