Prediction of postoperative opioid analgesia using clinical‐experimental parameters and electroencephalography

Gram, Mikkel; Erlenwein, Joachim; Petzke, Frank; Falla, Deborah; Przemeck, Michael; Emons, M. I.; Reuster, M; Olesen, Søren Schou; Drewes, Asbjørn Mohr

doi:10.1002/ejp.921

Cited by 43 publications

(42 citation statements)

References 59 publications

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“…In [70], the overall predictive accuracy of the presented models for prediction of optimal cancer drug therapies was 80%. In the analgesia field, the development of models to predict the postoperative pain treatment reached an accuracy of 65% [71]. According to previous research, a clinically acceptable accuracy level is reached when applying our proposal.…”

Section: Discussionmentioning

confidence: 57%

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision‐Making Process in Medicine

et al. 2018

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One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

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Section: Discussionmentioning

confidence: 57%

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision‐Making Process in Medicine

et al. 2018

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“…The electrical biopotentials generated by the human body are signals commonly used in medical diagnosis. The main biopotentials are electrocardiagram (ECG) [82,85], electromyogram (EMG) [11,35,43,215] and electroencephalogram (EEG) [46,50,[216][217][218][219]. Biopotential sensors are usually composed of the following elements: electrodes, used for the transduction of the ionic signals inside the body to electrical signals; an analog conditioning stage for the amplification of the electrical signal, of very low intensity, at measurable levels avoiding electromagnetic interference (EMI); and an analog-digital conversion stage.…”

Section: Biopotentials For Pain Assessmentmentioning

confidence: 99%

“…It should be noted that despite the lack of an objective reference for the evaluation of chronic pain, many studies have tried to induce pain levels in a controlled manner (by heat [23,[41][42][43][44][45][46][47][48][49][50][51][52][53], cold [45,48,52], pressure [44] or electrical stimulation [54]) as a first approximation to a method that allows the evaluation of chronic pain [1]. In contrast to the studies based on research with phasic pain stimuli (short duration), the number of studies based on tonic pain stimuli is growing as an approach to chronic pain [23,[41][42][43][44][45][46][47][48][49][50][51][52][52][53][54][55]. This validation approach represents an objective way for assessing chronic pain, leaving aside all the subjectivity that may be related to the perception of pain by the chronic patient [23,[41][42][43][44][45][46]…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the studies based on research with phasic pain stimuli (short duration), the number of studies based on tonic pain stimuli is growing as an approach to chronic pain [23,[41][42][43][44][45][46][47][48][49][50][51][52][52][53][54][55]. This validation approach represents an objective way for assessing chronic pain, leaving aside all the subjectivity that may be related to the perception of pain by the chronic patient [23,[41][42][43][44][45][46][47][48][49][50][51][52][52][53][54][55]. In addition, it becomes the most appropriate option in the case of patients with absence of communication skills, whether verbal, written or tactile (infants, unconscious patients, critically sick, patients with intellectual impairment, advanced dementia, etc.…”

Section: Introductionmentioning

confidence: 99%

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Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Naranjo-Hernández

Reina‐Tosina

Roa

2020

Sensors

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Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.

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“…60 For the adults, machine-learning techniques have been used to predict nonresponders to drug treatment in different settings, including oncology, immunology, and postoperative pain. [60][61][62] Depending on the similarity of disease between adults and children, and the explicability and the biological plausibility of the machine learning model, models developed in adults might be also applicable in the pediatric setting after validation. In other cases, the pediatric pathophysiology might be too different or the disease might be absent in adults.…”

Section: Predicting Treatment Respondersmentioning

confidence: 99%

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Goulooze

Zwep

Vogt

et al. 2020

Clin Pharma and Therapeutics

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Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real‐world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo‐responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.

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Prediction of postoperative opioid analgesia using clinical‐experimental parameters and electroencephalography

Abstract: The current clinical study demonstrates the viability of EEG as a biomarker and with results consistent with previous experimental results. The combined method of machine learning and electroencephalography offers promising results for future developments of personalized pain treatment.

Cited by 43 publications

References 59 publications

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision‐Making Process in Medicine

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision‐Making Process in Medicine

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

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