Vital Recorder—a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices

Lee, Hyung Chul; Jung, Chul-Woo

doi:10.1038/s41598-018-20062-4

Cited by 166 publications

(123 citation statements)

References 20 publications

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“…VitalDB database is a biosignal database consisting of data from 6388 patients who underwent surgery in 10 operating rooms at Seoul National University Hospital from 2016 to 2019 [35]. It includes biosignal waveform data, vital signs during surgery, various clinical parameters related to surgery, and laboratory test results.…”

Section: Vitaldb Databasementioning

confidence: 99%

Discovering hidden information in biosignals from patients using artificial intelligence

Yoon

Jang

Choi

et al. 2020

Korean J Anesthesiol

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Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

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Section: Vitaldb Databasementioning

confidence: 99%

Discovering hidden information in biosignals from patients using artificial intelligence

Yoon

Jang

Choi

et al. 2020

Korean J Anesthesiol

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“…If PIH could be accurately predicted, anesthesiologists would thereby be able to determine appropriate management proactively, thus preventing the negative outcomes associated with hypotension. These days, modern anesthesia data have been expanded to include highresolution time-synchronized physiological and pharmacological data from multiple anesthesia devices [4]. This adds a large amount of anesthesia-related data to traditional electronic health records (EHRs); however, in a busy operating room environment it is not easy for an anesthesiologist to analyze these data in real time to predict PIH occurrence.…”

Section: Introductionmentioning

confidence: 99%

Development of a prediction model for hypotension after induction of anesthesia using machine learning

et al. 2020

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Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between tracheal intubation and incision, using data for the period from between the start of anesthesia induction and immediately before tracheal intubation obtained from an anesthesia monitor, a drug administration infusion pump, an anesthesia machine, and from patients' demographics, together with preexisting disease information from electronic health records. Among 222 patients, 126 developed postinduction hypotension. The random-forest model showed the best performance, with an area under the receiver operating characteristic curve of 0.842 (95% confidence interval [CI]: 0.736-0.948). This was higher than that for the Naïve Bayes (0.778; 95% CI: 0.65-0.898), logistic regression (0.756; 95% CI: 0.630-0.881), and artificial-neural-network (0.760; 95% CI: 0.640-0.880) models. The most important features affecting the accuracy of machine-learning prediction were a patient's lowest systolic blood pressure, lowest mean blood pressure, and mean systolic blood pressure before tracheal intubation. We found that machine-learning models using data obtained from various anesthesia machines between the start of anesthesia induction and immediately before tracheal intubation can predict hypotension occurring during the period between tracheal intubation and incision.

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“…A BIS Quatro (Medtronic, Minneapolis, MN, USA) four-electrode sensor was placed on the patient's forehead. The vital signs, ventilator values, patient monitor values, and BIS and raw EEG data were recorded in the automatic recording program, Vital Recorder 16 . Our institute has recorded certain events during surgery such as intravenous agent administration, and birth on Vital Recorder since 2017.…”

Section: Comparison Of Electroencephalogram Between Propofol-and Thiomentioning

confidence: 99%

Comparison of electroencephalogram between propofol- and thiopental-induced anesthesia for awareness risk in pregnant women

Park

Kim

et al. 2020

Sci Rep

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Some studies have suggested that EEG oscillations are related to altered states of consciousness produced by anesthetics 11-15. However, there is a lack of detailed characterization of oscillatory EEG activity regarding thiopental and propofol administration in pregnant women. Therefore, characterizing EEG spectral dynamics should help to establish the risk of awareness during general anesthesia. We aimed to investigate whether there are any differences in frontal brain oscillations between propofol-and thiopental-induced general anesthesia for cesarean section. Further, BIS and entropy analyses were also conducted to evaluate awareness risk in each group. Methods patient population. This study was approved by the Asan Medical Centre institutional review board (IRB number 2019-0737). The need for informed consent was waived, considering the retrospective nature of this analysis. The study conformed to the tenets of the Declaration of Helsinki. A retrospective analysis of EEG data recorded during elective cesarean section was conducted between September 2017 and May 2019. Our institute has traditionally used only thiopental (Pentothal sodium, JW Pharmaceutical, Seoul, Korea) for cesarean section, but there was a temporary supply interruption from November 2017 until March 2018. Propofol was then used as an alternative induction agent for cesarean section. After the re-supply of thiopental, agent selection was decided by the attending anesthesiologist. We identified all parturient women who received general anesthesia. Through chart review, parturients were divided into two groups according to the anesthetic agent: the thiopental and propofol groups. Inclusion criteria were between 20 and 44 years old, American Society of Anesthesiologist grade 1 or 2, and elective operation. Patients with the following conditions were excluded from the study: (1) cases where regional anesthesia was converted to general anesthesia; (2) exposure to sevoflurane before the induction of general anesthesia; (3) missing Vital Recorder data; (4) difficult airway management cases. Anesthetic management. Our institutional standardized anesthesia protocols were used for cesarean section. All parturient women received at least 3 min of pre-oxygenation using 100% oxygen, followed by RSI with cricoid pressure. Thiopental 5 mg/kg or propofol 2 mg/kg was administered, followed by succinylcholine 1.5 mg/kg to facilitate intubation. After disappearance of fasciculation and of the electromyography (EMG) activity bar on the BIS monitor, all patients were intubated with a 6.5-cuffed tracheal tube using a video-scope. After confirming successful intubation, rocuronium 0.5 mg/kg was immediately administered to achieve further muscle relaxation. Anesthesia was maintained with 50% nitrous oxide (N 2 O) in oxygen (6 L/min) and sevoflurane. The initially inspired concentration of sevoflurane was set at 2.0 vol% on the vaporizer until the end-tidal sevoflurane (Et-Sevo) reached 1.3 vol%. A skin incision was made after the anesthesiologist confirmed intubatio...

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Vital Recorder—a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices

Cited by 166 publications

References 20 publications

Discovering hidden information in biosignals from patients using artificial intelligence

Discovering hidden information in biosignals from patients using artificial intelligence

Development of a prediction model for hypotension after induction of anesthesia using machine learning

Comparison of electroencephalogram between propofol- and thiopental-induced anesthesia for awareness risk in pregnant women

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