An Evaluation of a Novel Software Tool for Detecting Changes in Physiological Monitoring

Ansermino, J. Mark; Daniels, Jeremy; Hewgill, Randy T.; Lim, Joanne; Brouse, Chris J.; Dumont, Guy A.; Bowering, John

doi:10.1213/ane.0b013e318193ff87

Cited by 22 publications

(11 citation statements)

References 8 publications

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“…Operationally, this is accomplished by utilizing a significant abnormality in any one of the five conventional vital signs as a trigger (Table 1). Once a significant vital sign abnormality is recognized [facilitated by electronic alerts in the monitoring devices or electronic medical record (EMR)], a full 10 SOV assessment is performed at the bedside [14][15][16][17]. Nine of the 10 SOV can be assessed within several minutes; the 10th [base deficit or central venous oxygen saturation (ScVO 2 )] requires an arterial or central blood gas, a 15 min turn around time at many hospitals.…”

Section: Early Recognition Of Sepsismentioning

confidence: 99%

A systems approach to the early recognition and rapid administration of best practice therapy in sepsis and septic shock

Funk

Sebat

Kumar

2009

Current Opinion in Critical Care

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The institution of a rapid response system for the detection and treatment of septic shock requires a multidisciplinary approach. The infrastructure to create such a system must be facilitated by administrators and implemented by front-line healthcare providers. Continuous assessment of the outcome benefit of such a system by a quality assurance team is the final part of a truly integrated approach to sepsis treatment.

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Section: Early Recognition Of Sepsismentioning

confidence: 99%

A systems approach to the early recognition and rapid administration of best practice therapy in sepsis and septic shock

Funk

Sebat

Kumar

2009

Current Opinion in Critical Care

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“…They were engineered to detect events in real-time data with performance optimized, using receiver operating curves, on both simulated and real clinical data (offline). 4 These algorithms compare favourably with other methods of trend detection, such as Trigg's tracking signal. 10…”

Section: Change-point Detection Algorithmsmentioning

confidence: 99%

“…In this study, we tested algorithms for tracking the behaviour of dynamic physiological systems and automatically detecting key events in the processes over time. After extensive offline testing 4 and refinement, we now report the results of evaluation in real-time during anaesthesia.…”

mentioning

confidence: 99%

Clinical evaluation of algorithms for context-sensitive physiological monitoring in children

Dosani

Lim

Brouse

et al. 2009

British Journal of Anaesthesia

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The algorithms were able to detect a range of clinically significant physiological changes during paediatric anaesthesia, and were considered useful by participating anaesthetists. These findings indicate that automated detection of context-sensitive changes is possible and could be used by early warning systems during physiological monitoring. Further investigations are required to assess how this information can best be communicated to the anaesthetist.

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“…Algorithms designed to detect physiologic decline such as hypoxemia in pediatric patients can help optimize physician response time and ultimately minimize adverse events. Machine learning algorithms such as dynamic linear growth models have been used to detect changes in heart and respiratory rate, oxygen saturation, end-tidal carbon dioxide, exhaled minute ventilation and noninvasive arterial pressure in children [12,13]. A predictive model for intraoperative hypoxemia created through a deep learning model utilized ventilator parameters and pulse oximetry to predict hypoxemia in children 1 min prior to oxygen saturation (SpO 2 ) dropping <95% [14 ▪▪ ].…”

Section: Risk Prediction In Pediatric Perioperative Carementioning

confidence: 99%

Advances in pediatric perioperative care using artificial intelligence

Dundaru-Bandi,

Antel,

Ingelmo

2024

Current Opinion in Anaesthesiology

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Purpose of this review This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers. Recent findings The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools. Summary The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.

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An Evaluation of a Novel Software Tool for Detecting Changes in Physiological Monitoring

Abstract: The algorithms perform favorably compared with a visual inspection of the complete trend. Further research is needed to identify when and how to draw the clinician's attention to these changes.

Cited by 22 publications

References 8 publications

A systems approach to the early recognition and rapid administration of best practice therapy in sepsis and septic shock

A systems approach to the early recognition and rapid administration of best practice therapy in sepsis and septic shock

Clinical evaluation of algorithms for context-sensitive physiological monitoring in children

Advances in pediatric perioperative care using artificial intelligence

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