Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil

Artificial Intelligence and Machine Learning in Anesthesiology

Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated.The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them-perhaps bringing anesthesiology into an era of machine-assisted discovery.

Bispectral Index in predicting in-hospital mortality in patients with ischemic stroke: A methodological study

Background:

Ischemic stroke is a leading cause of death and functional disability worldwide. Several clinical scores or stroke scales, biological test or markers, clinical signs, and radiological imaging have been performed to predict both worse neurologic outcome and mortality for ischemic stroke.

Objectives:

The aim of our study was to investigate the association between early Bispectral Index scores and in-hospital mortality in patients with ischemic stroke.

Methods:

This is a comparative prospective methodological study, in which we evaluated the predictive accuracies of Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index for in-hospital mortality of patients with ischemic stroke. Receiver operating characteristic analysis was used for comparing the accuracy of the scoring systems, areas under receiver operating characteristic curves were calculated, and Youden J index was used for estimating associated cut-off values.

Results:

Among the 80 ischemic stroke patients, in-hospital mortality rate was 38.8% (n = 31). The areas under receiver operating characteristic curves were 0.984, 0.960, and 0.863 for Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index, respectively. The difference between areas under receiver operating characteristic curves for Bispectral Index and Glasgow Coma Scale was statistically similar. Besides, the difference between areas under receiver operating characteristic curves for Bispectral Index and Charlson Comorbidity Index, and the difference between areas under receiver operating characteristic curves for Glasgow Coma Scale and Charlson Comorbidity Index were statistically significant. The associated cut-off values were ⩽74, ⩽12, and >4 for Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index, respectively. For these cut-off points, sensitivity and specificity of Bispectral Index were 93.6% and 95.9%, sensitivity and specificity of Glasgow Coma Scale were 100.0% and 83.7%, and sensitivity and specificity of Charlson Comorbidity Index were 83.9% and 69.4%, respectively. However, accuracy of Bispectral Index was 95.0%, accuracy of Glasgow Coma Scale was 90.0%, and accuracy of Charlson Comorbidity Index was 75.0.

Conclusion:

Knowledge of the risk factors for mortality in patients with ischemic stroke can help to identify which patients have a higher risk of fatal outcome. The Bispectral Index score improved discrimination and classified patients with higher mortality better than both Glasgow Coma Scale and Charlson Comorbidity Index.

Deep Learning in Physiological Signal Data: A Survey

Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

Intraoperative Arterial Pressure Variability and Postoperative Acute Kidney Injury

BACKGROUND AND OBJECTIVES

High BP variability may cause AKI because of inappropriate kidney perfusion. This study aimed to investigate the association between intraoperative BP variability and postoperative AKI in patients who underwent noncardiac surgery.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We performed a cohort study of adults undergoing noncardiac surgery in hospitals in South Korea. We studied three cohorts using the following recording windows for intraoperative BP: discovery cohort, 1-minute intervals; first validation cohort, 5-minute intervals; and second validation cohort, 2-second intervals. We calculated four variability parameters (SD, coefficient of variation, variation independent of mean, and average real variability) based on the measured mean arterial pressure values. The primary outcomes were postoperative AKI (defined by the Kidney Disease Improving Global Outcomes serum creatinine cutoffs) and critical AKI (consisting of stage 2 or higher AKI and post-AKI death or dialysis within 90 days).

RESULTS

In the three cohorts, 45,520, 29,704, and 7435 patients were analyzed, each with 2230 (443 critical), 1552 (444 critical), and 300 (91 critical) postoperative AKI events, respectively. In the discovery cohort, all variability parameters were significantly associated with risk of AKI, even after adjusting for intraoperative hypotension. For example, average real variability was associated with higher risks of postoperative AKI (adjusted odds ratio, 1.13 per 1 SD increment; 95% CI, 1.07 to 1.19) and critical AKI (adjusted odds ratio, 1.13 per 1 SD increment; 95% CI, 1.02 to 1.26). Associations were evident predominantly among patients who also experienced intraoperative hypotension. In the validation analysis with 5-minute-interval BP records, all four variability parameters were associated with the risk of postoperative AKI or critical AKI. In the validation cohort with 2-second-interval BP records, average real variability was the only significant variability parameter.

CONCLUSIONS

Higher intraoperative BP variability is associated with higher risks of postoperative AKI after noncardiac surgery, independent of hypotension and other clinical characteristics.

Intraoperative hypotension and its prediction

Intraoperative hypotension (IOH) very commonly accompanies general anaesthesia in patients undergoing major surgical procedures. The development of IOH is unwanted, since it is associated with adverse outcomes such as acute kidney injury and myocardial injury, stroke and mortality. Although the definition of IOH is variable, harm starts to occur below a mean arterial pressure (MAP) threshold of 65 mmHg. The odds of adverse outcome increase for increasing duration and/or magnitude of IOH below this threshold, and even short periods of IOH seem to be associated with adverse outcomes. Therefore, reducing the hypotensive burden by predicting and preventing IOH through proactive appropriate treatment may potentially improve patient outcome. In this review article, we summarise the current state of the prediction of IOH by the use of so-called machine-learning algorithms. Machine-learning algorithms that use high-fidelity data from the arterial pressure waveform, may be used to reveal 'traits' that are unseen by the human eye and are associated with the later development of IOH. These algorithms can use large datasets for 'training', and can subsequently be used by clinicians for haemodynamic monitoring and guiding therapy. A first clinically available application, the hypotension prediction index (HPI), is aimed to predict an impending hypotensive event, and additionally, to guide appropriate treatment by calculated secondary variables to asses preload (dynamic preload variables), contractility (dP/dtmax), and afterload (dynamic arterial elastance, Eadyn). In this narrative review, we summarise the current state of the prediction of hypotension using such novel, automated algorithms and we will highlight HPI and the secondary variables provided to identify the probable origin of the (impending) hypotensive event.

The Future Directions of Research in Cardiac Anesthesiology

This article provides an overview of knowledge gaps that need to be addressed in cardiac anesthesia, including mitigating the inflammatory effects of cardiopulmonary bypass, defining myocardial infarction after cardiac surgery, improving perioperative neurologic outcomes, and the optimal management of patients undergoing valve replacement. In addition, emerging approaches to research conduct are discussed, including the use of new analytical techniques like machine learning, pragmatic trials, and adaptive designs.

A deep learning approach for real-time detection of sleep spindles

OBJECTIVE

Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications.

APPROACH

Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications.

MAIN RESULTS

Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~two-three spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species.

SIGNIFICANCE

SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments.

What we can learn from Big Data about factors influencing perioperative outcome

PURPOSE OF REVIEW

This narrative review will discuss what value Big Data has to offer anesthesiology and aims to highlight recently published articles of large databases exploring factors influencing perioperative outcome. Additionally, the future perspectives of Big Data and its major pitfalls will be discussed.

RECENT FINDINGS

The potential of Big Data has given an incentive to create nationwide and anesthesia-initiated registries like the MPOG and NACOR. These large databases have contributed in elucidating some of the rare perioperative complications, such as declined cognition after exposure to general anesthesia and epidural hematomas in parturients. Additionally, they are useful in finding patterns such as similar outcome in subtypes of beta-blockers and lower incidence of pneumonia in preoperative influenza vaccinations in the elderly.

SUMMARY

Big Data is becoming increasingly popular with the collaborative collection of registries offering anesthesia a way to explore rare perioperative complications and outcome to encourage further hypotheses testing. Although Big Data has its flaws in security, lack of expertise and methodological concerns, the future potential of analytics combined with genomics, machine learning and real-time decision support looks promising.

Immediate extubation after heart transplantation in a child by remifentanil-based ultra-fast anesthesia: A case report

RATIONALE

Ventilator-associated complications comprise important fatal aetiologies during heart transplantation. Ultra-fast anesthesia might provide the most effective measure to prevent this type of complication. Immediate extubation after heart transplantation (IEAHT) has recently been reported in adult patients. However, IEAHT in children is much more challenging due to limitations in anesthesia protocols. Recently, we managed to perform an ultra-fast anesthesia protocol combined with IEAHT during a heart transplant operation in a child, who had an excellent postoperative outcome.

PATIENT CONCERNS

A 13-year-old girl had been diagnosed with dilated cardiomyopathy 5 years before this case, due to intractable dyspnoea and cough. She received multiple medical treatments after diagnosis, with minimal effects. Physical examination findings included a bulge in her left chest and pitting edema over both legs. Moist rales could be heard in the lung. Echocardiography revealed very large heart chambers, with an ejection fraction of 17%.

DIAGNOSIS

The patient was diagnosed with dilated cardiomyopathy and scheduled to undergo an emergent operation for heart transplantation.

INTERVENTIONS

The patient underwent an ultra-fast anesthesia protocol and ultra-fast reversal during heart transplantation. General anesthesia was induced with etomidate, fentanyl, and vecuronium; it was then maintained with remifentanil-based total intravenous anesthesia.

OUTCOMES

Immediately after the end of the operation, the patient was brought to consciousness with stable breathing and haemodynamics. The patient was successfully extubated on the operating table and transferred to the intensive care unit with spontaneous breathing, without postoperative mechanical ventilation. The recovery period was uneventful and the patient was discharged 1 month later without complications.

LESSONS

Our experience, in this case, revealed that IEAHT in children is achievable if the ultra-fast protocol is performed properly and carefully, in order to prevent ventilator-associated complications.

Patient-Centered Mobile Health Data Management Solution for the German Health Care System (The DataBox Project)

This article describes the DataBox project which offers a perspective of a new health data management solution in Germany. DataBox was initially conceptualized as a repository of individual lung cancer patient data (structured and unstructured). The patient is the owner of the data and is able to share his or her data with different stakeholders. Data is transferred, displayed, and stored online, but not archived. In the long run, the project aims at replacing the conventional method of paper- and storage-device-based handling of data for all patients in Germany, leading to better organization and availability of data which reduces duplicate diagnostic procedures, treatment errors, and enables the training as well as usage of artificial intelligence algorithms on large datasets.