Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

Syed, Shorabuddin; Syed, Mahanazuddin; Prior, Fred; Zozus, Meredith N.; Syeda, Hafsa Bareen; Greer, Melody L.; Bhattacharyya, Sudeepa; Garg, Shashank

doi:10.3233/shti210145

Cited by 4 publications

(5 citation statements)

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“…Recently, several groups have modeled response to a sedative in adult patients undergoing a procedure using nonlinear response surface models and logistic regression models with good success 13,14 . Others have used machine learning to model response to a procedural sedative based on patient demographics, comorbidities, and prescribed medications, also with sufficient accuracy to enable clinical use 15 . Still others have examined serum levels of morphine 16 and midazolam 17 and their metabolites, correlating them with genotyping in a cohort of patients with respiratory failure, for dose optimization.…”

Section: Discussionmentioning

confidence: 99%

“…13,14 Others have used machine learning to model response to a procedural sedative based on patient demographics, comorbidities, and prescribed medications, also with sufficient accuracy to enable clinical use. 15 Still others have examined serum levels of morphine 16 and midazolam 17 and their metabolites, correlating them with genotyping in a cohort of patients with respiratory failure, for dose optimization. However, sedation of patients in the critical care environment is challenging and complex due to prolonged sedation requirements, recurring exposures to sedatives (leading to tolerance and withdrawal), end organ dysfunction (leading to altered drug metabolism), hemodynamic instability, and the presence of delirium.…”

Section: Discussionmentioning

confidence: 99%

“…By both chi-squared and linear-by-linear association.b Score categories of respiratory support score (RSS) are room air (0), nasal cannula or aerosol mask (1-5), high flow nasal cannula or CPAP(6)(7)(8)(9)(10)(11)(12)(13)(14)(15), BiPAP(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), pressure support-only ventilation (26-35), conventional mechanical ventilation (36-70), and high-frequency oscillatory or jet ventilation (71-80). c Acute Withdrawal Syndrome is defined as WAT-1 ≥ 3 within ±3 h. Note: Missing data: Respiratory support score, n = 482.…”

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confidence: 99%

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Predicting favorable response to intravenous morphine in pediatric critically ill cardiac patients

et al. 2023

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Introduction: Analgesia and sedation are integral to the care of critically ill children.However, the choice and dose of the analgesic or sedative drug is often empiric, and models predicting favorable responses are lacking. We aimed to compute models to predict a patient's response to intravenous morphine. Methods:We retrospectively analyzed data from consecutive patients admitted to the Cardiac Intensive Care Unit (January 2011-January 2020) who received at least one intravenous bolus of morphine. The primary outcome was a decrease in the State Behavioral Scale (SBS) ≥1 point; the secondary outcome was a decrease in the heart rate Z-score (zHR) at 30 min. Effective doses were modeled using logistic regression, Lasso regression, and random forest modeling. Results: A total of 117,495 administrations of intravenous morphine among 8140 patients (median age 0.6 years [interquartile range [IQR] 0.19, 3.3]) were included.The median morphine dose was 0.051 mg/kg (IQR 0.048, 0.099) and the median 30day cumulative dose was 2.2 mg/kg (IQR 0.4, 15.3). SBS decreased following 30% of doses, did not change following 45%, and increased following 25%. The zHR significantly decreased after morphine administration (median delta-zHR −0.34 [IQR−1.03, 0.00], p < 0.001). The following factors were associated with favorable response to morphine: A concomitant infusion of propofol, higher prior 30-day cumulative dose, being invasively ventilated and/or on vasopressors. Higher morphine dose, higher zHR pre-morphine, an additional analgosedation bolus ±30 min around the index bolus, a concomitant ketamine or dexmedetomidine infusion, and showing signs of withdrawal syndrome were associated with unfavorable response. Logistic regression (area under the receiver operating characteristic [ROC] curve [AUC] 0.900) and machine learning models (AUC 0.906) performed comparably, with a sensitivity of 95%, specificity of 71%, and negative predictive value of 97%.Conclusions: Statistical models identify 95% of effective intravenous morphine doses in pediatric critically ill cardiac patients, while incorrectly suggesting an effective dose in 29% of cases. This work represents an important step toward computer-aided, personalized clinical decision support tool for sedation and analgesia in ICU patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Predicting favorable response to intravenous morphine in pediatric critically ill cardiac patients

et al. 2023

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“…The management of patients undergoing gastroenterological procedures often require sedations to improve patient comfort and facilitate endoscopic performance. In 2021 Syed et al ( 22 ) created a ML model (XGBoost) that predicts the grade of sedation required to successfully conduct a colonoscopy with an AUC of 0.762 after being tested on tested on 10,025 colonoscopies.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and anesthesia: a narrative review

Carnà¹,

Russo²,

Vincenzo³

et al. 2022

Ann Transl Med

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Background and Objective The aim of this narrative review is to analyze whether or not artificial intelligence (AI) and its subsets are implemented in current clinical anesthetic practice, and to describe the current state of the research in the field. AI is a general term which refers to all the techniques that enable computers to mimic human intelligence. AI is based on algorithms that gives machines the ability to reason and perform functions such as problem-solving, object and word recognition, inference of world states, and decision-making. It includes machine learning (ML) and deep learning (DL). Methods We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases. The research string comprised various combinations of “artificial intelligence”, “machine learning”, “anesthesia”, “anesthesiology”. The databases were searched independently by two authors. A third reviewer would mediate any disagreement the results of the two screeners. Key Content and Findings The application of AI has shown excellent results in both anesthesia and in operating room (OR) management. In each phase of the perioperative process, pre-, intra- and postoperative ones, it is able to perform different and specific tasks, using various techniques. Conclusions Thanks to the use of these new technologies, even anesthesia, as it is happening for other disciplines, is going through a real revolution, called Anesthesia 4.0. However, AI is not free from limitations and open issues. Unfortunately, the models created, provided they have excellent performance, have not yet entered daily practice. Clinical impact analyzes and external validations are needed before this happens. Therefore, qualitative research will be needed to better understand the ethical, cultural, and societal implications of integrating AI into clinical workflows.

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“…A machine learning approach, based on patients' demographics, comorbidities, and prescribed medications, was developed to predict if a colonoscopy could be successfully completed with moderate sedation. The area under the curve (AUC) for this model was 0.85, establishing it as a potential invaluable tool for providing individualized anesthesia tailored to each patient's needs [21].…”

Section: Intraoperative Artificial Intelligence Considerationsmentioning

confidence: 96%

Artificial intelligence and nonoperating room anesthesia

Pardo,

Le Cam,

Verdonk

2024

Current Opinion in Anaesthesiology

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Purpose of review The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. Recent findings AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. Summary The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

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Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

Cited by 4 publications

References 13 publications

Predicting favorable response to intravenous morphine in pediatric critically ill cardiac patients

Predicting favorable response to intravenous morphine in pediatric critically ill cardiac patients

Artificial intelligence and anesthesia: a narrative review

Artificial intelligence and nonoperating room anesthesia

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