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.

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.

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.

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.

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.

Intraoperative monitoring of analgesia using nociceptive reflexes correlates with delayed extubation and immediate postoperative pain: A prospective observational study.

BACKGROUND: Immediate postoperative pain could be prevented by the administration of long-lasting analgesics before the end of the anaesthesia. However, to prevent over or underdosing of analgesics under anaesthesia, tools are required to estimate the analgesia-nociception balance. OBJECTIVE: We investigated whether the pupillary dilation reflex (PDR) and the nociceptive flexion reflex (NFR) at the end of general anaesthesia correlate with immediate postoperative pain, as a sign of analgesic underdosing, and with delayed tracheal extubation as a sign of analgesic overdosing. DESIGN: Prospective observational study. SETTING: Klinikum im Friedrichshain, Berlin, Germany, from May 2013 to April 2015. PATIENTS: A total of 110 patients scheduled for primary hip arthroplasty under general anaesthesia. OBSERVATIONS: Psychometric and clinical data were obtained preoperatively. The PDR and the NFR were assessed preoperatively and at the end of anaesthesia. Shortly after extubation of the trachea, patients rated their pain intensity. ENDPOINTS: The primary endpoint was the immediate postoperative numeric rating scale pain intensity (0 to 10) and the secondary endpoint was the length of the time interval between reflex measurements and tracheal extubation. RESULTS: PDR correlated significantly with the immediate postoperative pain intensity (Spearman's ρ = -0.28, P < 0.01). PDR and NFR correlated significantly with the time interval until extubation (ρ = 0.33/0.26, both P < 0.01). Multivariable linear regression models were able to predict 38% of the variability of immediate postoperative pain intensity and 44% of the variability of the time interval until extubation. The intraoperative nociceptive reflex thresholds were the strongest factors in both multivariable models, only exceeded by the preoperative reflex measurements in the model for postoperative pain. CONCLUSION: The investigated nociceptive reflexes reflected the analgesia-nociception balance under general anaesthesia. The preoperative reflexes provide additional information about individual subjective pain sensitivity. TRIAL REGISTRATION: Deutsches Register Klinischer Studien, DRKS (registration number DRKS00000665).

Optimizing Nociceptive Flexion Reflex (NFR) Scoring Criteria by Adjusting for Noise and Reflex Properties and Sampling Rate.

OBJECTIVE: To reanalyze scoring criteria for automatic detection of nociceptive flexion reflexes (NFRs) in electromyography (EMG) recordings and to improve detection accuracy by accounting for multiple characteristics of the recordings, such as baseline noise level or sampling rate. METHODS: Single scoring criteria for the NFR were reanalyzed and validated against an independent data set. To account for influences on the single scoring criteria, such as the baseline noise, multivariate classification models were derived. RESULTS: Reanalysis of single scoring criteria yielded significantly lower threshold values than previously reported. The threshold value of the best-performing single scoring criterion, the NFR Interval Peak z score, was found to be strongly dependent on the level of baseline noise and the EMG sampling rate. Multivariate classification models could reduce the number of incorrectly classified recordings in an independent data set by 25% to 37% compared with the best-performing single scoring criterion. DISCUSSION: The automatic detection of reflex responses in electromyograms can be significantly improved by including multiple reflex, baseline, and EMG characteristics into a classification model. These findings should help to improve the accuracy of currently used standard measurement algorithms and algorithms engineered toward specific properties, such as short measurements or less induced pain for the patients.

Reliability of subjective pain ratings and nociceptive flexion reflex responses as measures of conditioned pain modulation.

BACKGROUND: The endogenous modulation of pain can be assessed through conditioned pain modulation (CPM), which can be quantified using subjective pain ratings or nociceptive flexion reflexes. However, to date, the test-retest reliability has only been investigated for subjective pain ratings. OBJECTIVE: To compare the test-retest reliability of CPM-induced changes, measured using subjective pain ratings and nociceptive flexion reflexes, to provide a reliable scoring parameter for future studies. METHOD: A total of 40 healthy volunteers each received painful electrical stimuli to the sural nerve to elicit nociceptive flexion reflexes. Reflex sizes and subjective pain ratings were recorded before and during the immersion of the contralateral hand in hot water to induce CPM as well as innocuous water as control. Measurements were repeated in a retest 28 days later. RESULTS: Intraclass correlation coefficients showed good test-retest reliabilities of CPM during the hot water stimulus for both scoring parameters. Subjective pain ratings also correlated between test and retest during the control stimulus. CONCLUSIONS: Subjective pain ratings and nociceptive flexion reflexes show comparable test-retest reliabilities, but they reflect different components of CPM. While subjective pain ratings appear to incorporate cognitive influences to a larger degree, reflex responses appear to reflect spinal nociception more purely.